Patent Publication Number: US-11047213-B2

Title: Flooding analysis tool and method thereof

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims benefit under 35 USC 119 of U.S. Provisional Patent Application No. 62/040,909 with a filing date of Aug. 22, 2014. This application also claims benefit under 35 USC 119 of U.S. Provisional Patent Application No. 62/135,016 with a filing date of Mar. 18, 2015. This application claims priority to and benefits from the foregoing, the disclosures of which are incorporated herein by reference. 
    
    
     This application is one of multiple non-provisional patent applications filed on Aug. 21, 2015 with the title of FLOODING ANALYSIS TOOL AND METHOD THEREOF. All of these non-provisional patent applications are related, and all of their disclosures are incorporated herein by reference. 
     TECHNICAL FIELD 
     The present disclosure relates generally to producing hydrocarbons from a subterranean reservoir using a flood operation, and more specifically, this disclosure relates to analyzing the flood operation. 
     BACKGROUND 
     Different mechanisms are typically utilized to produce hydrocarbons, such as oil and gas, from a subterranean reservoir. Initially, hydrocarbons are driven from the reservoir to the surface by the natural differential pressure between the reservoir and the bottomhole pressure within a wellbore. After the pressure stage, an artificial lift system such as a sucker rod pump and an electrical submersible pump can be utilized to drive hydrocarbons to the surface. After the artificial lift stage, a flood operation can be utilized to drive hydrocarbons to the surface. In a flooding or flood operation, displacing fluid such as water, gas, surfactants, polymers, etc. is injected into the reservoir via one or more injection wells, and the displacing fluid displaces or physically sweeps the hydrocarbons towards one or more producing wells to the surface. 
     Analysis of a flood operation is oftentimes a difficult task. For example, the subterranean reservoir can include various geological features such as faults, naturally occurring fractures, different rock types, etc., and these geological features affect how the injection wells and the production wells are linked in the subterranean reservoir. Indeed, a typical field can have hundreds of wells, and the wells can be linked in all sorts of ways, further complicating analysis of the flood operation. Therefore, the industry is always searching for improvements in analyzing a flood operation. 
     SUMMARY 
     Described herein are various embodiments of computer-implemented methods, computing systems, and program products for analyzing a flood operation on a hydrocarbon reservoir. 
     In one aspect, a computer implemented method of analyzing at least a first flood operation and a second flood operation on a hydrocarbon reservoir having at least one production well and at least one injection well is provided. The method includes, for each of the first flood operation and the second flood operation: receiving production data for the at least one production well and injection data for the at least one injection well for the flood operation and running capacitance resistance modeling using the received production data and the received injection data for the flood operation to generate a response time and an interwell connectivity per injection well and production well pair for the flood operation. The method further includes using the generated response times, the generated interwell connectivities, the received production data, the received injection data, or any combination thereof to generate a proxy of pore volume swept per injection well and production well pair for the flood operation and aggregating the generated proxies of pore volume swept per injection well and production well pair for the flood operation to generate an estimate of pore volume swept at a well level, at a reservoir level, or both for the flood operation. The method further includes comparing the generated estimate of pore volume swept for the first flood operation and the generated estimate of pore volume swept for the second flood operation to determine a change in sweep efficiency at the well level, at the reservoir level, or both. 
     In yet another aspect, a computing system for analyzing at least a first flood operation and a second flood operation on a hydrocarbon reservoir having at least one production well and at least one injection well is provided. The system includes at least one processor and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to perform a method. The method includes, for each of the first flood operation and the second flood operation: receiving production data for the at least one production well and injection data for the at least one injection well for the flood operation and running capacitance resistance modeling using the received production data and the received injection data for the flood operation to generate a response time and an interwell connectivity per injection well and production well pair for the flood operation. The method further includes using the generated response times, the generated interwell connectivities, the received production data, the received injection data, or any combination thereof to generate a proxy of pore volume swept per injection well and production well pair for the flood operation and aggregating the generated proxies of pore volume swept per injection well and production well pair for the flood operation to generate an estimate of pore volume swept at a well level, at a reservoir level, or both for the flood operation. The method further includes comparing the generated estimate of pore volume swept for the first flood operation and the generated estimate of pore volume swept for the second flood operation to determine a change in sweep efficiency at the well level, at the reservoir level, 
     In yet another aspect, a computer implemented method of analyzing a polymer flood operation on a hydrocarbon reservoir having at least one injection well and at least one production well is provided. The method includes receiving production data for the at least one production well and injection data for the at least one injection well for the flood operation and running capacitance resistance modeling using the received production data and the received injection data for the polymer flood operation to generate a response time and an interwell connectivity per injection well and production well pair for the polymer flood operation. Running capacitance resistance modeling includes accounting for rheology of the polymer used in the polymer flood operation in the running of the capacitance resistance modeling. 
     The above summary section is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description section. The summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features of the present disclosure will become better understood with regard to the following description, claims and accompanying drawings where: 
         FIG. 1  illustrates an embodiment of a hydrocarbon reservoir with a plurality of injection wells and a plurality of production wells for a flood operation. 
         FIG. 2A  illustrates a computing system useable to analyze a flood operation on a hydrocarbon reservoir. 
         FIG. 2B  illustrates an embodiment of a data management system. 
         FIG. 2C  illustrates an embodiment of an interactivity workflow that may be executed by the computing system of  FIG. 2A . 
         FIGS. 3-5  illustrate one embodiment of a computer implemented method to analyze a flood operation. 
         FIG. 6  illustrates one embodiment of a method for determining the allowed well connections. 
         FIGS. 7-10  illustrate examples consistent with the embodiment of  FIG. 6 . 
         FIG. 11  is another embodiment of a method for determining allowed well connections. 
         FIG. 12  illustrates an example consistent with the embodiment of  FIG. 11 . 
         FIG. 13  illustrates one example of a conformance control workflow. 
         FIG. 14  illustrates an embodiment of a computer implemented method of analyzing a flood operation on a hydrocarbon reservoir having heavy oil. 
         FIGS. 15-17  illustrate examples consistent with the embodiment of  FIG. 14 . 
         FIG. 18  illustrates one embodiment of a computer implemented method of identifying at least one conformance candidate. 
         FIGS. 19A-19B  illustrates one embodiment of a computer implemented method of determining a conformance control treatment for a well of a hydrocarbon reservoir, where the well is in fluidic communication with a plurality of zones of the hydrocarbon reservoir. 
         FIG. 20  illustrates one embodiment of a computer implemented method of analyzing at least a first flood operation and a second flood operation on a hydrocarbon reservoir having at least one production well and at least one injection well. 
         FIGS. 21A-21C, 22A-22B, 23A-23G, 24A-24F  illustrate examples consistent with the embodiment of  FIG. 20 . 
         FIG. 25  illustrates one example of the sharp viscosity variation between near well bore areas to far well bore areas. 
         FIG. 26  illustrates one example of separating each injection well and production well pair of a polymer flood operation into three tanks including a near injection well tank, a near production well tank, and a middle tank between the near injection well tank and the near production well tank. 
         FIG. 27  illustrates one embodiment of a computer implemented method of analyzing a polymer flood operation on a hydrocarbon reservoir having at least one injection well and at least one production well 
         FIGS. 28A, 28B  illustrate one embodiment of a computer implemented method for using producer centered (e.g., Voronoi) polygons to help identify infill drilling locations and an example thereof. 
         FIGS. 29A, 29B, 29C  illustrate one embodiment of a computer implemented method for using polygons to choose between two infill candidates in two different reservoirs and examples thereof. 
         FIGS. 30A, 30B, 30C  illustrate one embodiment of a computer implemented method for using polygons (e.g., streamgrids) for pattern realignment and examples thereof. 
         FIGS. 31A, 31B, 31C  illustrate one embodiment of a computer implemented method for using polygons (e.g., streamgrid) allocation factors to initiate CRM interwell connectivity and examples thereof. 
         FIG. 32A, 32B, 32C  illustrate one embodiment of a computer implemented method for estimating maximal areal sweep by zone and by reservoir with the populated polygons (e.g., streamgrids) and examples thereof. 
         FIG. 33  illustrates one embodiment of a computer implemented method of determining a value of injected fluid for a flood operation on a hydrocarbon reservoir having at least one injection well and at least one production well. 
         FIGS. 34, 35A-35E, 36A-36H, 37A-37I  illustrate one embodiment of a method for analyzing a flood operation for a hydrocarbon reservoir having a plurality of zones and examples thereof. 
     
    
    
     DETAILED DESCRIPTION 
     The various embodiments of computer implemented methods described herein may be used to analyze a flood operation on a subterranean reservoir. Furthermore, the various methods described herein may be used in combination or individually. For example, the methods involving determining allowed well connections may be used in conjunction with determining the value of injected fluid (VOIF). As another example, the methods involving determining allowed well connections may be used in conjunction with CRM zonal. As another example, the methods involving determining allowed well connections may be used in conjunction with analyzing a first flood operation and a second flood operation. 
     Furthermore, there may be a corresponding apparatus (e.g., computing systems) and/or program product for each computer implemented method. Indeed, those of ordinary skill in the art will understand that the invention is not limited to the disclosed embodiments, and for example, computer implemented methods, apparatuses, and/or program products, as well as claim language for the same, are in the scope of this disclosure. 
     For ease of understanding, various running examples will be utilized throughout this disclosure. Also, for ease of understanding, terminology such as A, B, C, or any combination thereof may include (i) A only. The terminology A, B, C, or any combination thereof may include (ii) B only. The terminology A, B, C, or any combination thereof may include (iii) C only. The terminology A, B, C, or any combination thereof may include (iv) A and B. The terminology A, B, C, or any combination thereof may include (v) A and C. The terminology A, B, C, or any combination thereof may include (vi) B and C. The terminology A, B, C, or any combination thereof may include (vii) A, B, and C. 
     Furthermore, some terms are used interchangeably herein, such as producer and production well, injector and injection well, zone level and zonal level, etc. 
     Flood Operation 
       FIG. 1  schematically illustrates an embodiment of a hydrocarbon reservoir  20  with a plurality of injection wells (or injectors) and a plurality of production wells (or producers) for a flood operation. The reservoir  20  can be practically any type of subterranean or subsurface formation in which hydrocarbons are stored, such as limestone, dolomite, oil shale, sandstone, any combination thereof, or other subsurface formation. The reservoir  20  can be located onshore, offshore, deepwater, or at another location. 
     The flood operation can inject practically any displacing fluid such as water, brine or salt water, water alternating gas referred to as WAG, gas (e.g., carbon dioxide), steam, surfactant, polymer, any combination thereof (e.g., combination of polymer and surfactant), or other material. The flood operation may be performed on the reservoir  20  over a few years or decades. More than one flood operation may also be performed on the reservoir  20 . For example, a first flood operation during a first time period may inject water into the reservoir  20  and a subsequent second flood operation during a second time period may inject polymer (or combination of polymer and surfactant) in the reservoir  20 , as described further herein. 
     The injection wells and the production wells may be placed at practically any location that may facilitate hydrocarbon production from the reservoir  20 . For example, some of the wells may be placed in locations that form a pattern. The pattern may be practically any pattern that can be used in a flood operation, such as a two spot pattern, a three spot pattern, a four spot pattern, a skewed four spot pattern, a five spot pattern, a seven spot pattern, an inverted seven spot pattern, a nine spot pattern, an inverted nine spot pattern, a direct line drive pattern, a staggered line drive pattern, a peripheral flood pattern, an irregular pattern (e.g., an irregular five spot pattern), any combination thereof, etc. The reservoir  20  may also include different patterns and no-pattern well configurations. 
     At the well level, the production wells  30 ,  34  and the injection well  32  are drilled and completed in the reservoir  20 . The production wells or injection wells can be completed in any manner (e.g., an openhole completion, a cemented casing and/or liner completion, a gravel-packed completion, etc.) As illustrated in  FIG. 1 , completions  42 ,  44 ,  46 ,  50 ,  52  provide fluid communication (e.g., via perforations) between the injection well  32 , the reservoir  20 , and the production wells  30 ,  34 . The production wells  30 ,  34  and the injection well  32  fluidly connect the reservoir  20  to surface  40  of the subterranean reservoir  20 . The surface  40  can be a ground surface as depicted in  FIG. 1 , a platform surface in an offshore environment, etc. 
     Chokes or well control devices  54 ,  56 ,  60  are used to control the flow of fluid into and out of respective production wells  30 ,  34  and injection well  32 . The well control devices  54 ,  56 ,  60  also control the pressure profiles in the production wells  30 ,  34  and the injection well  32 . Although not shown, the production wells  30 ,  34  and the injection well  32  can fluidly connect with surface facilities (e.g., separators such as oil/gas/water separators, gas compressors, storage tanks, pumps, gauges, pipelines, etc.) at the surface  40 . The rate of flow of fluids through the production wells  30 ,  34  and the injection well  32  can depend on the fluid handling capacities of the surface facilities at the surface  40 . The control devices  54 ,  56 ,  60  can be above surface and/or positioned downhole. 
     During the flood operation, the displacing fluid injected by the injection well  32  can drive the hydrocarbons of the reservoir  20  to the production well  30 , the production well  34 , or both depending on how the wells  30 ,  32 ,  34  are linked in the reservoir  20 . The displacing fluid injected by the injection well  32  can additionally drive the hydrocarbons of the reservoir  20  to a production well  62 , a production well  64 , or both depending on how the wells  30 ,  32 ,  34 ,  62 ,  64  are linked in the reservoir  20  and the completions. Similarly, the fluid injected by an injection well  66  can drive the hydrocarbons of the reservoir  20  to one or more of the production wells  30 ,  34 ,  62 ,  64  depending on how the wells are linked in the reservoir  20 , and so on for an injection well  68 . At the zone or zonal level, the reservoir  20  can include a plurality of rock layers including hydrocarbon bearing stratas or zones  22 ,  24 . This zonal level discussion will focus on wells  30 ,  32 ,  34 , but the discussion equally applies to wells  62 ,  64 ,  66 ,  68 . Also, the reservoir  20  can include more zones than those illustrated in  FIG. 1 . 
     The production wells  30 ,  34  and the injection well  32  extend into one or more of the hydrocarbon bearing zones  22 ,  24  of the reservoir  20  such that the production wells  30 ,  34  and injection well  32  are in fluid communication with the hydrocarbon bearing zones  22 ,  24 . The completions  42 ,  44 ,  46 ,  50 ,  52  provide fluid communication (e.g., via perforations) between the injection well  32 , the hydrocarbon bearing zones  22 ,  24 , and the production wells  30 ,  34 . The production wells  30 ,  34  can receive displacing fluids (e.g., gas, oil, water, etc.) from the hydrocarbon bearing zones  22 ,  24  and the injection well  32  can inject fluid into the hydrocarbon bearing zones  22 ,  24 . Also, the displacing fluid injected into one zone may flow into one or more different zones referred to as crossflow. The production wells  30 ,  34  and the injection well  32  fluidly connect hydrocarbon bearing zones  22 ,  24  to the surface  40  of the subterranean reservoir  20 . 
     During the flood operation, the fluid injected by the injection well  32  at the zone  22  can drive the hydrocarbons of the zone  22  of the reservoir  20  to the production well  30  at zone  22 , the production well  34  at zone  22 , or both depending on how the wells  30 ,  32 ,  34  are linked in the reservoir  20  at the zonal level. The fluid injected by the injection well  32  at the zone  22  can additionally drive the hydrocarbons of the zone  22  of the reservoir  20  to the production well  62  at zone  22  (not shown), production well  64  at zone  22  (not shown), or both depending on how the wells  30 ,  32 ,  34 ,  62 ,  64  are linked in the reservoir  20  at the zonal level and the completions at the zonal level. Similarly, the fluid injected by the injection well  32  at zone  24  can drive the hydrocarbons of the reservoir  20  to one or more of the production wells  30 ,  34 ,  62 ,  64  depending on how the wells are linked in the reservoir  20  at the zonal level and the completions at the zonal level. For example, the production well  34  is not completed at zone  24  and therefore hydrocarbons that flow along zone  24  towards the production well  34  may not be able to enter the production well  34  at zone  24 . Similarly, the fluid injected by the injection well  66  at zone  24  (not shown) can drive the hydrocarbons of the reservoir  20  to one or more of the production wells  30 ,  34 ,  62 ,  64  depending on how the wells are linked in the reservoir  20  at the zonal level and the completions at the zonal level, and so on for the injection well  68 . 
     Those of ordinary skill in the art will appreciate that  FIG. 1  is provided for context and various modifications are possible. For example, some embodiments can have fewer or more than the quantity of wells illustrated in  FIG. 1 , as well as different patterns or well locations. The production wells or the injection wells can also deviate from the illustrated vertical position such that in some embodiments, one or more wells can be a directional well, a horizontal well, or a multilateral well. 
     Hardware and Software 
       FIG. 2A  illustrates a computing system  200  useable to analyze a flood operation on a subterranean reservoir. The computing system  200  can, in example embodiments, be communicatively connected to systems providing data such as field data  222  and/or systems for further processing or interpreting the field data  222  as described herein. The field data  222  can include practically any data related to a field or components thereof. Examples of the field data  222  are discussed in connection with  FIGS. 4-5 . In some embodiments, the field data  222  can include non-field data, such as manufacturer data about a displacing fluid or material thereof (e.g., manufacturer data about a polymer), manufacturer data about a conformance agent, manufacturer data on equipment tolerances, etc. In general, the computing system  200  includes at least one processor  205  communicatively connected to at least one memory  204  via at least one data bus  206 . The processor  205  can be any of a variety of types of programmable circuits capable of executing computer-readable instructions to perform various tasks, such as mathematical and communication tasks. 
     The memory  204  can include any of a variety of memory devices, such as using various types of computer-readable or computer storage media. A computer storage medium or computer-readable medium may be any medium that can contain or store the program for use by or in connection with the instruction execution system, apparatus, or device. By way of example, computer storage media may include dynamic random access memory (DRAM) or variants thereof, solid state memory, read-only memory (ROM), electrically-erasable programmable ROM, optical discs (e.g., CD-ROMs, DVDs, etc.), magnetic disks (e.g., hard disks, floppy disks, etc.), magnetic tapes, and other types of devices and/or articles of manufacture that store data. Computer storage media generally includes at least one or more tangible media or devices. Computer storage media can, in some embodiments, include embodiments including entirely non-transitory components. In the embodiment shown, the memory  204  may store a flooding analysis application  212 , discussed in further detail below. The computing system  200  can also include a communication interface  208  configured to receive and transmit data, for example, the field data  222 . Additionally, a display  210  can be used for presenting a graphical display of the flooding analysis application  212  or applications or components thereof, for displaying maps (e.g., saturation and pressure maps such as from a petrotechnical mapping application or component  220 ), for displaying user interfaces, for displaying curves, for displaying graphs, for displaying plots, for displaying tables, etc. 
     In various embodiments, the flooding analysis application  212  includes a capacitance resistance modeling or CRM application or component  214 , a polygon application or component  216 , a zonal application or component  218 , the petrotechnical mapping application or component  220 . The polygon application  216  may be a streamgrid application or component  216 . CRM will be discussed further in the context of  FIG. 3 . In some embodiments, for example, the flooding analysis application  212  (or applications or components  214 ,  216 ,  218 ,  220  thereof) can include one or more other application or component  221  and/or receive data from the one or more other application or component  221 . The one or more other application or component  221  can be a vendor product, a petrotechnical application such as a petrotechnical 4D seismic processing application or component, etc. In some embodiments, the CRM application  214 , the polygon application  216 , the zonal application  218 , the petrotechnical mapping application  220 , and/or the one or more other application  221  can be standalone and separate from the flooding analysis application  212 . 
     Moreover, some or all the applications  214 ,  216 ,  218 ,  220 ,  221  can interact with each other (e.g., as illustrated in  FIGS. 2A, 2C ). For example, some or all of the applications  214 ,  216 ,  218 ,  220 ,  221  can interact by sharing data directly or providing data to a repository (e.g., operational data store (ODS)) that is accessible to the other applications. 
     In one embodiment, the flooding analysis application  212  may be associated with a solution, such as a flood or waterflood surveillance, analysis, &amp; optimization solution. The flood surveillance, analysis, &amp; optimization solution may be one of many solutions i-Field® program solutions, such as (i) a real time reservoir management solution, (ii) a well reliability &amp; optimization decision support center solution, (iii) an integrated operations center solution, (iv) a drilling &amp; completions decision support center solution, (v) a logistics decision support solution, (vi) a machinery &amp; power support center solution, and (vii) a real time facilities optimization solution. An Upstream Workflow Transformation (UWT) effort or other effort can be utilized to accelerate the adoption and deployment of the solutions globally. Each of the solutions may interact with each other. Each of these solutions may be associated with at least one application (e.g., the other application  221 ) just like the flood surveillance, analysis, &amp; optimization solution is associated with the flooding analysis application  212 . 
     Each of these solutions may receive data from and/or send data to a data management system referred to as data foundation or UWT data foundation. For example, the data management system may provide at least some of the field data  222  for the flooding analysis application  212 . Thus, in some embodiments, the field data  222  may represent the data management system. The data management system may be centralized, and business units have access to all data for all business units or limited access to the data based on permission. Alternatively, each business unit may have its own local data management system with data that is of interest to that business unit. 
     The data management system may help business units to source data for quicker, more efficient deployment of the solutions of interest. For example, some of the solutions may depend on a complex set of data from disparate IT systems across the business units, often with histories spanning decades. Many business units may also have existing legacy technology standards and IT systems in place with existing dependencies on service providers or vendors to support and maintain. Furthermore, some of the solutions may depend on an abundance of homeless data that may need to be collected, reviewed, and organized, and much of this data is decentralized across assets. Some of the solutions may also depend on unstructured data residing in non-traditional IT systems. The business units may differ widely in geographical locations, asset classes, business models, regulatory requirements, partnerships, and legal agreements that impact the ability to deploy solutions, especially where dependencies on complex data types exist. 
     The data management system provides a standardized architecture to improve efficiency in deployments of the solutions. For example, the data management system provides a standardized architecture (e.g., based on UWT target architecture and upstream data objectives), implements consistent data standards, implements data models based on internal and industry standards, manages all forms of data, provides mature data governance, provides data accountability, and implements managed integration to reuse data and enables business workflows. The target architecture describes the preferred model for sourcing data from business unit system of records, performing transformations to the data to support workflows, and moving this data into a format suitable for access by the appropriate solution. The data management system also increases organizational capability to improve data quality for better decision making and decreased risk, for example, improving the quality of the high value data in daily operations and making available high quality data to support growth rates in functions and business units. The data management system may also improve the ability to actively monitor every barrel from business unit assets and may increase the ability to proliferate business unit led innovations across other business units. 
       FIG. 2B  illustrates an embodiment of a data management system  250 . In  FIG. 2B , “RM” stands for reservoir management, “D&amp;C” stands for drilling and completions, “BU” stands for business unit, and “G&amp;G” stands for geology and geophysics. Of note, the data management system  250  includes a box  252  for data quality and business rule management, which may represent business rules to improve data quality. For example, the business rules can be applied to ensure certain data is not zero, certain data is numeric, certain data is text, certain data is alphanumeric, certain data is within a particular range, certain data is in the correct location, certain data is in the correct unit of measurement, certain data is transformed, etc. The business rules can also be based on correlations within data, such as existence of one rule dependent on existence of other data or range of other data. Also, business rules can search for heuristic trends within the data or boundary conditions such as date intervals between well tests. As another example, the business rules may ensure that wells are tested based on predetermined assumptions or may apply practically any logic test The result of a business rule can be a monitoring condition or an action applied programmatically. For example, the result of a business rule may be a report or alert of failures, transposition of data based on logical conditions, etc. The business rules may be generated based on feedback from business units, based on requirements of the computing system or applications, any combination thereof, etc. 
     To create the data management system  250 , existing business unit local data foundation designs can be leveraged. Alternatively, existing business local data foundation designs (e.g., functionally, technically, or by adding assets) can be extended. Alternatively, the system can be built by partnering with an interested business unit when there is no existing local data foundation capability to leverage prior designs. 
     The embodiment illustrated in  FIG. 2C  can be executed by the computing system  200  of  FIG. 2A .  FIG. 2C  is discussed further in U.S. Provisional Patent Application No. 62/040,909, filed Aug. 22, 2014, title FLOODING ANALYSIS TOOL AND METHOD THEREOF, which is incorporated by reference in its entirety. 
     Those of ordinary skill in the art will appreciate that although certain terminology is used herein, such as the terms solution, application, component, etc., the invention is not limited to the exact embodiments disclosed herein. For example, embodiments consistent with this disclosure can be performed using computer executable instructions, computer executable code, modules, data structures, graphs, plots, maps, etc., and the embodiments are not limited to any specific arrangement in this disclosure. 
     Referring generally to the systems and methods herein, and referring to in particular computing systems embodying the methods and systems of the present disclosure, it is noted that various computing systems can be used to perform the processes disclosed herein. For example, embodiments of the disclosure may be practiced in various types of electrical circuits comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, aspects of the methods described herein can be practiced within a general purpose computer or in any other circuits or systems. 
     Embodiments of the present disclosure can be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, routines, code, applications, programs, or program modules. Computer storage media may include RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other article of manufacture which can be used to store information and which can be accessed by the computing system  200 , above. Computer storage media does not include a carrier wave or other propagated or modulated data signal. In some embodiments, the computer storage media includes at least some tangible features; in many embodiments, the computer storage media includes entirely non-transitory components. 
     Embodiments of the present disclosure can be implemented in hardware only, software only, or a combination of hardware and software. Furthermore, embodiments of the present disclosure can include at least one server, at least on client device, a workstation, a distributed setup, a mobile device, etc. depending on the implementation. 
     Embodiments of the present disclosure, for example, are described herein with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the disclosure. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Embodiments may include fewer than or more than the functionality/acts provided herein. 
     Capacitance Resistance Modeling (CRM) 
     Typically, CRM is run using only historical injection rates and production rates. CRM is built based on material balance and rooted in signal processing for nonlinear multivariate regression. With a rate variation in an injector, CRM can capture interactions among production wells and injection wells by matching the response in the production rates. Typically, running CRM results in two sets of parameters, namely, interwell connectivity (F ij ) and response time (Tau or τ) per injection well and production well pair. The interwell connectivity quantifies the support from one injection well to one production well, and the response time estimates the time a production well takes to response to a variation in the injection rate. Those parameters can be determined via history matching the production rates of all production wells. CRM can have three forms: CRMT to represent the drainage volume of the entire field, CRMP to represent the drainage volume of each production well, and CRMIP to represent the drainage volume of each production well and injection well pair. 
     Injection rates and production rates are typically used as input to CRM, as discussed in Weber, et al. “Improvements in Capacitance-Resistive Modeling and Optimization of Large Scale Reservoirs,” SPE 121299, 2009 SPE Western Regional Meeting held in San Jose, Calif., USA, 24-26 Mar. 2009, which is incorporated by reference in its entirety. The following documents also discuss CRM, and each of these documents is incorporated by reference in its entirety: (i) Sayarpour, et al. “The Use of Capacitance-Resistive Models for Rapid Estimation of Waterflood Performance and Optimization”, SPE 110081, 2007 SPE Annual Technical Conference and Exhibition held in Anaheim, Calif., USA, 11-14 Nov. 2007, (ii) Sayarpour, et al., “Field Applications of Capacitance Resistive Models in Waterfloods”, SPE 114983-MS, 2008 SPE Annual Technical Conference and Exhibition held in Denver, Colo., USA, 21-24 Sep. 2008, (iii) Sayarpour, et al., “Field Applications of Capacitance Resistive Models in Waterfloods”, SPE 114983-PA, December 2009 SPE Reservoir Evaluation &amp; Engineering, (iv) Sayarpour, et al., “Probabilistic History Matching With the Capacitance-Resistance Model in Waterfloods: A Precursor to Numerical Modeling”, SPE 129604, 2010 SPE Improved Oil Recovery Symposium held in Tulsa, Okla., USA, 24-28 Apr. 2010, and (v) Sayarpour, M., “Development and Application of Capacitance-Resistive Models to Water/CO 2  Floods”, pages 1-236, available at http://repositories.lib.utexas.edu/handle/2152/15357?show=full, which are all incorporated by reference in their entireties. 
     Allowed Well Connections 
       FIGS. 3-12  illustrate embodiments of computer implemented methods of analyzing a flood operation on a hydrocarbon reservoir having at least one production well and at least one injection well, and more particularly, embodiments that include determining allowed connections that may be used as input to CRM. The embodiments illustrated in  FIGS. 3-12  can be executed by a computing system, such as the computing system  200  of  FIG. 2A  or the CRM application  214  thereof. The allowed well connections may affect history matching and generation of the interwell connectivities when CRM is run, which may lead to more accurate output from CRM, such as more accurate response times and interwell connectivities. The output from CRM and/or other data available after executing the embodiments may be used as input to update or integrate with the polygon application  216 , the zonal application  218 , the petrotechnical mapping application  220 , the one or more other application  221 , the flooding analysis application  212 , and/or other item (e.g., as illustrated in  FIGS. 2A, 2C ). 
     Referring to  FIGS. 1-13 ,  FIG. 3  illustrates one embodiment of a method  300  that can be executed by a computing system, such as the computing system  200 , to analyze a flood operation on the reservoir  20  or the like. For simplicity, the discussion will first focus on the well level, and then turn to the zonal level. 
     At  302 , field data is received. Receiving field data can include acquiring, capturing, obtaining, requesting, providing, etc. For example, the field data  222  can be received from the wells  30 ,  32 ,  34 ,  62 ,  64 ,  66 ,  68  of the reservoir  20 , the well control devices  54 ,  56 ,  60 , sensors, meters, transmitters, and other equipment (e.g., hydrophones, gauges, etc.), databases, ODS, the data management system  250  of  FIG. 2B , analysis at the surface  40  (e.g., laboratory analysis), data streams, users, any combination thereof, etc. The field data  222  can be received in a raw state or in a non-raw state (e.g., errors are removed from the raw data). The field data  222  may include text, values, measurements, signals, etc. The field data  222  can be static or dynamic. The field data  222  can be real-time data, but does not have to be real-time data. The field data  222  can be numeric, text, alphanumeric, etc. The field data  222  can be actual data from actual hydrocarbon production, or in some embodiments, the field data  222  can include simulation data, synthetic data, estimates, forecasts, predictions, etc. The field data  222  may be streaming or non-streaming. 
       FIG. 4  elaborates on the field data  222  that can be received, with the static data illustrated in gray shading and the dynamic data illustrated with no shading. Examples of field data  222  are illustrated as  402 - 420 , however, the particular field data received for a particular reservoir also depends on the particular field data that is actually gathered for that particular reservoir. For example, if there is both tracer data and 4D seismic data for the particular reservoir, then both of these can be received and used consistent with this disclosure. However, if there is 4D seismic data, but not tracer data, for the particular reservoir, then the 4D seismic data can be received and used consistent with this disclosure. As such,  FIG. 4  as well as  FIG. 5  illustrate examples of received field data and processing of the received field data, and should not be considered a minimum and not be considered a maximum. 
     The field data  222  can include static geological data  402  such as porosity, permeability, fracture data (e.g., fracture architecture, fracture locations, etc.), fault data (e.g., fault architecture, fault locations), rock information, etc. The geological data  402  can be determined by users (e.g., by examining core samples, outcrops, etc.), from models, from simulations, any combination thereof, etc. 
     The field data  222  can include static seismic data such as three dimensional (3D) seismic data  404 . The 3D seismic data  404  can be practically any geophysical data measured remotely. For example, the 3D seismic data  404  can be recorded through the use of active seismic sources (e.g., air guns or vibrator units) and receivers (e.g., hydrophones or geophones). The sources and receivers may be arranged in many configurations, and a seismic survey is designed to optimize the source and receiver configurations. Therefore, the 3D seismic data  404  can include seismic surveys, can be recorded by sensors or receivers at the surface  40 , can be recorded by sensors or receivers in boreholes, any combination thereof, etc. 
     The field data  222  can include dynamic well production data (e.g., hydrocarbon volumes produced data or production rate data), injection rate data (or simply injection data), pressure data, and location data  406 . For example, the well production data can be received from flow meters (e.g., multiphase flow meters) at the production wells  30 ,  34 ,  62 ,  64 , whereas the injection rate data can be received from flow meters at injections wells  32 ,  66 ,  68 . The pressure data can be received from pressure sensors, as well as other downhole or surface equipment. The location data can include x, y, and z coordinates, and can be received from sensors, GPS equipment, any combination thereof, etc. Of note, the well production data, the injection rate data, the pressure data, and the location data (or depth data) can be at the zonal level, for example, from the zonal application  218  depending on the embodiment. 
     The field data  222  can include dynamic fluid production geochemistry data  408 . For example, the geochemistry data  408  of the produced fluids from the production wells  30 ,  34 ,  62 ,  64  can provide the composition of the produced fluids, such as a listing of the different items in the produced fluid as well as quantity measurements for the different items in the produced fluid. The geochemistry data  408  can be received from equipment, from analysis at the surface  40 , from laboratory analysis, any combination thereof, etc. 
     The field data  222  can include dynamic tracer data  410 . A tracer is a material that can be injected with water or other flooding material into the injection well  32  during the flood operation, and the tracer can be detected after a period of time at one or more of the production wells  30 ,  34 ,  62 ,  64  depending on how the wells are linked in the reservoir  20 . The tracer data  410  measures movement of the tracer as it travels through the reservoir  20 . The tracer data  410  can be received from equipment, from analysis at the surface  40 , from laboratory analysis, any combination thereof, etc. 
     The field data  222  can include dynamic log data  412 , such as well log data and production log data. The field data  222  can also include dynamic well test data  414 . For example, well test data can include a production logging test (PLT), an injection logging test (ILT), etc. 
     The field data  222  can be dynamic seismic data, such as time lapse seismic data, also referred to as four dimensional (4D) seismic data  416 . For 4D seismic data  416 , a baseline seismic survey is performed to obtain a baseline seismic dataset and subsequent monitoring seismic surveys are performed to obtain one or more monitor seismic dataset(s). Differences between the baseline and the monitor seismic dataset(s) can be analyzed to determine changes in the subsurface reservoir  20  affected by the production and/or injection. The 3D seismic data  404  can be used to determine the 4D seismic data  416 . 
     The field data  222  can be polygon data  418 . For example, the polygon data  418  may include polygons indicating geo-bodies, geological boundaries or structural boundaries such as fault or fractures from 3D seismic and/or 4D seismic data, well patterns, etc. The polygons may be overlapping, overlaying, etc. The polygon data  418  may be received from the polygon application or component  216 . 
     The field data  222  can also include CRM output data  420 . For example, CRM may be run a plurality of times and the CRM output can be the CRM output data  420 . 
     Those of ordinary skill in the art will appreciate that the listing of field data  222  illustrated in  FIG. 4  is not exhaustive. More field data or different field data can be received in some embodiments. For example, as will be discussed hereinbelow, this disclosure expressly contemplates receiving field data at the zonal level (e.g., field data such as production data and injection data at the zonal level from the zonal application  218 ). 
     Returning to  FIG. 3 , at  304 , at least a portion of the received field data  222  can be processed. For example, some of the field data  222  received at  302  can be utilized as-is, but at least a portion of the received field data  222  can be processed by a computer executed method only, a user only, or both a computer executed method and a user. Processing the received field data  222  can include analyzing, interpreting, generating additional data, generating maps, generating curves, generating tables, laboratory experiments, etc. Thus, at  304 , the received field data  222  can be processed using computer algorithms, manual methodologies, interpretation (e.g., user interpretation), procedures, tests, equipment, etc. For example, the received field data  22  can be processed in a manner known to those of ordinary skill in the art. 
       FIG. 5  elaborates on the processing of the received field data. At  502 , geological boundaries and/or geobodies of the reservoir  20  can be determined from the seismic data (3D seismic data)  404 . For example, a petrotechnical application  221  executing on the computing system  200  of  FIG. 2A  can compute the geological boundaries and/or geobodies of the reservoir  20  from the 3D seismic data  404 . The petrotechnical application  221  can be separate from the flooding application  212  or part of the flooding application  221  depending on the embodiment. Furthermore, the geobodies can be determined from the well log data at  504  by the same petrotechnical application  221  or another application (e.g., another pertrotechnical application). At the zonal level, continuity of zones can also be determined from the well log data at  504 . 
     At  506 , the geochemistry data analysis can be conducted on the geochemistry data  408 . For example, the geochemistry data can be analyzed to determine salinity of produced fluids, determine the amount of water in the produced fluids, determine the various components of the produced fluids (e.g., fingerprinting the oil or produced fluids), etc. The geochemistry data analysis can be conducted at  506  by the same petrotechnical application  221  or another application (e.g., another pertrotechnical application). 
     At  508 , tracer data analysis can be conducted on the tracer data  410 . For example, the tracer data from the production wells  30 ,  34 ,  62 ,  64  can be analyzed to determine how fluid flows through the reservoir  20  and for estimating remaining hydrocarbons in the reservoir  20 . The tracer data analysis can be conducted at  508  by the same petrotechnical application  221  or another application (e.g., another pertrotechnical application). 
     At  510 , pressure transient analysis can be conducted on the well test data  414 . For example, one or more pulse tests can be part of the pressure transient analysis. The tracer data analysis can be conducted at  510  by the petrotechnical application  221  or another application (e.g., another pertrotechnical application). 
     At  512 , pressure and saturation can be determined from the time lapse data (4D seismic data)  416 . The pressure and saturation can be computed at  514  by the petrotechnical application  221  or another application (e.g., another pertrotechnical application). Furthermore, at  514 , maps can be generated and/or displayed using the determined pressure and saturation by the petrotechnical mapping application  220  or another application (e.g., another pertrotechnical application). The petrotechnical mapping application  220  can be separate from the flooding application  212 , separate from the petrotechnical application  221 , a part of the petrotechnical application  221 , or a part of the flooding analysis application  212  depending on the particular embodiment. The petrotechnical mapping application  220  can be a remaining resource application or component of the flooding analysis application  212 , for example, as in U.S. Non-Provisional patent application Ser. No. 13/952,783, filed Jul. 29, 2013, title System and method for remaining resource mapping, which is incorporated by reference in its entirety. Each of the following papers is also incorporated by reference in its entirety: (i) Dietrich, et al, “A Method for Determining Reservoir Fluid Saturations Using Field Production Data”, SPE 3961, SPE-AIME 47 th  Annual Fall Meeting held in San Antonio, Tex. on Oct. 8-11, 1972, and (ii) Gruy, H. J., “Graphing facilitates tracking water and gas influx”, Oil &amp; Gas Journal, Mar. 26, 1990, which are both incorporated by reference in their entireties. 
     For example, maps of estimated change in pressure and water, oil, and/or gas saturation of the reservoir  20  can be generated using data driven approaches that combine observed 4D seismic data or signal with well pressure and production/injection rate as explained further in U.S. Provisional Patent Application No. 62/081,968, filed Nov. 19, 2014, title SYSTEM AND METHOD FOR PRESSURE AND SATURATION ESTIMATION FROM TIME-LAPSE SEISMIC DATA, which is incorporated by reference in its entirety. A copy of the U.S. Provisional Patent Application No. 62/081,968 was included in U.S. Provisional Patent Application No. 62/135,016, which is incorporated herein by reference in its entirety. Alternatively, or additionally, maps of estimated change in pressure and water, oil, and/or gas saturation of the reservoir  20  can be generated using model driven approaches that use physics modeling to estimate changes of pressure and saturation from 4D seismic data or signal. Alternatively, or additionally, maps of seismic attributes can be computed from 4D seismic data or signal. 
     Indeed, the pressure and saturation, and maps thereof, can include qualitative visualization of cross sections and map views of 4D seismic amplitudes (e.g., by a computer executed method only, a user only, or both a computer executed method and a user) and/or qualitative visualization of cross sections and map views of seismic impedances from inversion of seismic data (e.g., by a computer executed method only, a user only, or both a computer executed method and a user). Of note, although maps can be generated and displayed to a user, in some embodiments, maps do not need to be generated and displayed to a user. 
     The various items  502 - 514  and  402 - 420  can be checked for consistency at  516 . For example, at least a portion of the various items  502 - 514  and  402 - 420  can be reviewed for accuracy, contradictions, and overall consistency by a computer executed method only, a user only, or both a computer executed method and a user. The consistency interpretation at  516  can be performed by computerized methods only, users only, or both computerized methods and users depending on the embodiment. If any inconsistencies are found, control can pass to  304  to continue to re-process as appropriate and as many times as appropriate. The loop and iterations can continue until a sufficient level of consistency is reached. 
     The various items  502 - 514  and  402 - 416 , after the consistency check, can lead to results  518 - 528 . For example, the results  518 - 528  can include (i) the faults and the fractures ( 518 ) from the geological data ( 402 ), (ii) the geological boundaries ( 520 ) from the 3D seismic data ( 502 ,  404 ), (iii) the geobodies ( 522 ) from the 3D seismic data ( 502 ,  404 ) and/or the well log data ( 504 ,  412 ), (iv) the well production data, injection rate data, and the pressure date ( 524 ) from  406 , (v) maps of change in pressure and saturation ( 526 ) from the 4D seismic data and/or the pressure and saturation maps ( 514 ,  512 ,  416 ), (vi) polygons  517  from the polygon data  418 , and/or (vii) interwell connectivities and response times from the CRM output data  420 . Moreover, the results include preliminary well connections ( 528 ) or evidence thereof, for example, from the tracer data analysis ( 508 ,  410 ) and/or the pressure transient analysis ( 510 ,  414 ). This listing of results is not exhaustive and other results can also be included. 
     Returning to  306 ,  308 ,  310  of  FIG. 3 , practically any available data at this point (e.g., the received field data  402 - 416  and the processed field data (or interpreted field data)  502 - 514  and  518 - 528 ), can be used for input to CRM and/or to determine allowed well connections at  308 ,  310  and the allowed well connections are input to CRM. For example, injection rates and production rates can be used as input to CRM. Therefore, the well production data and the injection rate data  406  can be used as input to CRM at  306 . 
     However, consistent with this disclosure, input to CRM will also include allowed well connections, not just the well production data and the injected rate data  406 . In a particular example, some but not all of the preliminary well connections  528  will be allowed well connections depending on the determination. The allowed well connections can be determined at  308  from at least a portion of available data at this point (e.g., the received field data  402 - 416  and the processed field data (or interpreted field data)  502 - 514  and  518 - 528  illustrated in  FIG. 5 ). For example, in some embodiments, time lapse seismic data (4D seismic data), including interpreted time lapse seismic data (interpreted 4D seismic data) and any of  416 ,  512 ,  514 , can be used as an input to CRM and/or to determine allowed well connections and the allowed well connection are input to CRM. Interpreted time lapse seismic data (interpreted 4D seismic data) can be generated by, for example, at least one of: 
     (a) maps of estimated change in pressure and water, oil, and/or gas saturation of the reservoir generated using data driven approaches that combine observed 4D seismic data or signal with well pressure and production/injection rate as explained further in U.S. Provisional Patent Application No. 62/081,968, filed Nov. 19, 2014, title SYSTEM AND METHOD FOR PRESSURE AND SATURATION ESTIMATION FROM TIME-LAPSE SEISMIC DATA, which is incorporated by reference in its entirety—a copy of the U.S. Provisional Patent Application No. 62/081,968 was included in U.S. Provisional Patent Application No. 62/135,016, which is incorporated herein by reference in its entirety, 
     (b) maps of estimated change in pressure and water, oil, and/or gas saturation of the reservoir generated using model driven approaches that use physics modeling to estimate changes of pressure and saturation from 4D seismic data or signal, 
     (c) maps of seismic attributes computed from 4D seismic data or signal, 
     (d) qualitative visualization of cross sections and map views of 4D seismic amplitudes (e.g., by computerized methods, interpretation by a user, or both computerized methods and interpretation by a user), and/or 
     (e) qualitative visualization of cross sections and map views of seismic impedances from inversion of seismic data (e.g., by computerized methods, interpretation by a user, or both computerized methods and interpretation by a user). 
     Although maps can be generated and displayed to a user, in some embodiments, maps do not need to be generated and displayed to a user. Therefore, interpreted time lapse seismic data can be generated by the data behind the maps via computerized methods, interpretation by a user, or both computerized methods and interpretation by a user. Nonetheless, the allowed well connections can be determined at  308  from at least a portion of available data at this point (e.g., the received field data  402 - 416  and the processed field data (or interpreted field data)  502 - 514  and  518 - 528  illustrated in  FIG. 5 ). Moreover, any of this available data can be used for input to CRM and/or to determine allowed well connections at  308 ,  310  and the allowed well connections are the input to CRM. For example, in some embodiments, time lapse seismic data (4D seismic data), including interpreted time lapse seismic data (interpreted 4D seismic data) and any of  416 ,  512 ,  514 , can be used as an input to CRM and/or to determine allowed well connections and the allowed well connection are input to CRM. Moreover, in some embodiments, time lapse seismic data (4D seismic data), including interpreted time lapse seismic data (interpreted 4D seismic data) and any of  416 ,  512 ,  514 , along with other data (e.g., tracer data) can be used as an input to CRM and/or to determine allowed well connections and the allowed well connection are input to CRM. In short, practically any of the data available at this point can be used as input to CRM (e.g., constraints to CRM) and/or to determined allowed well connections that will be input to CRM (e.g., constraints to CRM). 
       FIG. 6  provides a method  600 , which is an embodiment of a method for determining the allowed well connections. The method  600  can be executed by the CRM application  214 . In some embodiments, the method  600  can be iterative, and allowed well connections can be determined for each injection well and production well pair. The method  600  can be executed for multiple injection wells or executed per injection well. The dataset of allowed well connections may be modified (e.g., iteratively) based on a distance category, a static features category, a dynamic features category, or any combination thereof. For example, the database of allowed well connections may be modified as will be discussed below in connection with  604 ,  606 ,  608 . For ease of understanding, a running example for determining allowed connections will be discussed in the context of  FIGS. 7-10 . For simplicity, the running example will focus on injection wells Inj-05 and Inj-08 and production wells Pro-01 through Pro-12. Bold and strikethrough are used to indicate the changes between iterations. 
     The method  600  can be accomplished by a computer executed method only, a user only, or both a computer executed method and a user. In some embodiments, the order of distance, then static features, and then dynamic features of the method  600  can also be changed such that the dynamic features are higher priority and used first, then the static features, then the distance. Moreover, within each category, there can also be a priority (e.g., geobodies have a higher priority than geological boundaries or permeability, or vice versa, within the static feature category) (e.g., 4D seismic data or the pressure and saturation from 4D seismic data has a higher priority than tracer data, or vice versa, within the dynamic features category). Thus, there can be a priority between categories (e.g., dynamic features, then static features, then distance) and/or a priority within a category (e.g., geobodies have a higher priority than permeability within the static feature category or vice versa). Moreover, a sensitivity analysis can be run for each category and the allowed well connections per category can be based on the sensitivity analysis per category. 
     In a computer method only implementation of method  600 , the computer method can rely on location data, boundaries, thresholds such as if the production well is within a number of feet or percentage outside of a geobody or saturation and pressure map boundary, and other data to determine the allowed well connections. Furthermore, it can display the corresponding diagrams/maps as in  FIGS. 7-10 , but it does not need to display the diagrams/maps. In some embodiments, a user can determined the allowed well connections as indicated in method  600 , or revise the allowed well connections determined by a computer method only implementation. For example, the user can do so if he or she wants to use more flexibility and expertise, and not thresholds, in deciding whether a production well is connected or not to an injection well. 
     At  602 , a dataset of allowed well connections can be established. The values for the allowed well connections can be initially established by indicating that a well connection exists for each injection well and production well pair. For example, a value of 1, yes, or other indication can be used to indicate a well connection between an injection well and a production well. In the running example, at  602 , the dataset of allow well connections may indicate that each injection well can potentially support each production well, in other words, that there is a well connection between every production well and each injection well, as illustrated below: 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Dataset of allowed well connections 
                 . . . 
                 Inj-05 
                 . . . 
                 Inj-08 
               
               
                   
               
             
            
               
                 Pro-01 
                   
                 1 
                   
                 1 
               
               
                 Pro-02 
                   
                 1 
                   
                 1 
               
               
                 Pro-03 
                   
                 1 
                   
                 1 
               
               
                 Pro-04 
                   
                 1 
                   
                 1 
               
               
                 Pro-05 
                   
                 1 
                   
                 1 
               
               
                 Pro-06 
                   
                 1 
                   
                 1 
               
               
                 Pro-07 
                   
                 1 
                   
                 1 
               
               
                 Pro-08 
                   
                 1 
                   
                 1 
               
               
                 Pro-09 
                   
                 1 
                   
                 1 
               
               
                 Pro-10 
                   
                 1 
                   
                 1 
               
               
                 Pro-11 
                   
                 1 
                   
                 1 
               
               
                 Pro-12 
                   
                 1 
                   
                 1 
               
               
                   
               
            
           
         
       
     
     At  604 , the dataset of allowed well connections can be modified based on a distance category (e.g., inverse distance). For example, a value of 1 for a particular injection well and production well pair is changed from 1 to 0 to indicate that a well connection does not exist based on distance for that particular injection well and production well pair. Alternatively, the value is kept at 1 if the well connection exists based on the distance category. A priority between categories may be received from a user, for example, and the priority may indicate that the distance category is first. Similarly, a priority within the distance features category may be used and received from a user. A user may designate the data that will be part of the distance category. 
     Returning to the running example, the diagrams or maps at  FIGS. 7-8  may be used to illustrate the distance category. Regarding permeability in  FIG. 8 , permeability can be treated as distance ( FIG. 8 ) or as a static feature ( FIG. 9 ) depending on the embodiment. In the running example, at  604 , the dataset of allowed well connections may be modified according to the distance illustrated in the diagram of  FIG. 7  to indicate allowed well connections between the Inj-05 and Pro-04 pair, the Inj-05 and Pro-05 pair, the Inj-05 and Pro-07 pair, the Inj-05 and Pro-08 pair, the Inj-08 and Pro-07 pair, the Inj-08 and Pro-08 pair, the Inj-08 and Pro-10 pair, and the Inj-08 and Pro-11 pair, as illustrated below: 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Dataset of allowed well connections  
                   
                 Inj- 
                   
                 Inj- 
               
               
                 modified by distance category per FIG. 7 
                 . . . 
                 05 
                 . . . 
                 08 
               
               
                   
               
             
            
               
                 Pro-01 
                   
                       0   
                   
                       0   
               
               
                 Pro-02 
                   
                       0   
                   
                       0   
               
               
                 Pro-03 
                   
                       0   
                   
                       0   
               
               
                 Pro-04 
                   
                 1 
                   
                       0   
               
               
                 Pro-05 
                   
                 1 
                   
                       0   
               
               
                 Pro-06 
                   
                       0   
                   
                       0   
               
               
                 Pro-07 
                   
                 1 
                   
                 1 
               
               
                 Pro-08 
                   
                 1 
                   
                 1 
               
               
                 Pro-09 
                   
                       0   
                   
                       0   
               
               
                 Pro-10 
                   
                       0   
                   
                 1 
               
               
                 Pro-11 
                   
                       0   
                   
                 1 
               
               
                 Pro-12 
                   
                       0   
                   
                       0   
               
               
                   
               
            
           
         
       
     
     Alternatively, in the running example, at  604 , the dataset of allowed well connections may be modified according to the distance illustrated in the diagram of  FIG. 8  to indicate allowed well connections between the Inj-05 and Pro-05 pair, the Inj-05 and Pro-07 pair, the Inj-08 and Pro-08 pair, and the Inj-08 and Pro-10 pair, as illustrated below: 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 Dataset of allowed well connections modified  
                   
                 Inj- 
                   
                 Inj- 
               
               
                 by distance category per FIG. 8 
                 . . . 
                 05 
                 . . . 
                 08 
               
               
                   
               
             
            
               
                 Pro-01 
                   
                       0   
                   
                       0   
               
               
                 Pro-02 
                   
                       0   
                   
                       0   
               
               
                 Pro-03 
                   
                       0   
                   
                       0   
               
               
                 Pro-04 
                   
                       0   
                   
                       0   
               
               
                 Pro-05 
                   
                 1 
                   
                       0   
               
               
                 Pro-06 
                   
                       0   
                   
                       0   
               
               
                 Pro-07 
                   
                 1 
                   
                       0   
               
               
                 Pro-08 
                   
                       0   
                   
                 1 
               
               
                 Pro-09 
                   
                       0   
                   
                       0   
               
               
                 Pro-10 
                   
                       0   
                   
                 1 
               
               
                 Pro-11 
                   
                       0   
                   
                       0   
               
               
                 Pro-12 
                   
                       0   
                   
                       0   
               
               
                   
               
            
           
         
       
     
     As in the running example, within the distance category, the distance associated with  FIG. 7  and the permeability associated with  FIG. 8  led to different well connections for the distance category. To resolve the conflict, modifying the dataset of allowed well connections according the distance category may include using a priority within the distance category. In the running example, distance was selected over permeability and therefore the distance was given the higher priority within the distance category for determining the well connections. If the running example included a second distance in addition to the distance of  FIG. 7  and the permeability of  FIG. 8 , then the highest priority within the distance category may be the second distance, then distance of  FIG. 7 , then the permeability of  FIG. 8  for the allowed connections. 
     At  606 , the dataset of allowed well connections can be modified based on a static features category such as geological facies, etc. (e.g., same geobody from 3D seismic data, permeability, etc.). The static features category uses static data of the hydrocarbon reservoir, and the static data may include geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, geological boundaries, a processed version of any of these, or any combination thereof. If a particular injection well and production well pair indicates a value of 1, yes, or some other indication that the well connection does exist based on distance, but the static features suggest that a well connection does not exist for that pair, then the value for that well connection for that pair can be changed to 0, no, or some other indication. By doing so, the static features have a higher priority than distance, thereby selecting the highest priority. The priority between categories may be received from a user. Similarly, a priority within the static features category may be used and received from a user. A user may designate the data that will be part of the static features category. 
     Returning to the running example, the diagram or map at  FIG. 9  may be used to illustrate the static features category. Assuming the previous values associated with  FIG. 7 , in the running example, at  606 , the dataset of allowed well connections may be modified according to the static features (e.g., geobodies  1  and  2  from geological study and 3D seismic data) illustrated in the diagram of  FIG. 9  to indicate allowed well connections between the Inj-05 and Pro-02 pair, the Inj-05 and Pro-05 pair, the Inj-05 and Pro-07 pair, the Inj-08 and Pro-08 pair, the Inj-08 and Pro-10 pair, the Inj-08 and Pro-11 pair, and the Inj-08 and Pro-11 pair, as illustrated below: 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Dataset of allowed well  
                   
                   
                   
                   
               
               
                 connections modified by static  
                   
                   
                   
                   
               
               
                 features category per FIG. 9 
                 . . . 
                 Inj-05 
                 . . . 
                 Inj-08 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 Pro-01 
                   
                      0 
                   
                      0 
               
               
                 Pro-02 
                   
                           1   
                   
                      0 
               
               
                 Pro-03 
                   
                      0 
                   
                      0 
               
               
                 Pro-04 
                   
                       0   
                   
                      0 
               
               
                 Pro-05 
                   
                 1 
                   
                      0 
               
               
                 Pro-06 
                   
                      0 
                   
                      0 
               
               
                 Pro-07 
                   
                 1 
                   
                       0   
               
               
                 Pro-08 
                   
                       0   
                   
                 1 
               
               
                 Pro-09 
                   
                      0 
                   
                      0 
               
               
                 Pro-10 
                   
                      0 
                   
                 1 
               
               
                 Pro-11 
                   
                      0 
                   
                 1 
               
               
                 Pro-12 
                   
                      0 
                   
                      0 
               
               
                   
               
            
           
         
       
     
     At  608 , the database of allowed well connections can be modified based on a dynamic features category such as 4D seismic data, tracer data analysis, pressure transient analysis, etc. (e.g., connected geobody from 4D seismic data). The dynamic features category uses dynamic data of the hydrocarbon reservoir, and the dynamic data may include well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, capacitance resistance modeling output data, polygon data, a processed version of any of these, or any combination thereof. If a particular injection well and production well pair indicates a value of 1, yes, or some other indication that the well connection does exist based on the static features, but the dynamic features suggest that a well connection does not exist for that pair, then the value for that well connection for that pair can be changed to 0, no, or some other indication. By doing so, the dynamic features have a higher priority than static features, thereby selecting the higher priority. The priority between categories may be received from a user. Similarly, a priority within the dynamic features category may be used and received from a user. A user may designate the data that will be part of the dynamic features category. 
     Returning to the running example, the diagrams and maps at  FIG. 10  may be used to illustrate the dynamic features category. Assuming the previous values associated with  FIG. 9 , in the running example, at  608 , the dataset of allowed well connections may be modified according to the dynamic features (e.g., connected volume1-4D data and connected volume2-tracer data) illustrated in the diagram of  FIG. 10  to indicate allowed well connections between the Inj-05 and Pro-02 pair, the Inj-05 and Pro-05 pair, the Inj-05 and Pro-07 pair, the Inj-05 and Pro-08 pair, and no well connections for Inj-08 but for Inj-11 instead, as illustrated below: 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 5 
               
               
                   
               
               
                 Dataset of allowed 
                   
                   
                   
                   
                   
                   
               
               
                 well connections modified 
                   
                   
                   
                   
                   
                   
               
               
                 by dynamic features 
                   
                   
                   
                   
                   
                   
               
               
                 category per FIG. 10 
                 . . . 
                 Inj-05 
                 . . . 
                 Inj-08 
                 . . . 
                 Inj-11 
               
               
                   
               
             
            
               
                 Pro-01 
                   
                     0 
                   
                     0 
                   
                   
               
               
                 Pro-02 
                   
                        1 
                   
                     0 
                   
                   
               
               
                 Pro-03 
                   
                     0 
                   
                     0 
                   
                   
               
               
                 Pro-04 
                   
                     0 
                   
                     0 
                   
                   
               
               
                 Pro-05 
                   
                 1 
                   
                     0 
                   
                   
               
               
                 Pro-06 
                   
                     0 
                   
                     0 
                   
                   
               
               
                 Pro-07 
                   
                 1 
                   
                     0 
                   
                   
               
               
                 Pro-08 
                   
                      1   
                   
                 
                   
                 
                   
                   
               
               
                 Pro-09 
                   
                     0 
                   
                     0 
                   
                   
               
               
                 Pro-10 
                   
                     0 
                   
                 
                   
                 
                   
                 
                   1 
                 
               
               
                 Pro-11 
                   
                     0 
                   
                 
                   
                 
                   
                 
                   1 
                 
               
               
                 Pro-12 
                   
                     0 
                   
                     0 
               
               
                   
               
            
           
         
       
     
     Of note, the dynamic features category indicates that Pro-10 and Pro-11 are connected to Inj-11 instead of Inj-08 based on the connected volume 1 (4D seismic data) and connected volume2 (tracer data).  FIG. 10  also illustrated a sealing fault referred to as the fault1. The fault1, which may be considered in the static features category, further confirms that Pro-10 and Pro-11 are connected to Inj-11 instead of Inj-08. Thus, a combination of static features and dynamic features can also be used together as a category (e.g., during one iteration) in some embodiments, as illustrated in  FIG. 10  with the fault1, the 4D seismic data, and the tracer data. 
     Furthermore, a sensitivity analysis can be run for each category and the allowed well connections per category can be based on the sensitivity analysis per category. For example, the values in Table 2 for the distance category may be based on a sensitivity analysis for the distance category. In the sensitivity analysis, a first distance may lead to first values, a second distance may lead to second values, a third distance may lead to third values, and so on, and if the value of 1 appears the most for a well pair then the value of 1 is selected for that well pair but if the value of 0 appears the most for a well pair then the value of 0 is selected for that well pair. A similar approach can be pursued for the static features category, the distance features category, or both. The sensitivity analysis for category may try to account for the variability (e.g., variability of a particular data item in that category such as first 4D seismic data, second 4D seismic data, etc. in the dynamic features category or first geological boundary, second geological boundary, etc. in the static features category) and potentially increase the accuracy of the allowed well connections for the category, and in turn potentially increase the accuracy of the final allowed well connections that are input to CRM. 
     At  610 , the remaining well connections (and values thereof) after the distance, static features, and dynamic features are the allowed well connections  308  that are input for CRM. As illustrated above, out of 24 possible well pairs including Inj-05 and Inj-08, there are 4 allowed well connections namely the Inj-05 and Pro-02 pair, the Inj-05 and Pro-05 pair, the Inj-05 and Pro-07 pair, and the Inj-05 and Pro-08 and none for Inj-08. The dataset of allowed well connections in Table 6 may be used as input for CRM. Alternatively, the dataset of allowed well connections provided as input to CRM may only include the well connections with a value of 1. 
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                   
                 TABLE 6 
               
               
                   
                   
               
               
                   
                 Dataset of allowed 
                   
                   
                   
                   
                   
                   
               
               
                   
                 well connections 
                 . . . 
                 Inj-05 
                 . . . 
                 Inj-08 
                 . . . 
                 Inj-11 
               
               
                   
                   
               
             
            
               
                   
                 Pro-01 
                   
                 0 
                   
                 0 
                   
                   
               
               
                   
                 Pro-02 
                   
                 1 
                   
                 0 
                   
                   
               
               
                   
                 Pro-03 
                   
                 0 
                   
                 0 
                   
                   
               
               
                   
                 Pro-04 
                   
                 0 
                   
                 0 
                   
                   
               
               
                   
                 Pro-05 
                   
                 1 
                   
                 0 
                   
                   
               
               
                   
                 Pro-06 
                   
                 0 
                   
                 0 
                   
                   
               
               
                   
                 Pro-07 
                   
                 1 
                   
                 0 
                   
                   
               
               
                   
                 Pro-08 
                   
                 1 
                   
                 0 
                   
                   
               
               
                   
                 Pro-09 
                   
                 0 
                   
                 0 
                   
                   
               
               
                   
                 Pro-10 
                   
                 0 
                   
                 0 
                   
                 1 
               
               
                   
                 Pro-11 
                   
                 0 
                   
                 0 
                   
                 1 
               
               
                   
                 Pro-12 
                   
                 0 
                   
                 0 
               
               
                   
                   
               
            
           
         
       
     
     After running CRM, the output of CRM may be used to further modify the dataset of allowed well connections. For example, the output of CRM such as the interwell connectivities (and Tau&#39;s) may be used to confirm well connections, add a new well connection (e.g., change 0 value to a 1 value for a well pair in the dataset of allowed well connections), remove a well connection (e.g., change 1 value to a 0 value for a well pair in the dataset of allowed well connections), etc. As an example, an interwell connectivity value of 0 or below a threshold for a particular well pair can lead to removing a well connection for that particular well pair by selecting 0 for that well pair in the dataset of allowed well connections. Indeed, CRM output available before or after running CRM may be treated as part of the dynamic features category to remove or add at least one allowed well connection to the dataset of allowed well connections. For instance, if dynamic data like tracer indicates no connection exists between a particular well pair while pulse test indicates a connection does exist between that same well pair, and then the value can be set to 1 for the well pair if pulse test has a higher priority but after running CRM, the CRM output indicates that the pulse test was incorrect so the value is changed to 0 to indicate no well connection for that well pair. The dataset of allowed well connections revised with the CRM output may be used as the initial dataset at  602 . 
     Furthermore, those of ordinary skill in the art will appreciate that various modification can be made to the disclosed embodiments. For example, in some embodiments, the initial dataset of allowed well connections can be based on the distance category, instead of assuming all well connections exist, and the initial dataset of allowed well connections may be modified based on the static features category, the dynamic features category, or both. Similarly, in some embodiments, the initial dataset of allowed well connections can be based on the dynamic features category, instead of assuming all well connections exist, and the initial dataset of allowed well connections may be modified based on the static features category, the distance category, or both. Similarly, the initial dataset of allowed well connections can be based on previously determined allowed well connections. In some embodiments, all or fewer than all categories may be used to determine the allowed well connections (e.g., the static features category only, the dynamic features category only, or both). Furthermore, data may be received at different points (e.g., not just at  602 ), and therefore the dataset of allowed well connections may be modified a plurality of times before the allowed well connections are used as input to CRM. 
       FIG. 11  provides a method  1100 , which is another embodiment of a method for determining allowed well connections. The method  1100  can be executed by the CRM application  214 . The method  600  modifies a single dataset of allowed well connections whereas the method  1100  can generate a plurality of datasets (e.g., one dataset for a dynamic features category, one dataset for a static features category, and one data set for a distance category) and the plurality of datasets can be combined into a single dataset of allowed well connections based on priority. 
     At  1102 , the method  1100  includes determining well connections based on a distance category (e.g., inverse distance). At  1104 , the method  1100  includes determining allowed well connections based on a static features category such as geological facies, etc. (e.g., same geobody from 3D seismic data). At  1106 , the method  1100  includes determining allowed well connections based on a dynamic features category such as 4D seismic data, tracer data analysis, pressure transient analysis, etc. (e.g., connected geobody from 4D seismic data). At  1108 , the method  1100  includes comparing the determined allowed well connections. At  1110 , the method  1100  includes combining the determined allowed well connections from the various datasets based on the comparisons and a priority (e.g., combining based on the dynamic features category having the highest priority of the three, then the static features category having the next higher priority, and then the distance category) into a combined dataset of allowed well connections. At  1112 , the remaining allowed well connections after the distance, static features, and dynamic features are combined are the allowed well connections  310  that are input for CRM. 
       FIG. 12  provides an example consistent with the method  1100  and illustrates one dataset per category. The three datasets can be combined into one dataset of allowed well connections for input to CRM. Similarly, various datasets can be generated from  FIGS. 7-10  and combined according to method  1100 . 
     The method  1100  can be accomplished by a computer executed method only (e.g., using thresholds, boundaries, location data, etc.), a user only (e.g., for more flexibility), or both a computer executed method and a user (e.g., for more flexibility) as described hereinabove for method  600 . Furthermore, in some embodiments, the order of distance, then static features, and then dynamic features of the method  600  can also be changed in the method  1100  such that the dynamic features are higher priority and used first at  1102 , then the static features, then the distance. Moreover, there can be a priority between categories (e.g., dynamic features, then static features, then distance) and/or a priority within a category (e.g., geobodies have a higher priority than permeability within the static feature category or vice versa). Furthermore, as discussed in connection with  FIG. 6 , the priority between categories may be received from a user. Similarly, a priority within a category may be used and received from a user. Sensitivity analysis per category may also be performed. Moreover, a user may designate the data that will be part of the categories. After running CRM, the output of CRM may also be used to further modify the allowed well connections. 
     Returning to  FIG. 3 , at  312 , CRM can be run using the allowed well connections as input to CRM from  310  and the other input to CRM from  306 . For example, the CRM application  214  can run CRM to quantify the interwell connectivities using techniques known in the art, such as those in the documents incorporated herein. However, consistent with this disclosure, CRM can be modified to quantify the interwell connectivities in a manner that is consistent with the available data (e.g., from  FIG. 5 ), consistent with the allowed well connections determined in  FIGS. 6, 11  that are input, etc. In other words, CRM can be provided with constraints to enforce consistency with the available data (e.g., the 4D seismic data, the pressure and saturation from the 4D seismic data, the pressure and saturation maps, etc.) and the allowed well connections. Table 7 (below) illustrates an example of potential interwell connectivities that can be generated by CRM: 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 7 
               
               
                   
               
               
                 Interwell Connectivities (F ij ) 
                   
                   
                   
                   
                   
                   
               
               
                 generated with allowed well 
                   
                 Inj- 
                   
                 Inj- 
                   
                 Inj- 
               
               
                 connections as an input to CRM 
                 . . . 
                 05 
                 . . . 
                 08 
                 . . . 
                 11 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Pro-01 
                   
                 0 
                   
                 0 
                   
                   
               
               
                 Pro-02 
                   
                 .1 
                   
                 0 
                   
                   
               
               
                 Pro-03 
                   
                 0 
                   
                 0 
                   
                   
               
               
                 Pro-04 
                   
                 0 
                   
                 0 
                   
                   
               
               
                 Pro-05 
                   
                 .3 
                   
                 0 
                   
                   
               
               
                 Pro-06 
                   
                 0 
                   
                 0 
                   
                   
               
               
                 Pro-07 
                   
                 .4 
                   
                 0 
                   
                   
               
               
                 Pro-08 
                   
                 .2 
                   
                 0 
                   
                   
               
               
                 Pro-09 
                   
                 0 
                   
                 0 
                   
                   
               
               
                 Pro-10 
                   
                 0 
                   
                 0 
                   
                 .3 
               
               
                 Pro-11 
                   
                 0 
                   
                 0 
                   
                 .2 
               
               
                   
               
            
           
         
       
     
     At  314 , practically any data available thus far can be integrated with at least one application or component by a computer executed method only, a user only, or both a computer executed method and a user. For example, the data available at this point can be the most current data (e.g., the most current interwell connectivities determined by CRM, the most current allowed well connections, the most current 4D seismic data, the most current pressure and saturation from the 4D seismic data, etc.). An application that uses or receives as input that type of data can be updated to integrate the data, for example, the application can be the petrotechnical mapping application  220 , a simulation application, etc. such as illustrated in  FIG. 2A, 2C . 
     Furthermore, at  314 , additional interpretation can occur. For example, the additional interpretation can be a check for consistency, can find any inconsistencies, etc., which may also lead to integration of data into at least one application. The interpretation can happen before or after integration of data into at least one application. Control can pass to  304  to restart the loop. 
     At  316 , reservoir decisions can be addressed. For example, a recommendation can automatically be generated based on the available data. Alternatively, a user can make at least one reservoir decision based on the available data. The reservoir decisions can relate to optimization, history matching, conformance control, changing a parameter of the flood operation, etc., for example, to improve the flood operation. Control can pass to  302  to restart the loop to receive more field data  222 . 
     At the zonal level, a similar approach can be used. In some embodiments, each zone may be treated as an injection well, and allowed well connections for each injection well/zone and production well pair can be determined as described hereinabove in  FIGS. 6, 11 . For example, at  302 , the received field data can include zonal splits data from the zonal application  218  that indicates the percentage of water, oil, gas, and/or injection (e.g., water injection) per zone. At  304 , the received field data at the zonal level can be further processed as illustrated in  FIG. 3 , for example, to determine continuity of zones from the well log data  504 . At  308 ,  310 , the allowed well connections at the zonal level can be determined as illustrated in  FIGS. 6, 11  and an example is illustrated below at Table 8. 
     In Table 8, 0&#39;s and 1&#39;s are illustrated for each zone and production well pair. Table 8 assumes the existence of 3 zones in the reservoir, a plurality of injection wells, and a plurality of production wells. Table 8 also assumes that only the injection well Inj-01 has perforations and fluidic contact with the three zones. In this example, there are 10 allowed well connections out of a possible 15 because the analysis is at the zonal level. At the well level, there would have been 9 possible allowed well connections, but Table 8 illustrates 10 allowed well connections out of a possible 15 at the zonal level: 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 8 
               
               
                   
               
               
                 Dataset of allowed 
                   
                   
                   
                   
                   
                   
               
               
                 well connections at 
                 Inj-01 
                 Inj-01 
                 Inj-01 
                   
                   
                   
               
               
                 the zonal level 
                 zone-01 
                 zone-02 
                 zone-03 
                 Inj-02 
                 Inj-03 
                 . . . 
               
               
                   
               
             
            
               
                 Pro-01 
                 0 
                 0 
                 1 
                 1 
                 1 
                   
               
               
                 Pro-02 
                 1 
                 1 
                 0 
                 1 
                 1 
                   
               
               
                 Pro-03 
                 1 
                 0 
                 0 
                 1 
                 1 
                   
               
               
                 . . . 
               
               
                   
               
            
           
         
       
     
     Furthermore, as discussed in connection with  FIGS. 6, 11 , a priority between categories may be used and received from a user. Similarly, a priority within a category may be used and received from a user. Sensitivity analysis per category may also be performed. Moreover, a user may designate the data that will be part of the categories. 
     At  312 , CRM can be run using the allowed well connections at the zonal level as input to CRM and the other input to CRM. After running CRM, the output of CRM may also be used to further modify the allowed well connections at the zonal level, as discussed in connection with  FIGS. 6, 11 . At  314 , the available data at the zonal level at this point can be integrated with at least one application. At  316 , reservoir decisions can be addressed at the zonal level, such as optimization at the zonal level, conformance control at the zonal level, history matching at the zonal level, changing a parameter at the zonal level, etc. 
     Those of ordinary skill in the art will appreciate that various embodiments and modifications are envisioned. For example, the input to CRM may be the entire dataset of allowed well connections with all of the 0&#39;s and 1&#39;s (such as from  FIG. 6 ) or the entire combined dataset of allowed well connections with all of the 0&#39;s and 1&#39;s (such as from  FIG. 11 ) at the well level. For example, the input to CRM may be the entire dataset of allowed well connections with all of the 0&#39;s and 1&#39;s (such as from  FIG. 6 ) or the entire combined dataset of allowed well connections with all of the 0&#39;s and 1&#39;s (such as from  FIG. 11 ) at the zonal level. For example, the input to CRM may be the dataset of allowed well connections with only the 1&#39;s (such as from  FIG. 6 ) or the combined dataset of allowed well connections with only the 1&#39;s (such as from  FIG. 11 ) at the well level. For example, the input to CRM may be the dataset of allowed well connections with only the 1&#39;s (such as from  FIG. 6 ) or the combined dataset of allowed well connections with only the 1&#39;s (such as from  FIG. 11 ) at the zonal level. Other modifications will become evident to those of ordinary skill in the art. 
     Those of ordinary skill in the art will appreciate that the accuracy of CRM can potentially be increased by the allowed well connections that are determined as the well level and/or at the zonal level. For example, the allowed well connection are based on dynamic features (e.g., 4D seismic data), static features, and/or distance for the reservoir, therefore potentially leading to improvements in the analysis of flood operations. Moreover, hidden faults and fractures are more likely to be identified and used in the analysis, multiple field data types suggesting the same are more likely to be identified and used in the analysis (e.g., tracer data and pulse test data supporting the existence of a well connection), conflicting data is more likely to be resolved (e.g., through priorities), etc. 
     Furthermore, in some embodiments, the analysis of a flood operation can be carried out in a faster or more efficient manner as only allowed well connections with 1&#39;s are input to CRM and/or processed further by CRM. In some examples, the analysis of the flood operation may speed up about 200 times or running CRM may decrease from hours, such as about 400 hours, to minutes, such as about 20 minutes. 
     Furthermore, those of ordinary skill in the art may appreciate that more accurate CRM output may be used to make at least one decision that is likely to improve the flood operation. The decisions may be related to optimization, history matching, conformance control, changing a parameter of the flood operation, or any combination thereof. 
     Heavy Oil Flood Operation 
     The hydrocarbon in some hydrocarbon reservoirs is referred to as heavy oil. A flood operation, such as waterflood operation, may be performed on a hydrocarbon reservoir having heavy oil. Disclosed herein are embodiments for using CRM to analyze a flood operation on a hydrocarbon reservoir having heavy oil. As will be discussed herein, various time windows may be generated and CRM may be run for each time window so as to account for mobility, time, or other items characteristics that are specific to hydrocarbon reservoirs having heavy oil. By doing so, CRM output may be more accurate and the analysis of the flood operation may be more accurate. 
     An embodiment of a computer implemented method of analyzing a flood operation on a hydrocarbon reservoir having heavy oil, referred to as  1400  in  FIG. 14 . The method  1400  can be executed by a computing system, such as the computing system  200  of  FIG. 2A  or the CRM application  214  thereof. After executing the method  2000  of  FIG. 20 , the resulting output may be used as input to update or integrate with the polygon application  216 , the zonal application  218 , the petrotechnical mapping application  220 , the one or more other application  221 , the flooding analysis application  212 , and/or other item (as in  FIGS. 2A, 2C ). For ease of understanding, a running example will be used and the running example assumes one injection well and three production wells. 
     At  1402 , the method  1400  receives injection rate data and production rate data for production wells communicating with the first injection well for a period of time. For example, the method receives well names, well locations, injection rates, and production rates. At  1404 , the method  1400  establishes a plurality of time windows based on breakthrough time of each production well that has broken through until the current date, wherein the production rate data is indicative of breakthrough. For example, a time window may be established for each producer that has broken through, and optionally, an extra time window. For example, 4 producers that have broken through may lead to establishing 4 or 5 time windows. In some embodiments, 10% watercut may be used as the breakthrough time indicator. In the running example, dates D2 and D3 are at 10% watercut. As such, a first time window from time 0-time 15 is established for the first producer to breakthrough and reach watercut of 10%, a second time window from time 15-time 60 is established for the second producer to breakthrough and reach watercut of 10%, and a third time window from time 60-time 130 is established for all producers to breakthrough and reach watercut of 10%, as illustrated below: 
     
       
         
           
               
               
               
               
               
               
             
               
                   
               
               
                   
                   
                 Time,  
                   
                   
                   
               
               
                   
                 data 
                 month 
                 CRM-fij for I1-P1 
                 CRM-fij for Inj1-P2 
                 CRM Runs 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                 0 
                   
                   
                   
               
               
                   
                 1 
                 1 
                   
                   
                   
               
               
                   
                 2 
                 2 
                   
                   
                   
               
               
                 first window at  
                 3 
                 3 
                   
                   
                   
               
               
                 date D1 
                   
                   
                   
                   
                   
               
               
                   
                 4 
                 4 
                   
                   
                   
               
               
                   
                 5 
                 6 
                   
                   
                   
               
               
                   
                 6 
                 8 
                   
                   
                   
               
               
                   
                 7 
                 10 
                 0.743155395 
                 0.256844605 
                 Run1 - No Breakthrough 
               
               
                   
                 8 
                 15 
                   
                   
                 D1 
               
               
                   
                 9 
                 25 
                 0.784838501 
                 0.215161499 
                 Sliding Window Run - Overlap  
               
               
                   
                   
                   
                   
                   
                 Time Window 
               
               
                   
                 10 
                 30 
                   
                   
                   
               
               
                 second window at  
                 11 
                 38 
                 0.792383944 
                 0.207616056 
                 Run2 - Early stage post  
               
               
                 date D2 
                   
                   
                   
                   
                 breakthrough of first well - P1 
               
               
                   
                 12 
                 50 
                   
                   
                   
               
               
                   
                 13 
                 60 
                   
                   
                 D2-10% watercut first producer 
               
               
                   
                 14 
                 75 
                   
                   
                   
               
               
                   
                 15 
                 80 
                   
                   
                   
               
               
                   
                 16 
                 88 
                 0.922930965 
                 0.077069035 
                 Sliding Window Run - Overlap  
               
               
                   
                   
                   
                   
                   
                 Time Window 
               
               
                   
                 17 
                 95 
                   
                   
                   
               
               
                   
                 18 
                 100 
                   
                   
                   
               
               
                 third window at  
                 19 
                 110 
                 0.890533376 
                 0.109466624 
                 Run3-Early stage after BT of  
               
               
                 date D3 
                   
                   
                   
                   
                 another well-P2 (repeat when  
               
               
                   
                   
                   
                   
                   
                 another well BT) 
               
               
                   
                 20 
                 120 
                   
                   
                   
               
               
                   
                 21 
                 130 
                   
                   
                 D3-10% watercut second  
               
               
                   
                   
                   
                   
                   
                 producer 
               
               
                   
                 22 
                 132 
                   
                   
                   
               
               
                   
                 23 
                 133 
                 0.909424554 
                 0.090575446 
                 Sliding Window Run - Overlap  
               
               
                   
                   
                   
                   
                   
                 Time Window 
               
               
                   
                 24 
                 142 
                   
                   
                   
               
               
                   
                 25 
                 149 
                   
                   
                   
               
               
                   
                 26 
                 150 
                   
                   
                   
               
               
                   
                 27 
                 153 
                   
                   
                   
               
               
                   
                 28 
                 161 
                   
                   
                   
               
               
                   
                 29 
                 166 
                   
                   
                   
               
               
                   
                 30 
                 172 
                   
                   
                   
               
               
                   
                 31 
                 181 
                 0.899863617 
                 0.100136383 
                 Run4-After Breakthrough of All  
               
               
                   
                   
                   
                   
                   
                 Wells until the last data point 
               
               
                   
                 32 
                 182 
                   
                   
                   
               
               
                   
                 33 
                 191 
                   
                   
                 D4: current date 
               
               
                   
                 34 
                 201 
                   
                   
                   
               
               
                   
                 35 
                 211 
                   
                   
                   
               
               
                   
                 36 
                 221 
                   
                   
                   
               
               
                   
                 37 
                 231 
                   
                   
                   
               
               
                   
                 38 
                 241 
                   
                   
                   
               
               
                   
                 39 
                 251 
                   
                   
                   
               
               
                   
                 40 
                 261 
               
               
                   
               
            
           
         
       
     
     At  1406 , CRM may be run for each established time window to generate interwell connectivities. In the running example, for the first time window ending at date D1, the CRM output may include the following interwell connectivities fijs: 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                   
                   
                 CRM-fij for I1-P1 
                 CRM-fij for Inj1-P2 
               
               
                   
               
             
            
               
                   
                 First time window 
                 0.743155395 
                 0.256844605 
               
               
                   
               
            
           
         
       
     
     In the running example, for the second time window ending between D1 and D2, the CRM output may include the following interwell connectivities fijs: 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                   
                   
                 CRM-fij for I1-P1 
                 CRM-fij for Inj1-P2 
               
               
                   
               
             
            
               
                   
                 Second time 
                 0.784838501 
                 0.215161499 
               
               
                   
                 window 
                 0.792383944 
                 0.207616056 
               
               
                   
               
            
           
         
       
     
     In the running example, for the third time window ending between D3 and D2, the CRM output may include the following interwell connectivities fijs: 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                   
                   
                 CRM-fij for I1-P1 
                 CRM-fij for Inj1-P2 
               
               
                   
               
             
            
               
                   
                 Third time window 
                 0.922930965 
                 0.077069035 
               
               
                   
                   
                 0.890533376 
                 0.109466624 
               
               
                   
               
            
           
         
       
     
     Optionally, at  1408 - 1410 , at least one sliding time window may be established and CRM may be run for each sliding time window. The sliding time window may be established to improve the distribution of the interwell connectivity variation between CRM runs 1, 2, 3, and 4. A particular sliding time window may be established between two time windows established at  1404 . In the running example, a sliding time window may be established from time 25-time 88, another sliding time window may be established from time 88-time 133, and another sliding time window may be established from time 133-time 191. The following tables indicate the interwell connectivities for the sliding time windows. 
     
       
         
           
               
               
               
             
               
                   
               
               
                   
                 CRM-fij for I1-P1 
                 CRM-fij for Inj1-P2 
               
               
                   
               
             
            
               
                 first sliding time 
                 0.784838501 
                 0.215161499 
               
               
                 window 
                 0.792383944 
                 0.207616056 
               
               
                   
                 0.922930965 
                 0.077069035 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
               
             
               
                   
               
               
                   
                   
                 CRM-fij for I1-P1 
                 CRM-fij for Inj1-P2 
               
               
                   
               
             
            
               
                   
                 Second sliding time  
                 0.922930965 
                 0.077069035 
               
               
                   
                 window 
                 0.890533376 
                 0.109466624 
               
               
                   
                   
                 0.909424554 
                 0.090575446 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
               
             
               
                   
               
               
                   
                   
                 CRM-fij for I1-P1 
                 CRM-fij for Inj1-P2 
               
               
                   
               
             
            
               
                   
                 third sliding time  
                 0.909424554 
                 0.090575446 
               
               
                   
                 window 
                 0.899863617 
                 0.100136383 
               
               
                   
               
            
           
         
       
     
     At  1412 , the method  1400  may solve a continuous function for the generated interwell connectivity of each well pair over time using the discrete interwell connectivities obtained in  1406 - 1410 . The continuous function may be a bell shaped, log-normal, betta, logistic curve, or other. In the running example, the interwell connectivity variation plot at  FIG. 15  may be generated by plotting the interwell connectivities generated by CRM and then at least one function may be used to fit the interwell connectivity points (e.g., logistic curve) on a curve. The curve and the interwell connectivity points may be displayed to a user in some embodiments, but not in other embodiments. 
     In the running example, solving the continuous function may include solving the parameters of the function by matching function estimated interwell connectivities to the CRM generated interwell connectivities. Matching the interwell connectivities includes minimizing the error between the function estimated interwell connectivities and the CRM generated interwell connectivities. The parameters and the function estimated fij match values are illustrated below: 
     
       
         
           
               
               
               
               
               
               
             
               
                   
               
               
                   
                 Initial 
                   
                   
                   
                   
               
               
                   
                 fij 
                 Growth 
                 Decline Rate 
                 Integration,  
                 Peak Time, 
               
               
                   
                 Value 
                 Rate (G) 
                 (D) 
                 N 
                 Years 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 0.7 
                 0.05 
                 0.005 
                 90 
                 65 
               
               
                   
                 0.3 
                 0.05 
                 0.005 
                 90 
                 65 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
             
               
                   
               
               
                   
                 Function 
                 Function 
               
               
                   
                 estimated fij 
                 estimated fij 
               
               
                 Time, 
                 Match for 
                 Match for 
               
               
                 month 
                 I1-P1 
                 I1-P2 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 0 
                 0.729400211 
                 0.270599789 
               
               
                 1 
                 0.730789629 
                 0.269210371 
               
               
                 2 
                 0.732238642 
                 0.267761358 
               
               
                 3 
                 0.733749198 
                 0.266250802 
               
               
                 4 
                 0.735323244 
                 0.264676756 
               
               
                 6 
                 0.738669556 
                 0.261330444 
               
               
                 8 
                 0.742292858 
                 0.257707142 
               
               
                 10 
                 0.746207837 
                 0.253792163 
               
               
                 15 
                 0.757357586 
                 0.242642414 
               
               
                 25 
                 0.785903843 
                 0.214096157 
               
               
                 30 
                 0.803196639 
                 0.196803361 
               
               
                 38 
                 0.833762714 
                 0.166237286 
               
               
                 50 
                 0.878277722 
                 0.121722278 
               
               
                 60 
                 0.901382431 
                 0.098617569 
               
               
                 75 
                 0.904417667 
                 0.095582333 
               
               
                 80 
                 0.904258082 
                 0.095741918 
               
               
                 88 
                 0.903870664 
                 0.096129336 
               
               
                 95 
                 0.903399187 
                 0.096600813 
               
               
                 100 
                 0.902987362 
                 0.097012638 
               
               
                 110 
                 0.901978363 
                 0.098021637 
               
               
                 120 
                 0.900726493 
                 0.099273507 
               
               
                 130 
                 0.899237858 
                 0.100762142 
               
               
                 132 
                 0.898912333 
                 0.101087667 
               
               
                 133 
                 0.898746146 
                 0.101253854 
               
               
                 142 
                 0.897149159 
                 0.102850841 
               
               
                 149 
                 0.895783723 
                 0.104216277 
               
               
                 150 
                 0.895580055 
                 0.104419945 
               
               
                 153 
                 0.894956349 
                 0.105043651 
               
               
                 161 
                 0.893201722 
                 0.106798278 
               
               
                 166 
                 0.892039321 
                 0.107960679 
               
               
                 172 
                 0.890579879 
                 0.109420121 
               
               
                 181 
                 0.888263626 
                 0.111736374 
               
               
                 182 
                 0.887997153 
                 0.112002847 
               
               
                 191 
                 0.885520243 
                 0.114479757 
               
               
                 201 
                 0.882609705 
                 0.117390295 
               
               
                 211 
                 0.879544368 
                 0.120455632 
               
               
                   
               
            
           
         
       
     
     At  1414 , the production rate data is used to solve an oil-cut model for each production well that has broken through to estimate oil-cut value for each production well in the future. In the running example, the received production rate data for each producer may be used to solve the parameter of the oil cut model (e.g., power law model). Alpha and beta (below) are the parameters of the oil cut model and a plot of oil cut is illustrated in  FIG. 16 : 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 Oil-cut Model 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 breakthrough time 
                 30 
                 100 
               
               
                   
                 alpha 
                 0.1 
                 0.2 
               
               
                   
                 beta 
                 0.5 
                 0.7 
               
               
                   
                   
               
               
                   
                 Time, month 
                 Oil Cut-P1 
                 Oil Cut-P2 
               
               
                   
                   
               
               
                   
                 0 
                   
                   
               
               
                   
                 1 
                 1 
                 1 
               
               
                   
                 2 
                 1 
                 1 
               
               
                   
                 3 
                 1 
                 1 
               
               
                   
                 4 
                 1 
                 1 
               
               
                   
                 6 
                 1 
                 1 
               
               
                   
                 8 
                 1 
                 1 
               
               
                   
                 10 
                 1 
                 1 
               
               
                   
                 15 
                 1 
                 1 
               
               
                   
                 25 
                 1 
                 1 
               
               
                   
                 30 
                 1 
                 1 
               
               
                   
                 38  
                 0.7795188 
                 1 
               
               
                   
                 50 
                 0.690983 
                 1 
               
               
                   
                 60  
                 0.6461106 
                 1 
               
               
                   
                 75  
                 0.5985084 
                 1 
               
               
                   
                 80 
                 0.5857864 
                 1 
               
               
                   
                 88  
                 0.5676731 
                 1 
               
               
                   
                 95  
                 0.5536406 
                 1 
               
               
                   
                 100  
                 0.5444666 
                 1 
               
               
                   
                 110 
                 0.527864  
                 0.499407 
               
               
                   
                 120 
                 0.513167  
                 0.380465 
               
               
                   
                 130 
                 0.5 
                 0.316176 
               
               
                   
                 132  
                 0.4975247  
                 0.306491 
               
               
                   
                 133  
                 0.4963052  
                 0.301931 
               
               
                   
                 142  
                 0.4858377 
                 0.267581 
               
               
                   
                 149  
                 0.4782695 
                 0.24697 
               
               
                   
                 150  
                 0.4772256 
                 0.24435 
               
               
                   
                 153  
                 0.4741463  
                 0.236897 
               
               
                   
                 161  
                 0.4662978 
                 0.21957 
               
               
                   
                 166  
                 0.4616399  
                 0.210266 
               
               
                   
                 172  
                 0.4562798  
                 0.200331 
               
               
                   
                 181  
                 0.4486678  
                 0.187448 
               
               
                   
                 182  
                 0.4478515  
                 0.186144 
               
               
                   
                 191  
                 0.4407504  
                 0.175352 
               
               
                   
                 201  
                 0.4333376  
                 0.165047 
               
               
                   
                 211  
                 0.4263733 
                 0.15614 
               
               
                   
                   
               
            
           
         
       
     
     At  1416 , the value of injected fluid as a function of time in future may be determined for each injector using the continuous function estimated interwell connectivity  1412  and the estimated oil-cut value of  1414 . In the running example, VOIF(t) is calculated for the current timestep D4 and the timesteps in the forecast time window (1 or 2 or 3 years after the current date D4) and get an average VOIF. The following equation may be calculated to VOIF(t) for each injector: 
               VOIF   ⁡     (   t   )       =       ∑   j   N     ⁢           ⁢         f   ij     ⁡     (   t   )       ⁢       f   oj     ⁡     (   t   )                 
In the equation, f oj  (t) is oil cut as a function of time and f ij  (t) is interwell connectivity as a function of time. The (t) in the equation, such as in VOIF(t), is a function of time. In some embodiments, the f ij (t) may be at a steady state as discussed herein. Alternatively, if transient is desired, then fij(t) may be replaced with f*ij(t) in the equation. In the running example, 0.39 is the average forecast VOIF after current time D4 for I1, as illustrated below and at  FIG. 17 :
 
Average Forecast VOIF Post D4 for I1
         0.39       

     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                 Time,  
                 VOIF I1 as a function of 
               
               
                   
                 month  
                 time 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 0 
                   
                   
               
               
                   
                 1 
                 1 
                   
               
               
                   
                 2 
                 1 
                   
               
               
                   
                 3 
                 1 
                   
               
               
                   
                 4 
                 1 
                   
               
               
                   
                 6 
                 1 
                   
               
               
                   
                 8 
                 1 
                   
               
               
                   
                 10 
                 1 
                   
               
               
                   
                 15 
                 1 
                   
               
               
                   
                 25 
                 1 
                   
               
               
                   
                 30 
                 1 
                   
               
               
                   
                 38 
                 0.816 
                   
               
               
                   
                 50  
                 0.729 
                   
               
               
                   
                 60 
                 0.681 
                   
               
               
                   
                 75  
                 0.637 
                   
               
               
                   
                 80  
                 0.625 
                   
               
               
                   
                 88  
                 0.609 
                   
               
               
                   
                 95  
                 0.597 
                   
               
               
                   
                 100  
                 0.589 
                   
               
               
                   
                 110  
                 0.525 
                   
               
               
                   
                 120  
                 0.500 
                   
               
               
                   
                 130  
                 0.481 
                   
               
               
                   
                 132  
                 0.478 
                   
               
               
                   
                 133  
                 0.477 
                   
               
               
                   
                 142  
                 0.463 
                   
               
               
                   
                 149  
                 0.454 
                   
               
               
                   
                 150  
                 0.453 
                   
               
               
                   
                 153  
                 0.449 
                   
               
               
                   
                 161  
                 0.440 
                   
               
               
                   
                 166  
                 0.435 
                   
               
               
                   
                 172  
                 0.428 
                   
               
               
                   
                 181  
                 0.419 
                   
               
               
                   
                 182  
                 0.419 
                   
               
               
                   
                 191  
                 0.410 
                 D4: current date 
               
               
                   
                 201  
                 0.402 
                   
               
               
                   
                 211  
                 0.394 
                   
               
               
                   
                 221  
                 0.386 
                   
               
               
                   
                 231  
                 0.379 
                   
               
               
                   
                 241  
                 0.372 
               
               
                   
                   
               
            
           
         
       
     
     At  1418 , a plurality of injectors may be ranked based on average forecast VOIF (accounts for time) values for each injector for the forecasted time window, such as from current time to a defined period of timein. In the running example, there is only 1 injector, and it has as an average forecast VOIF of 0.39. However, this injector could be ranked against another injector. 
     At  1420 , the generated average forecast value of injected fluid for the first injection well is compared with a threshold to determine if the first injection well is a candidate for stimulation. For example, a low average forecast VOIF may indicate that the corresponding injector is a candidate for stimulation (e.g., stimulation with an acid agent). 
     Those of ordinary skill in the art will appreciate that various modifications may be made to the embodiments disclosed herein. For example, all producers had broken through in the running example, but such need not be the case. For example, if only a portion of the producers had broken through, then oil cut can equal 1 for injectors with corresponding producers that have not broken through. Also, the number of time windows may be based on producers broken through, so the fewer producers broken through then the fewer time windows may be established. 
     Those of ordinary skill may appreciate the illustrate embodiments may lead to more accurate analysis of flood operations of reservoirs having heavy oil Furthermore, the method  1400  may also be able to recommend an injection rate for each injector based on the ranking per average forecast VOIF and operation limits. The method  1400  may output (e.g., display, print, etc.) the injection rate recommendation and a user may then physically make the appropriate adjustments to achieve the recommend injection rate for a particular injector. Adjusting the injection rate based on the ranking per average forecast VOIF from and operational limits of injectors and producers may improve hydrocarbon recovery. 
     Conformance Control 
     A flood operation sometimes leads to non-uniform volumetric sweep or a deviation from uniform volumetric sweep in a reservoir because of the reservoir heterogeneity, unfavorable mobility ratio, gravity segregation, gas override, well placement, perforations, completions, casing, etc. Such a scenario is known as a conformance problem and hydrocarbon production may increase by conformance control. Based on input data, different tools may be used as shown in the table below: 
     
       
         
           
               
               
               
               
               
               
               
               
               
             
               
                   
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                 Well Profiles 
               
               
                 Data/ 
                 EV 
                   
                   
                 CRM 
                 Numerical 
                   
                 Modified 
                 (static and 
               
               
                 Evaluation 
                 Estimates 
                 Koval 
                 CRM 
                 Zonal 
                 Simulation 
                 Tracer 
                 Hall Plot 
                 dynamic logs) 
               
               
                   
               
             
            
               
                 Model calibration 
                 x 
                 x 
                 x 
                 x 
                 x 
                 x 
                   
                   
               
               
                 Logs, Core data 
                 x 
                 x 
                   
                   
                 x 
                   
                   
                 x 
               
               
                 &amp; tests 
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 Injection/ 
                 x 
                 x 
                 x 
                 x 
                 x 
                 x 
                 x 
                   
               
               
                 Production data 
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 Water chemistry 
                   
                   
                   
                   
                 x 
                 x 
                   
                   
               
               
                 Oil/gas chemistry 
                   
                   
                   
                   
                 x 
                 x 
                   
                   
               
               
                 Injection profiles 
                   
                   
                   
                 x 
                 x 
                   
                   
                 x 
               
               
                 Production profiles 
                   
                   
                   
                 x 
                 x 
                   
                   
                 x 
               
               
                 Reservoir pressures 
                   
                   
                 x 
                 x 
                 x 
                   
                 x 
                   
               
               
                 Flowing BHPs 
                   
                   
                 x 
                 x 
                 x 
                   
                 x 
                   
               
               
                 Tracer data 
                   
                   
                 x 
                 x 
                 x 
                 x 
                   
                   
               
               
                 Saturation logs 
                   
                   
                   
                 x 
                 x 
                   
                   
                   
               
               
                 Seismic data 
                   
                   
                 x 
                 x 
                 x 
                   
                   
                   
               
               
                 Flow-Storage 
                   
                 x 
                 x 
                 x 
                 x 
                 x 
                   
                 x 
               
               
                 Capacity 
               
               
                   
               
            
           
         
       
     
     To screen for conformance issues where an injecting entity (an injection entity can be a field, a reservoir field, reservoir, well or zone) causes early and excessive displacing fluid production at a producing entity (a producing entity can be a field, a reservoir, a well, or a zone) and reduces hydrocarbon recovery, analytical tools such as CRM, modified Koval method (MKM), modified Hall Plot, tracer and flow-storage capacity plotsand, etc. may be used. To identify and rank conformance candidates at the level of any injecting and producing entity (e.g., a field, a reservoir, a well, or a zone), a conformance problem index (CPI) is introduced herein. 
     
       
         
           
               
               
            
               
                   
               
               
                   
                 Method 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                   
                 Well Profiles 
               
               
                   
                   
                   
                 Numerical 
                   
                 Modified 
                 (static and 
               
               
                   
                 Koval 
                 CRM 
                 Simulation 
                 Tracer 
                 Hall Plot 
                 dynamic logs) 
               
               
                   
               
               
                 Applied to Field/Reservoir Level 
                 X 
                 X 
                 X 
                   
                   
                   
               
               
                 Screening 
                   
                   
                   
                   
                   
                   
               
               
                 Applied to Injection Entity 
                   
                 X 
                 X 
                 X 
                 X 
                   
               
               
                 Screening 
                   
                   
                   
                   
                   
                   
               
               
                 Applied to Production Entity 
                 X 
                   
                 X 
                 X 
                   
                   
               
               
                 Screening 
                   
                   
                   
                   
                   
                   
               
               
                 Applied to Injection Entity - 
                   
                 X 
                 X 
                 X 
                   
                 X 
               
               
                 Zonal Level 
                   
                   
                   
                   
                   
                   
               
               
                 Applied to Production Entity - 
                 X 
                   
                 X 
                 X 
                   
                 X 
               
               
                 Zonal Level 
                   
                   
                   
                   
                   
                   
               
               
                 Applied Injection-Production 
                   
                 X 
                 X 
                 X 
                   
                   
               
               
                 Entity Pair Level Screening 
               
               
                   
               
            
           
         
       
     
     An embodiment of a computer implemented method of identifying at least one conformance candidate (e.g., using the CPI and components thereof) is provided herein, referred to the method  1800  (numbers  1802 - 1820 ) in  FIG. 18 . The method  1800  may correspond to the diagnostic &amp; screening portion of the conformance control workflow of  FIG. 13 . The method  1800  can be executed by a computing system, such as the computing system  200  of  FIG. 2A . After executing the method  2000  of  FIG. 20 , the resulting output may be used as input to update or integrate with the CRM application  214 , polygon application  216 , the zonal application  218 , the petrotechnical mapping application  220 , the one or more other application  221 , the flooding analysis application  212 , and/or other item (e.g., as illustrated in  FIGS. 2A, 2C ). 
     Defining Conformance Problem Index (CPI): As disclosed herein, a CPI is evaluated at the level of injecting and producing entities by defining and evaluating its three main components, which are Injection Entity Index (IEI), Production Entity Index (PEI) and Operational Index ( 0 I), as illustrated below:
 
CPI=IEI*PEI*OI
 
     The CPI value ranges between 0 and 1, the higher the CPI value the higher the degree of conformance issue in the entity (e.g., producing/injecting entities). Two running examples will be presented to illustrate use of the CPI at a field/reservoir level and at a well/zone level. 
     Defining Injection Entity Index: An IEI is defined by combining (i) injection efficiency and (ii)) Value of Injected Fluid (VIOF) obtained between at least one injecting entity and one producing entity pair (e.g., an injecting-producing entity pair could be at reservoir-reservoir level, injector-producer level, or injector-producer at zonal level) and (iii) pore volume injected (PVI) for each injecting entity. This calculation can be obtained based on analytical methods such as capacitance-resistance model (CRM), tracer analysis, or any combination thereof. In some embodiments, fij, may also be used. Nonetheless, an IEI ranges between 0 and 1, and may be solved according to the equation below:
 
IEI=(1− f   ij )*VOIF ij /PVI i  
 
     Defining Producing Entity Index (PEI): A PEI is defined by evaluating an estimate (or a proxy of fraction) of remaining moveable oil in place at one pore volume injected for at least one producing entity. This calculation can be obtained based on analytical methods such as modified Koval method (MKM), tracer analysis, or any combination thereof. A recovery factor estimate, RF@1PVI is used in calculating PEI. A PEI ranges between 0 and 1, and may be solved according to the equation below:
 
PEI=1−RF@1PVI
 
     Defining the Operational Index (OI): An OI is a value of either one or zero which represents operation status (e.g., the availability of a given producing or injecting entity) for any conformance treatment. 
     Application when injection and production entities are field/reservoir—the field/reservoir level injection efficiency for an example of four field/reservoir is shown below: 
     
       
         
           
               
            
               
                   
               
               
                 CRM or Tracer Output 1 
               
            
           
           
               
               
               
            
               
                   
                 Injection Efficiency  
                 Injection Entities  
               
               
                   
                 between Injection and  
                 (Field or Reservoir) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Production Entities 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Production 
                 PE 01 
                 0.75 
                   
                   
                   
               
               
                 Entities 
                 PE 02 
                   
                 0.40 
                   
                   
               
               
                 (Field or 
                 PE 03 
                   
                   
                 0.92 
                   
               
               
                 Reservoir) 
                 PE 04 
                   
                   
                   
                 0.60 
               
               
                   
                 Sum 
                 0.75 
                 0.4 
                 0.92 
                 0.6 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are field/reservoir—the field/reservoir level value of injected fluid (VOIF) for an example of four field/reservoir is shown below: 
     
       
         
           
               
            
               
                   
               
               
                 CRM Output 2 
               
            
           
           
               
               
               
            
               
                   
                   
                 Injection Entities (Field or Reservoir) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Value of Injected Fluid 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Production 
                 PE 01 
                 0.82 
                   
                   
                   
               
               
                 Entities 
                 PE 02 
                   
                 0.43 
                   
                   
               
               
                 (Field or 
                 PE 03 
                   
                   
                 0.05 
                   
               
               
                 Reservoir) 
                 PE 04 
                   
                   
                   
                 0.30 
               
               
                   
                 Sum 
                 0.82 
                 0.43 
                 0.05 
                 0.3 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are field/reservoir—the field/reservoir level pore volumes injected (PVI) for an example of four field/reservoir is shown below: 
     
       
         
           
               
               
            
               
                   
               
               
                   
                 Injection Entities (Field or Reservoir) 
               
            
           
           
               
               
               
               
               
            
               
                   
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
               
                 Pore Volume Injected in 
                 1.2 
                 0.7 
                 2.1 
                 1.8 
               
               
                 Analysis Period 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are field/reservoir—the field/reservoir level Injection Entity Index (IEI) for an example of four field/reservoir is calculated from IEI equation above and is shown below, in this example IEI is highest for field/reservoir 3: 
     
       
         
           
               
               
               
            
               
                   
               
               
                   
                 Injection Entity  
                 Injection Entities (Field or Reservoir) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Index (IEI) 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
               
                 Production 
                 PE 01 
                 0.11 
                   
                   
                   
               
               
                 Entities 
                 PE 02 
                   
                 0.33 
                   
                   
               
               
                 (Field or 
                 PE 03 
                   
                   
                 0.42 
                   
               
               
                 Reservoir) 
                 PE 04 
                   
                   
                   
                 0.23 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are field/reservoir—the field/reservoir level Production Entity Index (PEI) for an example of four field/reservoir is calculated from the estimates of recovery factors at one pore volume injected are shown below in this example the PEI is highest for field/reservoir 3: 
     
       
         
           
               
            
               
                   
               
               
                 Modified Koval Method 
               
               
                 Output 1 
               
            
           
           
               
               
               
               
            
               
                   
                   
                 Recovery Factor based on 
                 Production 
               
               
                   
                   
                 movable oil at 1 Pore Volume 
                 Entity Index 
               
               
                   
                   
                 Injected 
                 (PEI) 
               
               
                   
               
               
                 Production 
                 PE 01 
                 0.40 
                 0.60 
               
               
                 Entities 
                 PE 02 
                 0.32 
                 0.68 
               
               
                 (Well or 
                 PE 03 
                 0.15 
                 0.85 
               
               
                 Zone) 
                 PE 04 
                 0.20 
                 0.80 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are field/reservoir—the field/reservoir level Operational Index (OI) for an example of four field/reservoir is shown below, in this example the OI is one for all field/reservoir indicating availability of all candidates for conformance treatment: 
     
       
         
           
               
            
               
                   
               
               
                 Operational Condition/ 
               
               
                 Stability 
               
            
           
           
               
               
               
            
               
                   
                 Operational  
                 Injection Entities (Field or Reservoir) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Index (OI) 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
               
                 Production 
                 PE 01 
                 1 
                   
                   
                   
               
               
                 Entities 
                 PE 02 
                   
                 1 
                   
                   
               
               
                 (Field or 
                 PE 03 
                   
                   
                 1 
                   
               
               
                 Reservoir) 
                 PE 04 
                   
                   
                   
                 1 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are field/reservoir—the field/reservoir level Conformance Problem Index (CPI) for an example of four field/reservoir is calculated from CPI equation above and is shown below, in this example the CPI is highest for field/reservoir 3 indicating the field/reservoir with highest degree of conformance issue: 
     
       
         
           
               
               
               
            
               
                   
               
               
                   
                 Conformance  
                 Injection Entities (field) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Problem Index 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
               
                 Production 
                 PE 01 
                 0.07 
                   
                   
                   
               
               
                 Entities 
                 PE 02 
                   
                 0.22 
                   
                   
               
               
                 (Field or 
                 PE 03 
                   
                   
                 0.35 
                   
               
               
                 Reservoir) 
                 PE 04 
                   
                   
                   
                 0.19 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are well/zone—the well/zone level injection efficiency for an example of four well/zone is shown below: 
     
       
         
           
               
            
               
                   
               
               
                 CRM Output 1 
               
            
           
           
               
               
               
            
               
                   
                 Injection Efficiency  
                   
               
               
                   
                 between Injection and  
                 Injection Entities (Well or Zone) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Production Entities 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Production 
                 PE 01 
                 0.96 
                 0.47 
                 0.10 
                 0.18 
               
               
                 Entities 
                 PE 02 
                 0.01 
                 0.02 
                 0.02 
                 0.15 
               
               
                 (Well or 
                 PE 03 
                 0.00 
                 0.19 
                 0.02 
                 0.00 
               
               
                 Zone) 
                 PE 04 
                 0.03 
                 0.32 
                 0.86 
                 0.67 
               
               
                   
                 Sum 
                 1 
                 1 
                 1 
                 1 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are well/zone—the well/zone level value of injected fluid (VOIF) for an example of four well/zone is shown below: 
     
       
         
           
               
            
               
                   
               
               
                 CRM Output 2 
               
            
           
           
               
               
               
            
               
                   
                 Value of 
                 Injection Entities (Well or Zone) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Injected Fluid 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
               
                 Production 
                 PE 01 
                 0.30 
                 0.15 
                 0.03 
                 0.06 
               
               
                 Entities 
                 PE 02 
                 0.00 
                 0.01 
                 0.01 
                 0.06 
               
               
                 (Well or 
                 PE 03 
                 0.00 
                 0.03 
                 0.00 
                 0.00 
               
               
                 Zone) 
                 PE 04 
                 0.01 
                 0.06 
                 0.15 
                 0.12 
               
               
                   
                 Sum 
                 0.31 
                 0.24 
                 0.19 
                 0.23 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are well/zone—the well/zone level pore volumes injected (PVI) for an example of four well/zone is shown below: 
     
       
         
           
               
               
            
               
                   
               
               
                   
                 Injection Entities (Well or Zone) 
               
            
           
           
               
               
               
               
               
            
               
                   
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 Pore Volume Injected in  
                 1.00 
                 2.00 
                 1.20 
                 2.10 
               
               
                 Analysis Period 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are well/zone—the well/zone level Injection Entity Index (IEI) for an example of four well/zone is calculated from JET equation above and is shown below, in this example JET is highest for well/zone IE01: 
     
       
         
           
               
               
               
            
               
                   
               
               
                   
                 Injection Entity  
                 Injection Entities (Well or Zone) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Index (IEI) 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
               
                 Production 
                 PE 01 
                 0.67 
                 0.20 
                 0.08 
                 0.08 
               
               
                 Entities 
                 PE 02 
                 0.01 
                 0.01 
                 0.02 
                 0.07 
               
               
                 (Well or 
                 PE 03 
                 0.00 
                 0.09 
                 0.02 
                 0.00 
               
               
                 Zone) 
                 PE 04 
                 0.03 
                 0.15 
                 0.61 
                 0.28 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are well/zone—the well/zone level Production Entity Index (PEI) for an example of four well/zone is calculated from the estimates of recovery factors at one pore volume injected are shown below in this example the PEI is highest for well/zone PEW: 
     
       
         
           
               
            
               
                   
               
               
                 Modified Koval Method Output 1 
               
            
           
           
               
               
               
               
            
               
                   
                   
                 Recovery Factor based on 
                   
               
               
                   
                   
                 movable oil at 1 Pore 
                 Production Entity Index 
               
               
                   
                   
                 Volume Injected 
                 (PEI) 
               
               
                   
               
               
                 Production 
                 PE 01 
                 0.20 
                 0.80 
               
               
                 Entities 
                 PE 02 
                 0.40 
                 0.60 
               
               
                 (Well or 
                 PE 03 
                 0.56 
                 0.44 
               
               
                 Zone) 
                 PE 04 
                 0.37 
                 0.63 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are well/zone—the well/zone level Operational Index (OI) for an example of four well/zone is shown below, in this example the OI is one for all well/zone indicating availability of all candidates for conformance treatment: 
     
       
         
           
               
            
               
                   
               
               
                 Operational Condition/Stability 
               
            
           
           
               
               
               
            
               
                   
                 Operational  
                 Injection Entities (Well or Zone) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Index (OI) 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
               
               
                 Production 
                 PE 01 
                 1 
                 1 
                 1 
                 1 
               
               
                 Entities 
                 PE 02 
                 1 
                 1 
                 1 
                 1 
               
               
                 (Well or 
                 PE 03 
                 1 
                 1 
                 1 
                 1 
               
               
                 Zone) 
                 PE 04 
                 1 
                 1 
                 1 
                 1 
               
               
                   
               
            
           
         
       
     
     Application when injection and production entities are well/zone—the well/zone level Conformance Problem Index (CPI) for an example of four well/zone is calculated from CPI equation above and is shown below, in this example the CPI is highest for well/zone 3 indicating the well/zone with highest degree of conformance issue: 
     
       
         
           
               
               
               
               
            
               
                   
                   
               
               
                   
                   
                 Conformance 
                 Injection Entities (Well) 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                 Problem Index 
                 IE 01 
                 IE 02 
                 IE 03 
                 IE 04 
               
               
                   
                   
               
               
                   
                 Production 
                 PE 01 
                 0.53 
                 0.16 
                 0.06 
                 0.06 
               
               
                   
                 on Entities 
                 PE 02 
                 0.01 
                 0.00 
                 0.01 
                 0.04 
               
               
                   
                 (Well or 
                 PE 03 
                 0.00 
                 0.04 
                 0.01 
                 0.00 
               
               
                   
                 Zone) 
                 PE 04  
                 0.02 
                 0.09 
                 0.38 
                 0.18 
               
               
                   
                   
               
            
           
         
       
     
     After an entity (e.g., a well) is identified as a conformance candidate, a conformance control treatment may be determined.  FIGS. 19A-19B  illustrates one embodiment of a computer implemented method of determining a conformance control treatment for a well of a hydrocarbon reservoir, where the well is in fluidic communication with a plurality of zones of the hydrocarbon reservoir, referred to herein as method  1900 . The method  1900  may correspond to modeling optimum slug design/placement portion of the conformance control workflow of  FIG. 13 . The method  1900  can be executed by a computing system, such as the computing system  200  of  FIG. 2A . After executing the method  1900  of  FIG. 20 , the resulting output may be used as input to update or integrate with the CRM application  214 , polygon application  216 , the zonal application  218 , the petrotechnical mapping application  220 , the one or more other application  221 , the flooding analysis application  212 , and/or other item (e.g., as illustrated in FIG.). The method  1900  may be used to determine which zone or zones should be treated with a conformance agent, determine slug size, determine concentration of the conformance agent, and/or determine when to perform the conformance control treatment. The well may be identified as discussed herein in connection with  FIG. 18  or using some other process. A running example will be discussed that assumes a hydrocarbon reservoir with at least one injection well and five zones. The running example also assumes an injection rate of 2500 bbls/day, a pore volume of 100,000 bbls, and a threshold for residence time distribution of 10 days. 
     At  1902 , data for each zone of the plurality of zones of the hydrocarbon reservoir may be received. The data includes depth, porosity, permeability, and pore volume for each of the zones. In the running example, the following data is received for the zones. 
     
       
         
           
               
               
               
               
            
               
                   
               
               
                   
                 Input Data 
                   
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Depth 
                 Porosity 
                 Permeability 
                 Pore  
                   
               
               
                 Reservoir  
                 (ft) 
                 Fraction 
                 (md) 
                 volume 
                   
               
               
                 Zones 
                 485 
                 0 
                 0 
                 Bbls 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 1 
                 500 
                 0.3 
                 100 
                 26,316 
                 0.01 
               
               
                 2 
                 503 
                 0.3 
                 1000 
                 5,263 
                 0.1 
               
               
                 3 
                 535 
                 0.3 
                 10 
                 56,140 
                 0.001 
               
               
                 4 
                 540 
                 0.3 
                 500 
                 8,772 
                 0.05 
               
               
                 5 
                 542 
                 0.3 
                 5000 
                 3,509 
                 0.5 
               
               
                   
               
            
           
         
       
     
     At  1904 , a residence time distribution (or proxy thereof) may be determined for each zone. The residence time distribution may be determined using CRM zonal, F−C Plot, tracers, etc. The F−C Plot or flow capacity-Storage capacity is a static plot where both flow and storage capacity information are obtained from static log data, permeability, porosity, and thickness. In some embodiments, the F+C plot may be used. The F+C plot is a semi-dynamic plot—constructed similar to an F−C plot, but the flow information is from profile data and the storage capacity information is from porosity logs. In some embodiments, the F-Phi plot may be used. The F-Phi plot is a dynamic plot and both flow and storage information are from dynamic production data. Indeed, in some embodiments, determining the residence time distribution for each zone of the plurality of zones includes using tracer data, CRM zonal, a F−C plot, F+C plot, F-Phi plot, log data, ILT data, CRM generated PLT, slope data, or any combination thereof. In the running example, the following residence time distributions are determined for the zones. 
     
       
         
           
               
            
               
                   
               
               
                 Original residence time distribution before treatment 1 
               
            
           
           
               
               
               
            
               
                   
                 Residence time  
                 Residence time  
               
               
                   
                 distribution  
                 distribution  
               
               
                   
                 using CRM Zonal,  
                 using CRM Zonal,  
               
               
                 Reservoir  
                 F-C, Tracers, etc. 
                 F-C, Tracers, etc. 
               
               
                 Zones 
                 tD, Dimensionless 
                 t, days 
               
               
                   
               
            
           
           
               
               
               
            
               
                 5 
                 0.06 
                 2 
               
               
                 2 
                 0.30 
                 12 
               
               
                 4 
                 0.61 
                 24 
               
               
                 1 
                 3.04 
                 122 
               
               
                 3 
                 30.39 
                 1215 
               
               
                   
               
            
           
         
       
     
     At  1906 , at least one zone of the plurality of zones may be identified to be treated with a conformance agent based on a comparison of the determined residence time distribution of each zone to a residence time distribution threshold. In the running example, the threshold for residence time distribution is 10 days and any zone with residence time distribution of less than or equal to the threshold may be identified for conformance control, which would likely shut off that zone. The threshold may be received from a user or automatically generated. In the running example, zone 2 has a residence time distribution of 2 days, which is lower than the threshold of 10 days, thus, zone 2 is identified for treatment with a conformance agent (treatment 1). A residence time distribution of 2 days indicates that the displacing fluid, such as water or brine from a waterflood operation, breaks through in about 2 days which is before the threshold of 10 days. 
     At  1908 , breakthrough time of slowest identified zone may be determined. The breakthrough time may be determined from the residence time distributions determined at  1904  for the corresponding identified zones. In the running example, only zone2 was identified and the residence time distribution is 2 days for zone 2 so the breakthrough time is determined to be 2 days. If another zone had been identified, then the slowest residence time distribution from the identified may be selected, which will be described further in connection with treatment2. 
     At  1910 , a first conformance control treatment may be recommended at the determined breakthrough time of the slowest identified zone. In the running example, a first conformance control treatment (treatment1) may be recommended at 2 days for zone 2, as illustrated below. 
     
       
         
           
               
               
               
             
               
                   
               
               
                   
                 Treatment 1 
                   
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 Time of conformance treatment is equal to  
                 2 
                 days 
               
               
                 breakthrough of the slowest of the zones to  
                   
                   
               
               
                 be shut off (days) 
                   
                   
               
               
                 Slug Size (bbls) is half of the injected volume  
                 3039 
                 bbls 
               
               
                 before breakthrough of the fastest zones 
                   
                   
               
               
                 Resistance factor to be used to reduce its  
                 17 
                   
               
               
                 velocity to 1/10th of its original velocity 
                   
                   
               
               
                 Concentration of the conformance agent to  
                 20776 
                 ppm 
               
               
                 be used based on the rheology 
                   
                   
               
               
                 Sweep Efficiency before treatment @ 1 PV  
                 0.25 
                   
               
               
                 injected 
                   
                   
               
               
                 Sweep efficiency after treatment 1 @ 1 PV  
                 0.5 
                   
               
               
                 injected 
               
               
                   
               
            
           
         
       
     
     At  1912 , a recommend slug size for the first conformance control treatment may be determined. The slug size is determined by calculating injected volume for a period of time which is not more than 50% of a breakthrough time for the fastest identified zone. In the running example, a first conformance treatment slug size (treatment 1) is recommended to be 3039 Bbls. 
     At  1914 , a recommended concentration of the conformance agent for treatment 1 may be determined by calculating a resistance factor of the conformance agent sufficient to reduce the original velocity of the fastest identified zone by a factor of about 10. The conformance agent rheology may be used to determine the concentration after the resistance factor is calculated. In the running example, the resistance factor for the first conformance agent (treatment 1) is recommended to be 17. From the rheology for the conformance agent, the recommended conformance agent concentration is determined to be 20776 ppm so that a resistance factor of 17 is achieved. 
     At  1916 , data for each zone of the plurality of zones of the hydrocarbon reservoir may be updated after treatment 1. At least one of porosity, permeability, pore volume, or any combination thereof for each of the plurality of zones of the hydrocarbon reservoir may be updated after treatment 1. In the running example, the following data is updated for the zones. 
     
       
         
           
               
               
               
               
               
               
             
               
                   
               
               
                   
                 Input Data 
                   
                   
                   
                   
               
               
                   
                 Depth 
                 Porosity 
                 Permeability 
                 Pore  
                   
               
               
                 Reservoir  
                 (ft) 
                 Fraction 
                 (md) 
                 volume 
                   
               
               
                 Zones 
                 485 
                 0 
                 0 
                 Bbls 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 1 
                 500 
                 0.3 
                 86 
                 26,316 
                 0.08 
               
               
                 2 
                 503 
                 0.3 
                 385 
                 5,263 
                 0.35 
               
               
                 3 
                 535 
                 0.3 
                 10 
                 56,140 
                 0.01 
               
               
                 4 
                 540 
                 0.3 
                 278 
                 8,772 
                 0.25 
               
               
                 5 
                 542 
                 0.3 
                 556 
                 3,509 
                 0.50 
               
               
                   
               
            
           
         
       
     
     At  1918 , a residence time distribution (or proxy thereof) may be determined for each zone after the first treatment. The new residence time distribution may be determined using updated CRM zonal, F−C Plot, tracers, Modified Koval Method, etc. The F−C Plot or flow capacity-Storage capacity is a static plot where both flow and storage capacity information are obtained from static log data, permeability, porosity, and thickness. In some embodiments, the F+C plot may be used. The F+C plot is a semi-dynamic plot—constructed similar to an F−C plot, but the flow information is from profile data and the storage capacity information is from porosity logs. In some embodiments, the F-Phi plot may be used. The F-Phi plot is a dynamic plot and both flow and storage information are from dynamic production data, for example, tracers or Modified Koval Method. Indeed, in some embodiments, determining the new residence time distribution for each zone of the plurality of zones includes using updated tracer data, CRM zonal, Modified Koval Method, an F−C plot, F+C plot, F-Phi plot, log data, ILT data, CRM generated PLT, or any combination thereof. In the running example, the following new residence time distributions are determined for the zones after the first treatment. 
     
       
         
           
               
            
               
                   
               
               
                 Original residence time distribution after treatment 1 or  
               
               
                 before treatment 2 
               
            
           
           
               
               
               
            
               
                   
                   
                 Residence time  
               
               
                   
                 Residence time  
                 distribution using  
               
               
                   
                 distribution  
                 CRM Zonal, F-C,  
               
               
                   
                 using CRM Zonal,  
                 Tracers or Modified  
               
               
                 Reservoir  
                 F-C, Tracers, etc. 
                 Koval Method etc. 
               
               
                 Zones 
                 tD, Dimensionless 
                 t, days 
               
               
                   
               
            
           
           
               
               
               
            
               
                 5 
                 0.17 
                 7 
               
               
                 2 
                 0.24 
                 10 
               
               
                 4 
                 0.33 
                 13 
               
               
                 1 
                 1.07 
                 43 
               
               
                 3 
                 9.38 
                 375 
               
               
                   
               
            
           
         
       
     
     At  1920 , at least one zone of the plurality of zones may be identified to be treated with a conformance agent (second treatment) by comparing the determined new residence time distributions (after first treatment) to a residence time distribution threshold. In the running example, the threshold for residence time distribution for treatment 2 is still 10 days and any zone with residence time distribution of less than or equal to the threshold may be identified for conformance control for second treatment, which would likely shut off that zone. The threshold may be received from a user or automatically generated. The threshold for this second conformance control treatment analysis may be the same or different than the threshold used for the first conformance control treatment analysis. In the running example, zones 5 and 2 have residence times of 7 and 10 days, respectively, after the first treatment which is lower than or equal to the threshold of 10 days. Thus, zones 5 and 2 are identified for treatment with a conformance agent (treatment 2). A residence time of 7 and 10 days indicates that the displacing fluid, such as water or brine from a waterflood operation, breaks through in about 10 days in the slowest of the identified layers (zone 2) which is equal to the threshold of 10 days. 
     At  1922 , breakthrough time of slowest identified zone may be determined after first treatment. The breakthrough time may be determined from the residence time distributions determined at  1918  for the corresponding identified zones after treatment 1. In the running example, zones 5 and 2 were identified and the residence time distribution is 7 days for zone 5 and 10 days for zone 2 so the breakthrough time of slowest identified zone is determined to be 10 days. 
     At  1924 , a second conformance control treatment may be recommended at the determined breakthrough time for the slowest zone that is identified after the first treatment. In the running example, a second conformance control treatment (treatment 2) may be recommended at 10 days for zones 5 and 2 after the first conformance treatment, as illustrated below. 
     
       
         
           
               
               
               
             
               
                   
               
               
                 Treatment 2 
                   
                   
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 Time of conformance treatment is equal to  
                 10 
                 days 
               
               
                 breakthrough of the slowest of the zones to  
                   
                   
               
               
                 be shut off (days) 
                   
                   
               
               
                 Slug Size (bbls) is half of the injected volume  
                 8308 
                 bbls 
               
               
                 before breakthrough of the fastest zone 
                   
                   
               
               
                 Resistance factor to be used to reduce its  
                 17 
                   
               
               
                 velocity to 1/10th of its original velocity 
                   
                   
               
               
                 Concentration of the conformance agent to  
                 20776 
                 ppm 
               
               
                 be used based on the rheology 
                   
                   
               
               
                 Sweep efficiency after treatment 2 @ 1 PV  
                 0.6 
                   
               
               
                 injected 
               
               
                   
               
            
           
         
       
     
     At  1926 , a recommended slug size for the second conformance control treatment may be determined. The slug size for the second conformance control treatment is determined by calculating injected volume for a period of time which is not more than 50% of an updated breakthrough time for the fastest identified zone. In the running example, the fastest of the zones identified is zone 5 and a second conformance treatment slug size (treatment 2) is recommended to be 8308 Bbls. 
     At  1928 , a recommended concentration of the conformance agent for treatment 2 may be determined. The concentration of the conformance agent for the second conformance control treatment is based on a resistance factor of the conformance agent for the second conformance control treatment and a rheology of the conformance agent for the second conformance control treatment. The resistance factor of the conformance agent of the second conformance control treatment is sufficient to reduce updated velocity of the fastest identified zone after the first conformance control treatment by a factor of about 10. The conformance agent rheology may be used to determine the concentration after the sufficient resistance factor is calculated. In the running example, the resistance factor for the second conformance agent (treatment 2) is recommended to be 17. From the rheology for the conformance agent, the recommended conformance agent concentration is determined to be 20776 ppm so that a resistance factor of 17 is achieved. 
     Those of ordinary skill in the art may appreciate that these embodiments may lead to an increase in accuracy and an increase in repeatability in the area of conformance control. For example, a user may be given a recommendation as to the zone, slug size, concentration, and/or time period for the conformance control treatment, and therefore, decisions may be based on data and not ad-hoc. 
     Comparing Different Flood Operations 
     Different flood operations are oftentimes performed on a hydrocarbon reservoir. For example, a flood operation with polymer, also referred to as polymer flooding, is an enhanced oil recovery technique. In a polymer flooding operation, certain polymers and/or surfactants may be dissolved in the displacing fluid (e.g., prior to injecting the injection fluid) to decrease the displacing fluid&#39;s mobility and increase the displacing fluid&#39;s viscosity. By doing so, the displacing fluid flows through the hydrocarbon reservoir in a slower manner in a polymer flood operation and increases the likelihood that a larger volume of the hydrocarbon reservoir will be contacted as compared to a waterflood operation. Analysis of multiple flood operations may be useful in evaluating project feasibility and for other purposes. 
       FIG. 20  illustrates one embodiment of a computer implemented method of analyzing at least a first flood operation and a second flood operation on a hydrocarbon reservoir having at least one production well and at least one injection well, referred to herein as method  2000 . The method  200  can be executed by a computing system, such as the computing system  200  of  FIG. 2A  or the CRM application  214  thereof. After executing the method  2000  of  FIG. 20 , the resulting change in sweep efficiency between the first flood operation (e.g., a waterflood operation) and the second flood operation (e.g., a polymer flood operation) may be used as input to update or integrate with the polygon application  216 , the zonal application  218 , the petrotechnical mapping application  220 , the one or more other application  221 , the flooding analysis application  212 , and/or other item (e.g., as illustrated in  FIGS. 2A, 2C ). The change in sweep efficiency between the first flood operation (e.g., the waterflood operation) and the second flood operation (e.g., the polymer flood operation) may also be used to make physical adjustments, such as physical adjustments to the polymer flood operation like changing the polymer concentration, changing the injection rate, and other physical adjustments. 
     Starting with the first flood operation, at  2002 , production data may be received for at least one production well and injection data for at least one injection well for the first flood operation. A running example will be discussed that assumes a hydrocarbon reservoir with a first injection well I 1  and six production wells P 1 , P 2 , P 3 , P 4 , P 5 , P 6 , a first flood operation (e.g., a waterflood operation) was performed on the hydrocarbon reservoir between years 1980-1990, and a second flood operation (e.g., a polymer flood operation) was performed on the hydrocarbon reservoir between years 1990-1995. Production data such as production rate data may be received for the six production wells P 1 , P 2 , P 3 , P 4 , P 5 , P 6  from the first flood operation, for example, production rate data for the entire or a portion of the first flood operation. Injection data such as injection rate data may be received for the first injection well I 1  from the first flood operation, for example, injection rate data for the entire or a portion of the first flood operation. The production data may include production rate and flowing pressure data as a function of time for production wells, and the injection data may include injection rate and flowing pressure data as a function of time for injection wells. 
     At  2004 , CRM may be run using the received production data and the received injection data for the first flood operation to generate a response time and an interwell connectivity per injection well and production well pair for the first flood operation. For example, the response time injection well and production well pair may be generated by CRM using the following equation: 
               τ   ij     =         c   t     ⁢     V   pij         J   ij             
In the equation above, V pij  represents pore volume of a particular injection well and production well pair,  i  represents an injection well,  j  represents an production well, c t  represents compressibility as function of time, J ij  represents the productivity index for a particular injection well and production well pair, and Tau or τ ij  represents the response time of a particular injection well and production well pair.
 
     Also, the interwell connectivity per injection well and production well pair for the first flood operation may be generated by CRM using the following equation: 
               f   ij     =       q   ij       I   i             
In the equation above, q ij  represents the total flow rate for a particular injection well and production well pair, I i  represents the total flow rate for a particular injection well,  i  represents an injection well,  j  represents an production well, and f ij  represents the interwell connectivity between a particular injection well and production well pair.
 
     The productivity index J ij  between a particular injection well and production well pair may be calculated as follows: 
               J   ij     =         I   i     ⁢     f   ij         Δ   ⁢           ⁢     P   ij               
In the equation above, ΔP ij  represents the pressure difference between a particular injection well and a production well and the other terms are discussed hereinabove.
 
     CRM may be run with or without allowed well connections as described herein. CRM may generate the response times and interwell connectivities for the first injection well I 1  and the six production wells drilled into the hydrocarbon reservoir. Alternatively, if this example included hundreds of wells in addition to the first injection well I 1  and the six production wells drilled into the reservoir, CRM may generate response times and interwell connectivities for the first flood operation for all injection well and production well pairs and the generated response times and interwell connectivities for the well pairs of interest may be used at  2006 . 
     The following table A illustrates the generated response times in the second column, steady state interwell connectivities in the third column for an infinite period of time (fij), and transient interwell connectivities in the fifth column for a specified period of time (f*ij) for Inj 1  and P 1 , P 2 , P 3 , P 4 , P 5 , P 6  for the first flood operation: 
     
       
         
           
               
             
               
                 TABLE A 
               
             
            
               
                   
               
               
                 Injection well Inj 1  and production wells P 1 -P 6 -first flood operation 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 Equation 
               
               
                   
                   
                   
                   
                   
                 results with 
               
               
                   
                   
                   
                 Equation results 
                   
                 transient f*ij 
               
               
                   
                 Response 
                   
                 with steady 
                   
                 Swept Pore 
               
               
                   
                 time (τ) 
                   
                 state fij 
                 f*ij - interwell 
                 Volume 
               
               
                   
                 Response 
                 fij - interwell  
                 Swept Pore 
                 connectivity 
                 Proxy (max 
               
               
                   
                 time 
                 connectivity 
                 Volume Proxy 
                 (max duration 
                 duration of 
               
               
                 Producers 
                 Proxy 
                 (steady state) 
                 (steady state) 
                 of the flood) 
                 the flood) 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 P 1   
                 10 
                 0.30 
                 153,846 
                 0.30 
                 153,846 
               
               
                 P 2   
                 10 
                 0.20 
                 106,667 
                 0.20 
                 106,667 
               
               
                 P 3   
                 10 
                 0.20 
                 95,238 
                 0.20 
                 95,238 
               
               
                 P 4   
                 50 
                 0.10 
                 246,914 
                 0.09 
                 233,053 
               
               
                 P 5   
                 20 
                 0.10 
                 111,111 
                 0.10 
                 111,028 
               
               
                 P 6   
                 80 
                 0.10 
                 410,256 
                 0.08 
                 342,441 
               
               
                   
                 180 
                 Total swept 
                 1,124,032 
                   
                 1,042,274 
               
               
                   
                   
                 Pore volume 
                   
                   
                   
               
               
                   
                   
                 For Inj 1   
                   
                   
                   
               
               
                   
                   
                 Lorenz 
                 .44 
                   
                   
               
               
                   
                   
                 Coefficient 
                   
                   
                   
               
               
                   
                   
                 For first flood 
                   
                   
                   
               
               
                   
                   
                 operation 
               
               
                   
               
            
           
         
       
     
     At  2006 , the generated response times, the generated interwell connectivities, the received production data, the received injection data, or any combination thereof may be used to generate a proxy of pore volume swept per injection well and production well pair for the first flood operation. Disclosed herein are three methodologies to generate the proxy of pore volume swept per injection well and production well pair for the first flood operation. Each of the three methodologies will be discussed, but those of ordinary skill in the art will appreciate that all three methodologies are not necessary. For example, the second methodology only may be used throughout. As another example, the third methodology only may be used throughout. 
     The first methodology to generate a proxy of pore volume swept for a particular injection well and production well pair is to treat the generated response time for the particular pair as the proxy of pore volume swept. Thus, using the generated response time, the proxy for Inj 1  and P 1  is 10, the proxy for Inj 1  and P 2  is 10, the proxy for Inj 1  and P 3  is 10, the proxy for Inj 1  and P 4  is 50, the proxy for Inj 1  and P 5  is 20, and the proxy for Inj 1  and P 6  is 80. 
     The second methodology to generate a proxy of pore volume swept for a particular injection well and production well pair is to use the following equation with steady state interwell connectivities: 
               V   pij     =         τ   ij     ⁢     f   ij     ⁢     I   i           c   t     ⁢   Δ   ⁢           ⁢     P   ij               
In the equation above, V pij  represents pore volume of a particular injection well and production well pair, c t  represents compressibility as a function of time, f ij  represents the interwell connectivity for an injection well and production well pair, Tau or τ ij  represents the response time of a particular injection well and production well pair, ΔP ij  represents the pressure difference between a particular injection well and a production well, and I i  represents the total flow rate for a particular injection well. The above equation for V pij  is a combination of the J ij  and τ ij  equations. Thus, using the equation with steady state interwell connectivity, the proxy for Inj 1  and P 1  is 153,846, the proxy for Inj 1  and P 2  is 106,667, the proxy for Inj 1  and P 3  is 95,238, the proxy for Inj 1  and P 4  is 246,914, the proxy for Inj 1  and P 5  is 111,111, and the proxy for Inj 1  and P 6  is 410,256.
 
     The third methodology to generate a proxy of pore volume swept for a particular injection well and production well pair is to use the following equation with transient interwell connectivities: 
               V   pij     =         τ   ij     ⁢     f   ij   *     ⁢     I   i           c   t     ⁢   Δ   ⁢           ⁢     P   ij               
In the equation above, the main difference is the use of f* ij  to represent a transient interwell connectivity for an injection well and production well pair. For the running example, 36 months was used for the transient interwell connectivities. Thus, using the equation with transient interwell connectivity, the proxy for Inj 1  and P 1  is 153,846, the proxy for Inj 1  and P 2  is 106,667, the proxy for Inj 1  and P 3  is 95,238, the proxy for Inj 1  and P 4  is 233,053, the proxy for Inj 1  and P 5  is 111,028, and the proxy for Inj 1  and P 6  is 342,441.
 
     At  2008 , the generated proxies of pore volume swept per injection well and production well pair for the first flood operation may be aggregated to generate an estimate of pore volume swept at a well level, at a reservoir level, or both for the first flood operation. The generated proxies may be aggregated using addition. The estimate of pore volume swept at the well level may be for a particular injection well or for a particular production well. Returning to the running example, the estimate of pore volume swept at the well level for Inj 1  based on the generated response time is 180. The estimate of pore volume swept at the well level for Inj 1  based on the equation with steady state interwell conductivities is 1,124,032. The estimate of pore volume swept at the well level for Inj 1  based on the equation with transient interwell conductivities is 1,042,274. 
     At  2010 , heterogeneity at the well level, the reservoir level, or both may be determined for the first flood operation (e.g., by using the generated response times and the generated interwell connectivities). The heterogeneity may be determined to verify the accuracy of a change in sweep efficiency, discussed further at  2026 . Heterogeneity of the first flood operation may be calculating using a Lorenz coefficient (Lc), Koval, a flow capacity-storage capacity curve, any combination thereof, etc. In some embodiments, the heterogeneity may be determined by quantifying the area below the flow capacity-storage capacity curve for the first flood operation. Heterogeneity is discussed further in U.S. Pat. No. 8,428,924, which is incorporated herein by reference in its entirety. In the running example, the heterogeneity at the well level for the first flood operation is 0.44182, as illustrated in  FIG. 21A . 
     At  2012 ,  2014 ,  2016 ,  2018 ,  2020 , a similar approach may be used for the second flood operation, as in Table B. Returning to the running example, the estimate of pore volume swept at the well level for the second flood operation based on the generated response time for Inj 1  is 275. The estimate of pore volume swept at the well level for the second flood operation based on the equation with steady state interwell conductivities for Inj 1  is 1,199,319. The estimate of pore volume swept at the well level for the second flood operation based on the equation with transient interwell conductivities for Inj 1  is 1,135,528. The heterogeneity at the well level for the second flood operation is 0.1182, as illustrated in  FIG. 21B . 
     
       
         
           
               
             
               
                 TABLE B 
               
             
            
               
                   
               
               
                 Injection well Inj 1  and production wells P 1 -P 6 -second flood operation 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 Equation 
               
               
                   
                   
                   
                   
                   
                 results 
               
               
                   
                   
                   
                 Equation 
                   
                 with f*ij 
               
               
                   
                   
                   
                 results with 
                   
                 Swept Pore 
               
               
                   
                 Response 
                   
                 fij 
                 f*ij - interwell 
                 Volume 
               
               
                   
                 time (τ) 
                 fij - interwell  
                 Swept Pore 
                 connectivity 
                 Proxy (max 
               
               
                   
                 Response 
                 connectivity 
                 VolumeProxy 
                 (max duration 
                 duration of 
               
               
                 Producers 
                 time Proxy 
                 (steady state) 
                 (steady state) 
                 of the flood) 
                 the flood) 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 P 1   
                 40 
                 0.20 
                 208,333 
                 0.19 
                 202,641 
               
               
                 P 2   
                 50 
                 0.10 
                 134,409 
                 0.09 
                 126,864 
               
               
                 P 3   
                 50 
                 0.10 
                 122,549 
                 0.09 
                 115,670 
               
               
                 P 4   
                 35 
                 0.22 
                 194,444 
                 0.22 
                 191,268 
               
               
                 P 5   
                 40 
                 0.12 
                 133,333 
                 0.12 
                 129,690 
               
               
                 P 6   
                 60 
                 0.26 
                 406,250 
                 0.24 
                 369,396 
               
               
                   
                 275 
                 Total swept 
                 1,199,319 
                   
                 1,135,528 
               
               
                   
                   
                 Pore volume 
                   
                   
                   
               
               
                   
                   
                 For Inj 1   
                   
                   
                   
               
               
                   
                   
                 Lorenz 
                 .12 
                   
                   
               
               
                   
                   
                 Coefficient 
                   
                   
                   
               
               
                   
                   
                 for second flood 
                   
                   
                   
               
               
                   
                   
                 operation 
                   
                   
                   
               
               
                   
                   
                 Positive  
                  7% 
                 Positive change 
                 9% 
               
               
                   
                   
                 change in  
                   
                 in volumetric 
                   
               
               
                   
                   
                 volumetric 
                   
                 sweep (max 
                   
               
               
                   
                   
                 sweep 
                   
                 duration of the 
                   
               
               
                   
                   
                 (steady state) 
                   
                 flood) 
                   
               
               
                   
                   
                 Lorenz 
                 73% 
                   
                   
               
               
                   
                   
                 coefficient 
                   
                   
                   
               
               
                   
                   
                 reduction 
               
               
                   
               
            
           
         
       
     
     At  2022 , the generated estimate of pore volume swept for the first flood operation and the generated estimate of pore volume swept for the second flood operation may be compared to determine a change in sweep efficiency at the well level, at the reservoir level, or both. The change in sweep efficiency may be determined by calculating the difference between the estimate of pore volume swept for the second flood operation and the estimate of pore volume swept for the first flood operation. The determined difference is divided by the estimate of pore volume swept for the first flood operation. Returning to the running example, if the first methodology was used, then the change in sweep efficiency for Inj 1  is about 53% (i.e., (275−180)/180=0.5277*100=53%), which is a positive change in volumetric sweep. If the second methodology was used, then the change in sweep efficiency for Inj 1  is about 7% (i.e., (1,199,319−1,124,032)/1,124,032=0.0669*100=7%), which is a positive change in volumetric sweep (steady state). If the third methodology was used, then the change in sweep efficiency for Inj 1  is about 9% (i.e., (1,135,528−1,042,274)/1,042,274=0.0894*100=9%), which is a positive change in volumetric sweep (max duration of the flood). 
     At  2024 , the determined heterogeneity of the first flood operation and the determined heterogeneity of the second flood operation may be compared to determine a change in heterogeneity at the well level, at the reservoir level, or both. The comparison at  2022  may be performed before the comparison at  2024  or the comparison at  2024  may be performed before the comparison at  2022 . Nonetheless, the change in heterogeneity may be determined by calculating the difference between the determined heterogeneity for the second flood operation and the determined heterogeneity for the first flood operation. The determined difference is divided by the determined heterogeneity for the first flood operation. Returning to the running example, at the well level, the heterogeneity for Inj 1  is about 73% (i.e., (0.44−0.12)/0.44=0.7272*100=73%) as illustrated in  FIG. 21C , which is a Lorenz coefficient reduction. 
     At  2026 , the determined change in heterogeneity and the determined change in sweep efficiency may be compared to verify accuracy of the determined change in sweep efficiency. In the running example, an inverse relationship may indicate that the determined change in sweep efficiency, regardless of the methodology used, is likely accurate. For instance, a positive change in volumetric sweep is consistent with a reduction in heterogeneity, and therefore a positive increase in 53%, 7%, or 9% is consistent with a reduction of 73% in heterogeneity. 
     Those of ordinary skill in the art will appreciate that various modifications may be made to the illustrated embodiments. For example, CRM may be run at  2004  and/or  2014  using allowed well connections as input, as discussed herein. The allowed well connections may be determined, and the running of the capacitance resistance modeling for the first flood operation, the second flood operation, or both includes using the determined allowed connections as an input to the capacitance resistance modeling. 
     Moreover, the first flood operation may be practically any flood operation and the second flood operation may be practically any flood operation, and the embodiments disclosed herein may be used to analyze them. For example, the first flood operation may be a polymer flood operation using a polymer at a first concentration, and the second flood operation may be a polymer flood operation using a polymer at a second concentration. Alternatively, the first flood operation may be a waterflood operation like in the running example, but the second flood operation may be a polymer flood operation with a surfactant and a polymer. Alternatively, the first flood operation may be a waterflood operation that uses a first brine and the second flood operation may be a waterflood operation that uses a second brine. The first brine and the second brine may be different, or the first brine and the second brine may be substantially similar in some embodiments. 
     Furthermore, the running example assumed one injection well for simplicity, but a change in sweep efficiency may be generated as described above for a plurality of injection wells. Indeed, the running example may include two injections wells, as illustrated further in  FIGS. 22A-22B . Also, the embodiments discussed above can be applied at the reservoir level, as illustrated in  FIGS. 23A-23G . Furthermore, the reservoir may include a plurality of zones, and each zone may be treated as an injection well, as illustrated in  FIGS. 24A-24F . 
     Optionally, if a flood operation uses a polymer alone or a polymer in combination with other material (e.g., surfactant), then the rheology of the polymer may be accounted for in CRM. In the running example, the second flood operation is a polymer flood operation, and therefore, the rheology of the polymer could have been accounted for in the running of CRM at  2014 . Polymers used in oil fields oftentimes exhibit shear thinning rheology. Shear thinning rheology for a polymer oftentimes causes its viscosity to increase away from the well bore area. The viscosity variation may be sharp between near well bore areas to far well bore areas, as illustrated in  FIG. 25 . The polymer rheology can be mathematically described by at least one rheological model such as a power law model, Meters model, Carreau model, etc. based on laboratory experiments. 
     To account for the rheology in running CRM, accounting for the rheology includes separating each injection well and production well pair of the polymer flood operation into three tanks including a near injection well tank, a near production well tank, and a middle tank between the near injection well tank and the near production well tan, as illustrated in  FIG. 26 . By separating into at least three tanks, the rheology near well bore is separated from the rheology effects away from the well bore. 
     Furthermore, running the capacitance resistance modeling includes using (i) material balance equations for each of the tanks, (ii) injection and production well rates, (iii) injection and production well flowing pressure data, and (iv) the polymer rheology (e.g., using the power law model). The material balance equations are written out for each of the tanks separately as illustrated below. The near injection well tank material balance equation is: 
                 c   t     ⁢     V     p   ,   i       ⁢       ∂       P   _     i         ∂   t         =         i     i   ,   p       ⁡     (   t   )       -       q   i     ⁡     (   t   )               
In the near injection well tank equation, c t  is the total compressibility, V p,i  is the pore volume of the near injection well tank associated with injection well i,  P   i  is the average pressure in the near injection well tank associated with injection well i, i i,p (t) is the flow rate of the polymer in injection well i and q i (t) is flow rate of the fluid out of the near injection well tank associated with the injection well i.
 
     The near production well tank material balance equation is: 
                 c   t     ⁢     V     p   ,   j       ⁢       ∂       P   _     j         ∂   t       ⁢         q   _     j     ⁡     (   t   )         -     (         q     p   ,   j       ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )           
In the near production well tank equation, c t  is the total compressibility, V p,j  is the pore volume of the near production well tank associated with production well j,  P   j  is the average pressure in the near production well tank associated with production well j,  q   j (t) is the flow rate of the fluid in production well j, q p,j (t) is the polymer flow rate in production well j and q o,j (t) is the oil flow rate in production well j.
 
     The middle tank material balance equation is: 
                 c   t     ⁢       V   _       p   ,   j       ⁢       ∂         P   _     j     _         ∂   t         =         ∑     i   =   1     Ni     ⁢           ⁢       q     i   ,   j       ⁡     (   t   )         -         q   _     j     ⁡     (   t   )               
In the middle tank equation, c t  is the total compressibility, q i,j (t) is the interwell total flow rate between injection well i and production well j,  P j    is the average pressure in the middle tank,  V   p,j  is the pore volume of the middle tank.
 
     The CRM polymer formulation (a single equation, shown below) is obtained by the combination of the material balance equations for each of the tanks. The final CRM formulation thus depends (i) material balance equations for each of the tanks, (ii) injection and production well rates, (iii) injection and production well flowing pressure data, and (iv) the polymer rheology (e.g., using the power law model). The CRM polymer formulation is: 
                     τ   _     j     ⁢     τ   j     ⁢     ∂     ∂   t       ⁢     (         ∂     (         q     p   ,   j     n     ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )         ∂   t       +       J   j     ⁢       ∂     P     wf   ,   j           ∂   t           )       +         τ   _     j     ⁢     ∂     ∂   t       ⁢     (         q     p   ,   j       ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )       +       (           τ   _     j     ⁢         J   _     j       J   j         +     τ   j       )     ⁢     (         ∂     (         q     p   ,   j     n     ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )         ∂   t       +       J   j     ⁢       ∂     P     wf   ,   j           ∂   t           )       +     (         q     p   ,   j       ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )       =       ∑     i   =   1     Ni     ⁢           ⁢       f     i   ,   j       ⁡     (         i     i   ,   p       ⁡     (   t   )       +       τ   i     ⁡     (         ∂       i     i   ,   p     n     ⁡     (   t   )           ∂   t       -       J   i     ⁢       ∂     P     wf   ,   i           ∂   t           )         )               
In the CRM polymer formulation, τ i  is the time constant for the near injection well tank associated with injection well i, τ j  is the time constant for the near production well tank associated with production well j, and  τ   j  is the time constant for the middle tank. J i  is the injectivity index for injection well i, J j  is the productivity index for production well j and  J   j  is the productivity index for the middle tank. P wf,j  is the well flowing pressure for production well j and P wf,i  is the well flowing pressure for injection well i.
 
     The output from CRM using the CRM polymer formulation above includes τ for each of the tanks and the interwell connectivities fij or f*ij for each injection well and production well pair, and CRM therefore also accounts for the rheology of the polymer of the flood operation. Those of ordinary skill in the art will appreciate that existing CRM cannot account for rheology of polymer, and therefore the embodiments disclosed herein may lead to more accurate CRM output for flood operations involving a polymer. In some embodiments, rheology of a polymer may be accounted for in running CRM separate from determining sweep efficiency and/or separate from determining heterogeneity, as illustrated in an embodiment of a method of analyzing a polymer flood operation on a hydrocarbon reservoir in  FIG. 27  (e.g., at method  2700 , numbers  2702 - 2710 ). 
     Those of ordinary skill in the art may appreciate that analysis of a first flood operation and a second flood operation may have various benefits. For example, the injection and production field data of the polymer flood may be used via CRM to characterize the sweep efficiency improvement and performance of the polymer flood on the fly. This development may help with the following: 1. Optimizing a polymer flood while in progress by making changes such as adjusting injection-production rates or bottom hole pressures of wells to re-direct flow to un-swept volumes and improve performance of the polymer flood operation, 2. Design/optimize operational parameters of a polymer floods based on waterflood operation performance (e.g., choose optimal polymer concentrations, optimize injection-production rates and pressures, etc.), 3. Analyze the performance of a past polymer flood (pilot) to determine potential of future implementations, 4. Analyze a polymer flood pilot and use the incremental sweep efficiency information obtained to design future polymer floods, and/or 5. Determine infill drilling opportunities for the undergoing polymer flood to increase hydrocarbon recovery. Manipulation of CRM fundamental equations allows calculation of improved sweep efficiency by polymer floods from CRM analysis of polymer floods and comparison to the corresponding waterflood CRM performance. Time scale of connection or response time (tau) and well pair connectivities (fij) are used to calculate incremental sweep efficiency of polymer flood. As discussed above, this effort is not limited to waterfloods and polymer floods, and for example, may be applied to surfactant-polymer flood operations. This capability may allow robust design and optimization of chemical enhanced oil recovery (CEOR) processes based on field data. It may also help improve the performance of floods on the fly, which can change the outcome of a project from a failure to a success, potentially saving millions of dollars. 
     Pattern Management 
     Oftentimes, injection wells and production wells are drilled in locations so as to form patterns. Polygons are sometimes generated to represent the patterns. The embodiments discussed herein relate to (i) using producer centered (e.g., Voronoi) polygons to help identify infill drilling locations, (ii) using the polygons in choosing between two infill candidates in two different reservoirs, (iii) using the polygons (e.g., streamgrids) for converting an existing production well into an injection well or vice versa (referred to as pattern realignment, (iv) using polygon (e.g., streamgrid) allocation factors to initiate CRM interwell connectivity, and/or (v) estimating maximal areal sweep by zone and by reservoir with the populated polygons (e.g., streamgrids). The embodiments can be executed by a computing system, such as the computing system  200  of  FIG. 2A  or the polygon application  216  thereof. As discussed herein, a polygon may be a streamgrid or streamgrid polygon in some embodiments. After executing the embodiments, the output may be used as input to update or integrate with the CRM application  214 , the zonal application  218 , the petrotechnical mapping application  220 , the one or more other application  221 , the flooding analysis application  212 , and/or other item (e.g., as illustrated in FIG.). 
       FIG. 28A  illustrates one embodiment of a method for using producer centered (e.g., Voronoi) polygons to help identify infill drilling locations, referred to herein as method  2800 . The method  2800  includes (i) loading or receiving well locations, reservoir boundary, and injection and production rate histories, (ii) creating producer-centered (e.g., Voronoi) polygons based on the producer locations and the reservoir boundary (or any fault, etc.), (iii) calculating the area (Ai) (e.g., drainage area, pore volume, proxy of OOIP, etc.) of any given producer by each polygon associated with each producer based on geometry or the geological boundary of the polygon, (iv) for each producer, calculating cumulative oil production (Qi) and a ratio between Qi and Ai, (v) ranking the producers from the smallest to largest Q/A ratio, and/or (vi) locating or indicating infill drilling places in the polygons with high-ranking producers.  FIG. 28B  and the following table illustrate an example. 
     
       
         
           
               
               
               
               
               
             
               
                   
               
               
                   
                 Area of producer- 
                 Cumulative Oil 
                   
                   
               
               
                   
                 centered  
                 Production from 
                 Ratio 
                   
               
               
                 Producer 
                 polygon (A) 
                 each producer (Q) 
                 (Q/A) 
                 Rank 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 1 
                 35 
                 1269 
                 36.3 
                 2 
               
               
                 2 
                 23 
                 6389 
                 277.8 
                 7 
               
               
                 3 
                 82 
                 8929 
                 108.9 
                 4 
               
               
                 4 
                 22 
                 9827 
                 446.7 
                 9 
               
               
                 5 
                 57 
                 6492 
                 113.9 
                 5 
               
               
                 6 
                 33 
                 446 
                 13.5 
                 1 
               
               
                 7 
                 15 
                 8921 
                 594.7 
                 10 
               
               
                 8 
                 24 
                 8956 
                 373.2 
                 8 
               
               
                 9 
                 45 
                 8013 
                 178.1 
                 6 
               
               
                 10 
                 45 
                 1643 
                 36.5 
                 3 
               
               
                   
               
            
           
         
       
     
       FIG. 29A  illustrates one embodiment of a computer implemented method for using polygons to choose between two infill candidates in two different reservoirs, referred to herein as the method  2900 . The method  2900  includes (i) loading or receiving well locations, reservoir boundary, and injection and production rate histories, (ii) creating producer-centered (e.g., Voronoi) polygons based on the producer locations and the reservoir boundary (or any fault, etc.), (iii) calculating the area (A i ) (e.g., drainage area, pore volume, proxy of OOIP, etc.) covered by each polygon associated with each producer based on geometry (or the geological boundary) of the polygon, (iv) for each producer, calculating cumulative oil production (Qi) and rank producers from largest Q to smallest Q, (v) for each producer, calculating cumulative oil production (Qi) and a ratio between Qi and Ai, (vi) ranking the producers from the smallest to largest Q/A ratio, (vii) calculating a norm area (or Pore volume) and norm cumulative oil production in that reservoir, and/or (viii) calculating an index of uneven sweep (IUS) of the reservoir. The method  2900  further includes (viiii) repeating (i)-(viii) for another reservoir, and ranking the reservoir for infill drilling opportunity with the largest IUS.  FIGS. 29B-29C  and the following table illustrate an example. 
     
       
         
           
               
            
               
                   
               
               
                 Reservoir 1 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                   
                 Area (or 
                   
                   
                   
                   
                   
                   
               
               
                   
                 Pore Volume) 
                 Cumulative 
                   
                   
                   
                   
                   
               
               
                   
                 of Producer- 
                 Oil Production 
                   
                   
                 Norm Area 
                 Norm 
                   
               
               
                   
                 centered 
                 from each 
                 Ratio 
                   
                 (or Pore 
                 Cum 
                   
               
               
                 Producer 
                 Polygon (A) 
                 producer (Q) 
                 (Q/A) 
                 Rank 
                 Volume) 
                 Oil 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 7 
                 15 
                 8921 
                 594.7 
                 1 
                 0.04 
                 0.15 
                 0.002884 
               
               
                 4 
                 22 
                 9827 
                 446.7 
                 2 
                 0.10 
                 0.31 
                 0.016005 
               
               
                 8 
                 24 
                 8956 
                 373.2 
                 3 
                 0.16 
                 0.46 
                 0.040035 
               
               
                 2 
                 23 
                 6389 
                 277.8 
                 4 
                 0.22 
                 0.56 
                 0.07067 
               
               
                 9 
                 45 
                 8013 
                 178.1 
                 5 
                 0.34 
                 0.69 
                 0.144579 
               
               
                 5 
                 57 
                 6492 
                 113.9 
                 6 
                 0.49 
                 0.80 
                 0.256018 
               
               
                 3 
                 82 
                 8929 
                 108.9 
                 7 
                 0.70 
                 0.94 
                 0.443589 
               
               
                 10 
                 45 
                 1643 
                 36.5 
                 8 
                 0.82 
                 0.97 
                 0.556779 
               
               
                 1 
                 35 
                 1269 
                 36.3 
                 9 
                 0.91 
                 0.99 
                 0.647012 
               
               
                 6 
                 33 
                 446 
                 13.5 
                 10 
                 1.00 
                 1.00 
                 0.733309 
               
               
                 sum 
                 381 
                 60885 
                   
                   
                   
                   
                   
               
               
                   
                   
                   
                   
                   
                   
                 IUS 
                 0.467 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
            
               
                   
               
               
                 Reservoir 2 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                   
                 Area (or 
                   
                   
                   
                   
                   
                   
               
               
                   
                 Pore Volume) 
                 Cumulative 
                   
                   
                   
                   
                   
               
               
                   
                 of Producer- 
                 Oil Production 
                   
                   
                 Norm Area 
                 Norm 
                   
               
               
                   
                 centered 
                 from each 
                 Ratio 
                   
                 (or Pore 
                 Cum 
                   
               
               
                 Producer 
                 Polygon (A) 
                 producer (Q) 
                 (Q/A) 
                 Rank 
                 Volume) 
                 Oil 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 9 
                 38 
                 6522 
                 171.6 
                 1 
                 0.05 
                 0.12 
                 0.003021 
               
               
                 2 
                 57 
                 7304 
                 128.1 
                 2 
                 0.13 
                 0.25 
                 0.017157 
               
               
                 5 
                 68 
                 8452 
                 124.3 
                 3 
                 0.22 
                 0.41 
                 0.04708 
               
               
                 7 
                 83 
                 9381 
                 113.0 
                 4 
                 0.33 
                 0.58 
                 0.101643 
               
               
                 6 
                 57 
                 4921 
                 86.3 
                 5 
                 0.40 
                 0.67 
                 0.149051 
               
               
                 4 
                 87 
                 5688 
                 65.4 
                 6 
                 0.52 
                 0.78 
                 0.232659 
               
               
                 10 
                 45 
                 2786 
                 61.9 
                 7 
                 0.58 
                 0.83 
                 0.280552 
               
               
                 8 
                 79 
                 4218 
                 53.4 
                 8 
                 0.68 
                 0.90 
                 0.371375 
               
               
                 3 
                 44 
                 1697 
                 38.6 
                 9 
                 0.74 
                 0.94 
                 0.425132 
               
               
                 12 
                 44 
                 1000 
                 22.7 
                 10 
                 0.80 
                 0.95 
                 0.480336 
               
               
                 1 
                 79 
                 1747 
                 22.1 
                 11 
                 0.90 
                 0.99 
                 0.582096 
               
               
                 11 
                 72 
                 764 
                 10.6 
                 12 
                 1.00 
                 1.00 
                 0.677043 
               
               
                 Sum 
                 753 
                 54480 
                   
                   
                   
                 IUS 
                 0.354 
               
               
                   
               
            
           
         
       
     
       FIG. 30A  illustrates one embodiment of a computer implemented method for using polygons (e.g., streamgrids) for pattern realignment, referred to herein as the method  3000 . The method  2800  includes (i) loading or receiving well locations, and injection and production rate histories for all wells, (ii) creating polygons (e.g., streamgrid polygons) based on the well locations, (iii) calculating allocation factors for injector and producers based on any available allocation method (e.g., injection angle, producer angle), (iv) calculating the allocated water injection and water production within each polygon (e.g., streamgrid) (between connected injector-producer pair), (v) calculating water cycling between connected injector-producer pair for each polygon (e.g., streamgrid), (vi) defining a threshold for water cycling based on a distribution, and/or (vii) identifying at least one polygon (e.g., streamgrid) that have water cycling above the threshold and convert the producer in that polygon (e.g., streamgrid) for pattern realignment.  FIGS. 30B-30C  illustrate an example. 
       FIG. 31A  illustrates one embodiment of a computer implemented method for using polygons (e.g., streamgrid) allocation factors to initiate CRM interwell connectivity, referred to herein as the method  3100 . The method  3100  includes (i) loading or receiving well locations, and injection and production rate histories for all wells, (ii) creating polygons (e.g., streamgrid) based on the well locations, (iii) calculating the allocation factors for injector based on any available allocation method (e.g. injection angle, number of producers, and Injector Area, etc.), and/or (iv) exporting those allocation factors as initial values of interwell connectivity between well pairs in CRM.  FIGS. 31B-31C  illustrate an example. 
       FIG. 32A  illustrates one embodiment of a computer implemented method for estimating maximal areal sweep by zone and by reservoir with the populated polygons (e.g., streamgrids), referred to herein as the method  3200 . The method  3200  includes (i) for any given zone of a reservoir, getting a reservoir boundary in that zone and calculating its total area (St), (ii) getting contact point of all wells that penetrate and are perforated in that zone, (iii) creating streamgrid with the well-zone contact locations, (iv) calculating the total area (S) covered by populated streamgrids, and/or (v) estimating maximal areal sweep efficiency in that zone, which equals S/St. The method  3200  also includes repeating (i)-(v) for all zones in a reservoir and getting the S and St for each zone, calculate the ratio between summation of S from all zones and summation of St from all zones to get the maximum areal sweep efficiency for the reservoir.  FIGS. 32B-32C  illustrate an example. 
     Value of Injected Fluid (VOIF) 
     An embodiment of a computer implemented method of determining a value of injected fluid for a flood operation on a hydrocarbon reservoir having at least one injection well and at least one production well, referred to as  3300  in  FIG. 33 . The method  3300  can be executed by a computing system, such as the computing system  200  of  FIG. 2A  or the CRM application  214  thereof. After executing the method  2000  of  FIG. 20 , the resulting output may be used as input to update or integrate with the polygon application  216 , the zonal application  218 , the petrotechnical mapping application  220 , the one or more other application  221 , the flooding analysis application  212 , and/or other item (e.g.,  FIGS. 2A, 2C ). For ease of understanding, a running example will be used and the running example assumes five injection wells and four production wells. 
     At  3302 , the method  3300 , for a first injection well, receiving injection rate data for the first injection well and production rate data for at least one production well communicating with the injection well. At  3304 , the method  3300  includes running capacitance resistance modeling using the received data to generate an interwell connectivity for each injection well and production well pair. CRM may be run using allowed well connections as described herein. The table below illustrates the interwell connectivities: 
     
       
         
           
               
            
               
                   
               
               
                 Connectivity Values 
               
            
           
           
               
               
               
            
               
                   
                 CRM Parameters for Total Production Estimation 
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                   
                   
                 Inj. 1 
                 Inj. 2 
                 Inj. 3 
                 Inj. 4 
                 Inj. 5 
                   
                 q(t = t0), 
                 qt_j Error, 
               
               
                   
                 τ 
                 (f1j) 
                 (f2j) 
                 (f3j) 
                 (f4j) 
                 (f5j) 
                 Jj 
                 B/D 
                 B/D 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                 Pro. 1 
                 365 
                 0.3 
                 0.2 
                 0.5 
                 0.25 
                 0.25 
                 0 
                 3177 
                 61.14295976 
               
               
                 Pro. 2 
                 60 
                 0.4 
                 0.4 
                 0 
                 0.25 
                 0.25 
                 0 
                 199 
                 41.56255395 
               
               
                 Pro. 3 
                 180 
                 0.3 
                 0.1 
                 0.25 
                 0.25 
                 0.1 
                 0 
                 187 
                 28.98366241 
               
               
                 Pro. 4 
                 180 
                 0 
                 0.1 
                 0.25 
                 0.25 
                 0.25 
                 0 
                 2333 
                 33.53199791 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Sum fij =&lt; 1 
                 1 
                 0.8 
                 1 
                 1 
                 0.85 
                   
                   
                 41.30529351 
               
               
                 Injection Efficiency: 
                 0.925 
                   
                   
                   
                   
                   
                   
                   
               
               
                 Target VRR: 
                 1.081 
               
               
                   
               
            
           
         
       
     
     At  3306 , the method  3300  includes determining a value of injected fluid for each injection well and production well pair using the generated interwell connectivity for the well pair and an oil-cut value for the production well of the pair. In some embodiments, determining the value of injected fluid per well pair includes using an equation for each well pair, wherein the equation is:
 
VOIF ij=f   ij   f   oj  
 
In the equation, f oj  is oil cut and f ij  is interwell connectivity. In some embodiments, the f ij  may be at a steady state as discussed herein. Alternatively, if transient is desired, then fij may be replaced with f*ij in the equation.
 
     At  3308 , the method  3300  includes aggregating the generated values of injected fluid per pair to determine a value of injected fluid for the first injection well. In the running example, the final value of injected fluid for the first injection well is 0.37 at steady state by aggregating the four values of injected fluid per well pair (0.15+0.16+0 0.06+0=0.37). 
     At  3310 , the method  3300  includes generating a value of injected fluid for at least one other injection well. At  3312 , the method  3300  includes ranking the first injection well and the at least one other injection well based on the values of injected fluid. Returning to the running example, at the steady state, the ranking and intermediate items may be the following: 
     
       
         
           
               
            
               
                   
               
               
                 Steady State Connectivities 
               
               
                 VOIF Table Steady State Connectivities 
               
               
                 Time 1E+13 days 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 fij Steady 
                   
                   
                   
                   
                   
                 Last Year 
                 Oil- 
               
               
                 State 
                 Inj. 1 
                 Inj. 2 
                 Inj. 3 
                 Inj. 4 
                 Inj. 5 
                 Oil cut 
                 Cut j 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Pro. 1 
                 0.30 
                 0.20 
                 0.50 
                 0.25 
                 0.25 
                   
                   
               
               
                 Pro. 2 
                 0.40 
                 0.40 
                 0.00 
                 0.25 
                 0.25 
                 Pro. 1 
                 0.5 
               
               
                 Pro. 3 
                 0.30 
                 0.10 
                 0.25 
                 0.25 
                 0.10 
                 Pro. 2 
                 0.4 
               
               
                 Pro. 4 
                 0.00 
                 0.10 
                 0.25 
                 0.25 
                 0.25 
                 Pro. 3 
                 0.2 
               
               
                   
                   
                   
                   
                   
                   
                 Pro. 4 
                 0.2 
               
               
                 Sum fij 
                 1 
                 0.8 
                 1 
                 1 
                 0.85 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                   
               
             
            
               
                 Calculate VOIF for each wellpair - Sum for Injectors, Rank 
               
               
                 Injectors 
               
               
                 For One Barrel 
               
               
                 Table Steady State 
               
               
                 Value of Injected Fluid (VOIF) Evaluation 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 for 1 bbl of injection 
                 1 
                 1 
                 1 
                 1 
                 1 
               
               
                 VOIF*_ij 
                 Inj. 
                 Inj.  
                 Inj. 
                 Inj.  
                 Inj.  
               
               
                   
                 1 
                 2 
                 3 
                 4 
                 5 
               
               
                 Pro. 1 
                 0.15 
                 0.1 
                 0.25 
                 0.125 
                 0.125 
               
               
                 Pro. 2 
                 0.16 
                 0.16 
                 0 
                 0.1 
                 0.1 
               
               
                 Pro. 3 
                 0.06 
                 0.02 
                 0.05 
                 0.05 
                 0.02 
               
               
                 Pro. 4 
                 0 
                 0.02 
                 0.05 
                 0.05 
                 0.05 
               
               
                 VOIF_i 
                 0.37 
                 0.3 
                 0.35 
                 0.325 
                 0.295 
               
               
                 Steady State Rank 
                 1 
                 4 
                 2 
                 3 
                 5 
               
               
                   
               
            
           
         
       
     
     At  3314 , the method  3300  may include generating a net value of injected fluid for the first injection well and the at least one other injection well. The net value for a particular injection well may be determines using the following equations:
 
Net VOIF i =Σ j   N   f   ij   I   i   f   oj  OR Net VOIF ij   =f   ij   I   i   f   oj  
 
     At  3316 , the method  3300  includes reranking the first injection well and the at least one other injection well based the net values of injected fluid. In the running example, for steady state, the reranking may be: 
     
       
         
           
               
             
               
                   
               
               
                 Calculate Net Value of Injection - Steady State 
               
               
                 For Current Injection 
               
               
                 Steady State 
               
               
                 Net Value of Injected Fluid (VOIF) Evaluation 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Current Inj_i, B/D 
                 1000 
                 500 
                 1000 
                 500 
                 1000 
               
               
                 Net VOIF_ij 
                 Inj. 1 
                 Inj. 2 
                 Inj. 3 
                 Inj. 4 
                 Inj. 5 
               
               
                 Pro. 1 
                 150 
                 50 
                 250 
                 62.5 
                 125 
               
               
                 Pro. 2 
                 160 
                 80 
                 0 
                 50 
                 100 
               
               
                 Pro. 3 
                 60 
                 10 
                 50 
                 25 
                 20 
               
               
                 Pro. 4 
                 0 
                 10 
                 50 
                 25 
                 50 
               
               
                 Net VOIF_i 
                 370 
                 150 
                 350 
                 162.5 
                 295 
               
               
                 Rank 
                 1 
                 5 
                 2 
                 4 
                 3 
               
               
                   
               
            
           
         
       
     
     In the running example, assuming transient, the following tables illustrate the items that may be generated: 
     
       
         
           
               
            
               
                   
               
               
                 Transient Connectivities 
               
               
                 VOIF Table 2 B 6 month 
               
               
                 Time 180 days 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                   
                 Last Year 
                 Oil- 
               
               
                 fij Transient 
                 Inj. 1 
                 Inj. 2 
                 Inj. 3 
                 Inj. 4 
                 Inj. 5 
                 Oil cut 
                 Cut j 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Pro. 1 
                 0.12 
                 0.08 
                 0.19 
                 0.10 
                 0.10 
                 Pro. 1 
                 0.5 
               
               
                 Pro. 2 
                 0.38 
                 0.38 
                 0.00 
                 0.24 
                 0.24 
                 Pro. 2 
                 0.4 
               
               
                 Pro. 3 
                 0.19 
                 0.06 
                 0.16 
                 0.16 
                 0.06 
                 Pro. 3 
                 0.2 
               
               
                 Pro. 4 
                 0.00 
                 0.06 
                 0.16 
                 0.16 
                 0.16 
                 Pro. 4 
                 0.2 
               
               
                 Sum 
                 0.69 
                 0.58 
                 0.51 
                 0.65 
                 0.56 
                   
                   
               
               
                 Transient fij 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                   
               
             
            
               
                 Calculate VOIF for each well pair - Sum for Injectors, Rank Injectors 
               
               
                 For One Barrel 
               
               
                 Table Transient 
               
               
                 Value of Injected Fluid (VOIF) Evaluation 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 for 1 bbl of injection 
                 1 
                 1 
                 1 
                 1 
                 1 
               
               
                 VOIF*_ij 
                 Inj. 1 
                 Inj. 2 
                 Inj. 3 
                 Inj. 4 
                 Inj. 5 
               
               
                 Pro. 1 
                 0.06 
                 0.04 
                 0.10 
                 0.05 
                 0.05 
               
               
                 Pro. 2 
                 0.15 
                 0.15 
                 0.00 
                 0.10 
                 0.10 
               
               
                 Pro. 3 
                 0.04 
                 0.01 
                 0.03 
                 0.03 
                 0.01 
               
               
                 Pro. 4 
                 0.00 
                 0.01 
                 0.03 
                 0.03 
                 0.03 
               
               
                 VOIF_i 
                 0.25 
                 0.22 
                 0.16 
                 0.21 
                 0.19 
               
               
                 Transient Rank 
                 1 
                 2 
                 5 
                 3 
                 4 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                   
               
             
            
               
                 Calculate VOIF for each well pair - Sum for Injectors, Rank Injectors 
               
               
                 For Current Injection 
               
               
                 Transient 
               
               
                 Net Value of Injected Fluid (VOIF) Evaluation 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Current Inj_i, B/D 
                 1000 
                 500 
                 1000 
                 500 
                 1000 
               
               
                 Net VOIF_ij 
                 Inj. 1 
                 Inj. 2 
                 Inj. 3 
                 Inj. 4 
                 Inj. 5 
               
               
                 Pro. 1 
                 58 
                 19 
                 97 
                 24 
                 49 
               
               
                 Pro. 2 
                 152 
                 76 
                 0 
                 48 
                 95 
               
               
                 Pro. 3 
                 38 
                 6 
                 32 
                 16 
                 13 
               
               
                 Pro. 4 
                 0 
                 6 
                 32 
                 16 
                 32 
               
               
                 Net VOIF_i 
                 248 
                 108 
                 161 
                 103 
                 188 
               
               
                 Rank 
                 1 
                 4 
                 3 
                 5 
                 2 
               
               
                   
               
            
           
         
       
     
     Those of ordinary skill in the art will appreciate that modifications may be made to the illustrated embodiments. For example, the hydrocarbon reservoir may include a plurality of zones, and each zone is treated as an injection well as described above. Indeed, VOIF may be determined at a well level or at a zonal level. Furthermore, sensitivity analysis may be used. For example, (i) sensitivity analysis may be performed to determine the allowed well connections for CRM, (ii) CRM may be run with sensitivity analysis, (iii) VOIF may be determined for an injection well at the steady state with or without sensitivity analysis, and/or (iv) VOIF may be determined for an injection well as transient with or without sensitivity analysis. VOIF may also be used to recommend a target injection rate. VOIF may also be used to recommend a conformance candidate (e.g., if f*ij or fij is more than a threshold such as 0.4, and if VOIF is less than a threshold such as 0.1, and if injection rate is more than average, then the method  3300  may recommend the corresponding well and/or zone as a conformance candidate). Criteria may also be used to recommend a stimulation candidate. Thus, at  3318 , the method  3300  may generate a recommendation. Also, in some embodiments, PLT can be a substitute for oil-cut in determining VOIF. 
     CRM Zonal 
     CRM General Formulation: In one example, a representation of a producer is interacting with offset injectors in which a fraction of each injector injection rate (f ij ) is contributing to the production rate of producer j. Because CRM is derived based on the continuity equation, all the model parameters reflect physical characteristics of the reservoir. Interwell connectivities (f ij ), delay time constants (τ ij ), and productivity indices (J ij ) between any injector i (i=1, 2, . . . , N inj ) and producer j (j=1, 2, . . . , N pro ) are CRM model parameters and are back-calculated during the course of simultaneous fitting of Eq. 1 to the fluid production of all producers. CRM formulation is based on producer control volume and can be written as: 
                       q   j     ⁡     (     t   n     )       =           q   j     ⁡     (     t   0     )       ⁢     e     -     (         t   n     -     t   0         τ   j       )           +                 ⁢       ∑     k   =   1     n     ⁢           ⁢         e     -     (         t   n     -     t   0         τ   j       )         ⁡     (     1   -     e         -   Δ     ⁢           ⁢     t   k         τ   j           )       ⁢     (         ∑     i   =   1       N   inj       ⁢           ⁢     [       f   ij     ⁢     I   i     (   k   )         ]       -       J   j     ⁢     τ   j     ⁢       Δ   ⁢           ⁢     p     wf   ,   j       (   k   )           Δ   ⁢           ⁢     t   k             )     ⁢     
     ⁢           ⁢     (         for   ⁢           ⁢   j     =   1     ,   2   ,   …   ⁢           ,     N   pro       )                     (   1   )               
Where I i   (k)  and Δp wf,j   (k)  represent the rate of injector i and changes in flowing bottomhole pressure of producer j during time interval Δt k =t k −t k-1 , respectively.
 
     As Eq. 1 illustrates, fluid production rate at producer j is composed of three elements: primary depletion the first term, the impact of injection input signals, and the variation caused by changing the bottomhole pressure of producer j. 
     CRM Zonal and its application in generating continuous production profile over time: Zonal connectivity can be identified by CRM using ILT data. Injection profile data obtained over time can be used to split injection to the zonal level. Assuming no vertical communication between different zones, and using the injection profile and injection rates each injector gets split into multiple zonal level injectors. The Table below illustrates the breakdown of injection rate from one injector (I1) over time into two zones (Z1 and Z2) as an example. 
     
       
         
           
               
            
               
                   
               
               
                 Injection Profile 
               
            
           
           
               
               
               
            
               
                   
                 Zonal split 
                 Zonal 
               
            
           
           
               
               
               
               
            
               
                   
                   
                 from ILT (%) 
                 Injection Rate 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                 Injection 
                 ILT 
                 ILT 
                 Injection - 
                 Injection - 
               
               
                   
                 Date 
                 Rate I1 
                 I1-Z1 
                 I1-Z2 
                 I1Z1 
                 I1Z2 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 1 
                 Jan. 1, 2000 
                 4490 
                 41 
                 59 
                 1840.9 
                 2649.1 
               
               
                 2 
                 Feb. 1, 2000 
                 1090 
                 45 
                 55 
                 490.5 
                 599.5 
               
               
                 3 
                 Mar. 1, 2000 
                 2530 
                 20 
                 80 
                 506 
                 2024 
               
               
                 4 
                 Apr. 1, 2000 
                 2950 
                 92 
                 8 
                 2714 
                 236 
               
               
                 5 
                 May 1, 2000 
                 2050 
                 51 
                 49 
                 1045.5 
                 1004.5 
               
               
                 6 
                 Jun. 1, 2000 
                 4040 
                 91 
                 9 
                 3676.4 
                 363.6 
               
               
                 7 
                 Jul. 1, 2000 
                 3010 
                 32 
                 68 
                 963.2 
                 2046.8 
               
               
                 8 
                 Aug. 1, 2000 
                 3640 
                 2 
                 98 
                 72.8 
                 3567.2 
               
               
                 9 
                 Sep. 1, 2000 
                 2960 
                 67 
                 33 
                 1983.2 
                 976.8 
               
               
                 10 
                 Oct. 1, 2000 
                 3230 
                 73 
                 27 
                 2357.9 
                 872.1 
               
               
                 11 
                 Nov. 1, 2000 
                 4420 
                 53 
                 47 
                 2342.6 
                 2077.4 
               
               
                 12 
                 Dec. 1, 2000 
                 2290 
                 44 
                 56 
                 1007.6 
                 1282.4 
               
               
                 13 
                 Jan. 1, 2001 
                 4080 
                 4 
                 96 
                 163.2 
                 3916.8 
               
               
                 14 
                 Feb. 1, 2001 
                 4880 
                 56 
                 44 
                 2732.8 
                 2147.2 
               
               
                 15 
                 Mar. 1, 2001 
                 2280 
                 33 
                 67 
                 752.4 
                 1527.6 
               
               
                 16 
                 Apr. 1, 2001 
                 1980 
                 61 
                 39 
                 1207.8 
                 772.2 
               
               
                 17 
                 May 1, 2001 
                 4710 
                 6 
                 94 
                 282.6 
                 4427.4 
               
               
                 18 
                 Jun. 1, 2001 
                 4530 
                 75 
                 25 
                 3397.5 
                 1132.5 
               
               
                   
               
            
           
         
       
     
     Following the splitting of an injector well into multiple zonal level injectors, a capacitance resistance model is generated in which each zonal level injector is treated as a single injector, therefore interwell connectivities are obtained at the zonal level for at least one injector and the producers that it supports. In the running example with one injector (I1) and two zones (Z1 and Z2) and two producers (P1 and P2), interwell connectivities are obtained at the zonal level (fij) between injector I1 at zone Z1 and zone Z2 with the two producers P2 and P3 as shown in table below: 
     
       
         
           
               
            
               
                   
               
               
                 CRM Connectivity at Zonal level - fij 
               
            
           
           
               
               
               
               
               
            
               
                   
                 I1Z1-P2 
                 I1Z2-P2 
                 I1Z1-P3 
                 I1Z2-P3 
               
               
                   
                   
               
               
                   
                 0.2 
                 0.4 
                 0.8 
                 0.6 
               
               
                   
                   
               
            
           
         
       
     
     The injected fluid can have a preferred flow path in different zones. The preferential flow path in the running example is shown in Table above. It shows that the injected fluid has a preferred flow path between Injector I1 and Producer P3 in zone Z2. 
     Secondary Production Application: Continuous production profile in case of if only secondary recovery is contributing in production is obtained from injection rates and zonal level connectivities by summing up contribution of zonal injectors and their connectivities to at least one of the producers. 
     
       
         
           
             
               At 
               ⁢ 
               
                   
               
               ⁢ 
               Zone 
               ⁢ 
               
                   
               
               ⁢ 
               1 
               ⁢ 
               
                   
               
               ⁢ 
               for 
               ⁢ 
               
                   
               
               ⁢ 
               Producer 
               ⁢ 
               
                   
               
               ⁢ 
               j 
               ⁢ 
               
                   
               
               ⁢ 
               
                 : 
               
               ⁢ 
               
                   
               
               ⁢ 
               
                 
                   q 
                   j 
                   
                     Z 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                 
                 ⁡ 
                 
                   ( 
                   t 
                   ) 
                 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 
                   N 
                   inj 
                 
               
               ⁢ 
               
                   
               
               ⁢ 
               
                 [ 
                 
                   
                     f 
                     ij 
                     
                       Z 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       1 
                     
                   
                   ⁢ 
                   
                     
                       I 
                       i 
                       
                         ( 
                         
                           Z 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           1 
                         
                         ) 
                       
                     
                     ⁡ 
                     
                       ( 
                       t 
                       ) 
                     
                   
                 
                 ] 
               
             
           
         
       
       
         
           
             ( 
             
               
                 
                   for 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   j 
                 
                 = 
                 1 
               
               , 
               2 
               , 
               … 
               ⁢ 
               
                   
               
               , 
               
                 N 
                 pro 
               
             
             ) 
           
         
       
     
     Considering the zonal level connectivities and injection rates, of the example above, CRM estimates of continuous production rate at the zonal level for contributing zones of producer P2 and P3 are obtained and shown in table below, 
     
       
         
           
               
               
               
            
               
                   
                   
               
               
                   
                   
                 CRM Estimates of Secondary flux at zone 
               
               
                   
                   
                 level (qij = Injection rate by zone 
               
               
                   
                   
                 multiplied by connectivity @ zone 1 and 2) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                 I1Z2- 
                   
                 I1Z2- 
               
               
                   
                 Date 
                 I1Z1-P2 
                 P2 
                 I1Z1-P3 
                 P3 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Jan. 1, 2000 
                 368 
                 1060 
                 1473 
                 1589 
               
               
                   
                 Feb. 1, 2000 
                 98 
                 240 
                 392 
                 360 
               
               
                   
                 Mar. 1, 2000 
                 101 
                 810 
                 405 
                 1214 
               
               
                   
                 Apr. 1, 2000 
                 542 
                 94 
                 2171 
                 142 
               
               
                   
                 May 1, 2000 
                 209 
                 402 
                 836 
                 603 
               
               
                   
                 Jun. 1, 2000 
                 735 
                 145 
                 2941 
                 218 
               
               
                   
                 Jul. 1, 2000 
                 192 
                 819 
                 771 
                 1228 
               
               
                   
                 Aug. 1, 2000 
                 14 
                 1427 
                 58 
                 2140 
               
               
                   
                 Sep. 1, 2000 
                 396 
                 391 
                 1587 
                 586 
               
               
                   
                 Oct. 1, 2000 
                 471 
                 349 
                 1886 
                 523 
               
               
                   
                 Nov. 1, 2000 
                 468 
                 831 
                 1874 
                 1246 
               
               
                   
                 Dec. 1, 2000 
                 201 
                 513 
                 806 
                 769 
               
               
                   
                 Jan. 1, 2001 
                 32 
                 1567 
                 131 
                 2350 
               
               
                   
                 Feb. 1, 2001 
                 546 
                 859 
                 2186 
                 1288 
               
               
                   
                 Mar. 1, 2001 
                 150 
                 611 
                 602 
                 917 
               
               
                   
                 Apr. 1, 2001 
                 241 
                 309 
                 966 
                 463 
               
               
                   
                 May 1, 2001 
                 56 
                 1771 
                 226 
                 2656 
               
               
                   
                 Jun. 1, 2001 
                 679 
                 453 
                 2718 
                 680 
               
               
                   
                   
               
            
           
         
       
     
     CRM estimates of production rates for contributing zone is normalized to provide production split of each zone as a continues value during the production of the producers, as shown in table below, these estimate are cross checked with sparse production profile data which also is considered in calibration of the CRM zonal level analysis. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                   
               
               
                   
                   
                   
                 check 
                   
                   
                 Check 
               
               
                   
                   
                   
                 for P2, 
                   
                   
                 for P3, 
               
               
                 Date 
                 Z1-P2 
                 Z2-P2 
                 sum = 1 
                 Z1-P3 
                 Z2-P3 
                 sum = 1 
               
               
                   
               
             
            
               
                 Jan. 1, 2000 
                 0.26 
                 0.74 
                 1.00 
                 0.48 
                 0.52 
                 1.00 
               
               
                 Feb. 1, 2000 
                 0.29 
                 0.71 
                 1.00 
                 0.52 
                 0.48 
                 1.00 
               
               
                 Mar. 1, 2000 
                 0.11 
                 0.89 
                 1.00 
                 0.25 
                 0.75 
                 1.00 
               
               
                 Apr. 1, 2000 
                 0.85 
                 0.15 
                 1.00 
                 0.94 
                 0.06 
                 1.00 
               
               
                 May 1, 2000 
                 0.34 
                 0.66 
                 1.00 
                 0.58 
                 0.42 
                 1.00 
               
               
                 Jun. 1, 2000 
                 0.83 
                 0.17 
                 1.00 
                 0.93 
                 0.07 
                 1.00 
               
               
                 Jul. 1, 2000 
                 0.19 
                 0.81 
                 1.00 
                 0.39 
                 0.61 
                 1.00 
               
               
                 Aug. 1, 2000 
                 0.01 
                 0.99 
                 1.00 
                 0.03 
                 0.97 
                 1.00 
               
               
                 Sep. 1, 2000 
                 0.50 
                 0.50 
                 1.00 
                 0.73 
                 0.27 
                 1.00 
               
               
                 Oct. 1, 2000 
                 0.57 
                 0.43 
                 1.00 
                 0.78 
                 0.22 
                 1.00 
               
               
                 Nov. 1, 2000 
                 0.36 
                 0.64 
                 1.00 
                 0.60 
                 0.40 
                 1.00 
               
               
                 Dec. 1, 2000 
                 0.28 
                 0.72 
                 1.00 
                 0.51 
                 0.49 
                 1.00 
               
               
                 Jan. 1, 2001 
                 0.02 
                 0.98 
                 1.00 
                 0.05 
                 0.95 
                 1.00 
               
               
                 Feb. 1, 2001 
                 0.39 
                 0.61 
                 1.00 
                 0.63 
                 0.37 
                 1.00 
               
               
                 Mar. 1, 2001 
                 0.20 
                 0.80 
                 1.00 
                 0.40 
                 0.60 
                 1.00 
               
               
                 Apr. 1, 2001 
                 0.44 
                 0.56 
                 1.00 
                 0.68 
                 0.32 
                 1.00 
               
               
                 May 1, 2001 
                 0.03 
                 0.97 
                 1.00 
                 0.08 
                 0.92 
                 1.00 
               
               
                 Jun. 1, 2001 
                 0.60 
                 0.40 
                 1.00 
                 0.80 
                 0.20 
                 1.00 
               
               
                   
               
            
           
         
       
     
     Primary and Secondary Production Application: Continuous production profile from primary and secondary production is obtained by using the primary and secondary recovery portion of CRM estimate once the CRM zonal is performed. The Primary portion of production profile is estimated from CRM zonal level primary portion which is an exponential decline. The secondary portion of production rates at zonal level are obtained from summing the multiplication of injection rates and connectivities at zonal level from all injectors contributing in the production of a given producer. 
     
       
         
           
             
               Primary 
               ⁢ 
               
                   
               
               ⁢ 
               Portion 
               ⁢ 
               
                   
               
               ⁢ 
               of 
               ⁢ 
               
                   
               
               ⁢ 
               
                 CRM 
                 : 
               
               ⁢ 
               
                   
               
               ⁢ 
               
                 
                   q 
                   j 
                 
                 ⁡ 
                 
                   ( 
                   
                     t 
                     0 
                   
                   ) 
                 
               
               ⁢ 
               
                 e 
                 
                   - 
                   
                     ( 
                     
                       
                         
                           t 
                           n 
                         
                         - 
                         
                           t 
                           0 
                         
                       
                       
                         τ 
                         
                           Primary 
                           , 
                           j 
                         
                       
                     
                     ) 
                   
                 
               
             
             - 
             
               
                 J 
                 j 
               
               ⁢ 
               
                 τ 
                 
                   Primary 
                   , 
                   j 
                 
               
               ⁢ 
               
                 
                   Δ 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     p 
                     
                       wf 
                       , 
                       j 
                     
                     
                       ( 
                       k 
                       ) 
                     
                   
                 
                 
                   Δ 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     t 
                     k 
                   
                 
               
               ⁢ 
               
                 ( 
                 
                   1 
                   - 
                   
                     e 
                     
                       
                         
                           - 
                           Δ 
                         
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           t 
                           k 
                         
                       
                       
                         τ 
                         
                           Primary 
                           , 
                           j 
                         
                       
                     
                   
                 
                 ) 
               
             
           
         
       
       
         
           
             
                 
             
             ⁢ 
             
               Secondary 
               ⁢ 
               
                   
               
               ⁢ 
               Portion 
               ⁢ 
               
                   
               
               ⁢ 
               of 
               ⁢ 
               
                   
               
               ⁢ 
               
                 CRM 
                 : 
               
               ⁢ 
               
                   
               
               ⁢ 
               
                 ( 
                 
                   1 
                   - 
                   
                     e 
                     
                       
                         
                           - 
                           Δ 
                         
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           t 
                           k 
                         
                       
                       
                         τ 
                         
                           Secondary 
                           , 
                           j 
                         
                       
                     
                   
                 
                 ) 
               
               ⁢ 
               
                 ( 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     
                       N 
                       inj 
                     
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     [ 
                     
                       
                         f 
                         ij 
                       
                       ⁢ 
                       
                         I 
                         i 
                         
                           ( 
                           k 
                           ) 
                         
                       
                     
                     ] 
                   
                 
                 ) 
               
             
           
         
       
       
         
           
             
                 
             
             ⁢ 
             
               ( 
               
                 
                   
                     for 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     j 
                   
                   = 
                   1 
                 
                 , 
                 2 
                 , 
                 … 
                 ⁢ 
                 
                     
                 
                 , 
                 
                   N 
                   pro 
                 
               
               ) 
             
           
         
       
     
     In the running example table below shows historical production, CRM estimate and its primary and secondary portions estimated from equation above, 
     
       
         
           
               
               
               
               
            
               
                   
               
               
                 Production Rate 
                   
                   
                   
               
               
                 History 
                 Primary + 
                 Secondary 
                 Primary 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Pro- 
                 Pro- 
                 Secondary 
                 Portion 
                 Portion 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                   
                 ducer 
                 ducer 
                 CRM 
                 CRM 
                 CRM 
                 CRM 
                 CRM 
                 CRM 
               
               
                 Date 
                 P2 
                 P3 
                 P2 
                 P3 
                 P2 
                 P3 
                 P2 
                 P3 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Jan. 1, 2000 
                 2168 
                 3717 
                 2303 
                 4032 
                 1428 
                 3062 
                 876 
                 969 
               
               
                 Feb. 1, 2000 
                 887 
                 1288 
                 
                   950 
                 
                 1349 
                 338 
                 752 
                 612 
                 597 
               
               
                 Mar. 1, 2000 
                 1317 
                 2058 
                 1414 
                 2144 
                 911 
                 1619 
                 503 
                 525 
               
               
                 Apr. 1, 2000 
                 938 
                 2672 
                 1020 
                 2732 
                 636 
                 2313 
                 383 
                 419 
               
               
                 May 1, 2000 
                 834 
                 1733 
                 890 
                 
                   1813 
                 
                 611 
                 1439 
                 279 
                 373 
               
               
                 Jun. 1, 2000 
                 1046 
                 3400 
                 1092 
                 3501 
                 880 
                 3159 
                 212 
                 342 
               
               
                 Jul. 1, 2000 
                 1133 
                 2196 
                 1184 
                 2406 
                 1011 
                 1999 
                 173 
                 408 
               
               
                 Aug. 1, 2000 
                 1532 
                 2360 
                 1658 
                 2462 
                 1441 
                 2199 
                 218 
                 264 
               
               
                 Sep. 1, 2000 
                 854 
                 2305 
                 884 
                 2382 
                 787 
                 2173 
                 97 
                 210 
               
               
                 Oct. 1, 2000 
                 870 
                 2518 
                 893 
                 2631 
                 820 
                 2410 
                 73 
                 221 
               
               
                 Nov. 1, 2000 
                 1336 
                 3209 
                 1376 
                 3286 
                 1299 
                 3121 
                 77 
                 165 
               
               
                 Dec. 1, 2000 
                 741 
                 1648 
                 808 
                 1696 
                 714 
                 1576 
                 95 
                 121 
               
               
                 Jan. 1, 2001 
                 1619 
                 2540 
                 1741 
                 2645 
                 1599 
                 2481 
                 142 
                 164 
               
               
                 Feb. 1, 2001 
                 1420 
                 3523 
                 1516 
                 3831 
                 1405 
                 3475 
                 111 
                 357 
               
               
                 Mar. 1, 2001 
                 772 
                 1558 
                 800 
                 1587 
                 761 
                 1518 
                 39 
                 68 
               
               
                 Apr. 1, 2001 
                 558 
                 1462 
                 566 
                 1521 
                 550 
                 1430 
                 16 
                 92 
               
               
                 May 1, 2001 
                 1833 
                 2909 
                 1966 
                 2972 
                 1827 
                 2883 
                 139 
                 90 
               
               
                 Jun. 1, 2001 
                 1137 
                 3419 
                 1198 
                 3492 
                 1132 
                 3398 
                 66 
                 95 
               
               
                   
               
            
           
         
       
     
     Similar to the case of no primary recovery zonal contribution from each of producing zones for secondary production is estimated using CRM zonal level interwell connectivities as shown before while the primary recovery contribution is evaluated by using at least one measurement of production profile data for each producer to divide the primary production portion of to its contributing zones, in the example of the producer P2 CRM estimates a total production rate of 950 bbl/day (highlighted in table above), and profile is obtained on February 2000 indicating 332 (234+98) and 618 (378+240) bbl/day production for Zone Z1 and Z2 accordingly; and for producer P3 CRM estimates a total production rate of 1813 bbl/day (highlighted in table above), a profile is obtained on May 2000 indicating the production rate of 906 (Secondary portion 70+primary portion 836) and 907 (304+603) bbl/day production for Zone Z1 and Z2 accordingly; therefore, the Primary production portion of a well is divided accordingly to make up for the remaining production of each zone in February 2000 and May 2000 for Producer P2 and P3: 
     
       
         
           
               
               
               
            
               
                   
               
               
                   
                 Secondary Portions 
                 Primary Portions 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                   
                 I1Z1- 
                 I1Z2- 
                 I1Z1- 
                 I1Z2- 
                 Z1- 
                 Z2- 
                 Z1- 
                 Z2- 
               
               
                 Date 
                 P2 
                 P2 
                 P3 
                 P3 
                 P2 
                 P2 
                 P3 
                 P3 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Jan. 1, 2000 
                 368 
                 1060 
                 1473 
                 1589 
                 335 
                 540 
                 181 
                 788 
               
               
                 Feb. 1, 2000 
                 
                   98 
                 
                 
                   240 
                 
                 392 
                 360 
                 
                   234 
                 
                 
                   378 
                 
                 112 
                 485 
               
               
                 Mar. 1, 2000 
                 101 
                 810 
                 405 
                 1214 
                 193 
                 310 
                 98 
                 427 
               
               
                 Apr. 1, 2000 
                 542 
                 94 
                 2171 
                 142 
                 147 
                 237 
                 78 
                 341 
               
               
                 May 1, 2000 
                 209 
                 402 
                 
                   836 
                 
                 
                   603 
                 
                 107 
                 172 
                 
                   70 
                 
                 
                   304 
                 
               
               
                 Jun. 1, 2000 
                 735 
                 145 
                 2941 
                 218 
                 81 
                 131 
                 64 
                 278 
               
               
                 Jul. 1, 2000 
                 192 
                 819 
                 771 
                 1228 
                 66 
                 107 
                 76 
                 331 
               
               
                 Aug. 1, 2000 
                 14 
                 1427 
                 58 
                 2140 
                 83 
                 134 
                 49 
                 214 
               
               
                 Sep. 1, 2000 
                 396 
                 391 
                 1587 
                 586 
                 37 
                 60 
                 39 
                 171 
               
               
                 Oct. 1, 2000 
                 471 
                 349 
                 1886 
                 523 
                 28 
                 45 
                 41 
                 180 
               
               
                 Nov. 1, 2000 
                 468 
                 831 
                 1874 
                 1246 
                 29 
                 47 
                 31 
                 134 
               
               
                 Dec. 1, 2000 
                 201 
                 513 
                 806 
                 769 
                 36 
                 58 
                 23 
                 98 
               
               
                 Jan. 1, 2001 
                 32 
                 1567 
                 131 
                 2350 
                 54 
                 87 
                 31 
                 134 
               
               
                 Feb. 1, 2001 
                 546 
                 859 
                 2186 
                 1288 
                 42 
                 68 
                 67 
                 290 
               
               
                 Mar. 1, 2001 
                 150 
                 611 
                 602 
                 917 
                 15 
                 24 
                 13 
                 56 
               
               
                 Apr. 1, 2001 
                 241 
                 309 
                 966 
                 463 
                 6 
                 10 
                 17 
                 75 
               
               
                 May 1, 2001 
                 56 
                 1771 
                 226 
                 2656 
                 53 
                 86 
                 17 
                 73 
               
               
                 Jun. 1, 2001 
                 679 
                 453 
                 2718 
                 680 
                 25 
                 41 
                 18 
                 77 
               
               
                   
               
            
           
         
       
     
     In the running example, CRM estimates of production rates for each contributing zone is then normalized to provide production split of each zone as a continuous value during the production of the producers for both primary and secondary contributions; as shown in table below, these estimate are cross checked with sparse production profile data which also is considered in calibration of the CRM zonal level analysis. 
     
       
         
           
               
            
               
                   
               
               
                 CRM PLT Estimates (Primary + Secondary) 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Time 
                 Z1-P2 
                 Z2-P2 
                 Check 
                 Z1-P3 
                 Z2-P3 
                 Check 
               
               
                   
               
               
                 Jan. 1, 2000 
                 0.31 
                 0.69 
                   
                 0.41 
                 0.59 
                   
               
               
                 Feb. 1, 2000 
                 
                   0.35 
                 
                 
                   0.65 
                 
                 Measured P2 
                 0.37 
                 0.63 
                   
               
               
                 Mar. 1, 2000 
                 0.21 
                 0.79 
                   
                 0.23 
                 0.77 
                   
               
               
                 Apr. 1, 2000 
                 0.68 
                 0.32 
                   
                 0.82 
                 0.18 
                   
               
               
                 May 1, 2000 
                 0.35 
                 0.65 
                   
                 
                   0.50 
                 
                 
                   0.50 
                 
                 Measured P3 
               
               
                 Jun. 1, 2000 
                 0.75 
                 0.25 
                   
                 0.86 
                 0.14 
                   
               
               
                 Jul. 1, 2000 
                 0.22 
                 0.78 
                   
                 0.35 
                 0.65 
                   
               
               
                 Aug. 1, 2000 
                 0.06 
                 0.94 
                   
                 0.04 
                 0.96 
                   
               
               
                 Sep. 1, 2000 
                 0.49 
                 0.51 
                   
                 0.68 
                 0.32 
                   
               
               
                 Oct. 1, 2000 
                 0.56 
                 0.44 
                   
                 0.73 
                 0.27 
                   
               
               
                 Nov. 1, 2000 
                 0.36 
                 0.64 
                   
                 0.58 
                 0.42 
                   
               
               
                 Dec. 1, 2000 
                 0.29 
                 0.71 
                   
                 0.49 
                 0.51 
                   
               
               
                 Jan. 1, 2001 
                 0.05 
                 0.95 
                   
                 0.06 
                 0.94 
                   
               
               
                 Feb. 1, 2001 
                 0.39 
                 0.61 
                   
                 0.59 
                 0.41 
                   
               
               
                 Mar. 1, 2001 
                 0.21 
                 0.79 
                   
                 0.39 
                 0.61 
                   
               
               
                 Apr. 1, 2001 
                 0.44 
                 0.56 
                   
                 0.65 
                 0.35 
                   
               
               
                 May 1, 2001 
                 0.06 
                 0.94 
                   
                 0.08 
                 0.92 
                   
               
               
                 Jun. 1, 2001 
                 0.59 
                 0.41 
                   
                 0.78 
                 0.22 
               
               
                   
               
            
           
         
       
     
     Those of ordinary skill in the art will appreciate that modifications may be made to the illustrated embodiments, such as the illustrated CRM zonal embodiments (see also  FIGS. 34, 35A-35E, 36A-36H, 37A-37I ). For example, VOIF may be determined at a well level or at a zonal level. Furthermore, sensitivity analysis may be used. Also, for example, (i) sensitivity analysis may be performed to determine the allowed well connections for CRM, (ii) CRM may be run with sensitivity analysis, (iii) VOIF may be determined for an injection well (or zone) at the steady state with or without sensitivity analysis, and/or (iv) VOIF may be determined for an injection well (or zone) as transient with or without sensitivity analysis. CRM zonal may also be used to determine conformance candidates at zonal level and/or to make recommendations as discussed herein. VOIF may also be used to recommend a conformance candidate (e.g., if f*ij or fij is more than a threshold such as 0.4, and if VOIF is less than a threshold such as 0.1, and if injection rate is more than average, then the method  3300  may recommend the corresponding well and/or zone as a conformance candidate). Criteria may also be used to recommend a stimulation candidate. Thus, the method  3400  may generate a recommendation. Also, in some embodiments, PLT can be a substitute for oil-cut in determining VOIF. 
     Furthermore, CRM zonal may be used to determining if there is crossflow between at least two zones. For example, allocation factors may be received or determined from ILT/PLT, K H Q, or other zonal splits (e.g., from zonal application  218 ). Next, CRM may be run with the assumption that each zone is treated like an injector. Next, the method may include calculating CRM based zonal splits for producers by zone. Next, the method may compare the CRM based zonal splits with those received to determine if there is crossflow. 
     Those of ordinary skill in the art will appreciate that CRM zonal may be used to identify the value of injected fluid for each zone of a hydrocarbon reservoir (as discussed in the VOIF section herein), to generate PLTs, to identify conformance control issues (as discussed in the conformance control section herein), etc. 
     While many details have been set forth for purpose of illustration, it will be apparent to those skilled in the art that the disclosure is susceptible to alteration and that certain other details described herein can vary considerably without departing from the basic principles of the invention. In addition, it should be appreciated that structural features or method steps shown or described in any one embodiment herein can be used in other embodiments as well. 
     For the avoidance of doubt, the present application includes the subject-matter defined in the following numbered paragraphs: 
     Allowed Connections 
     A1. A computer implemented method of analyzing a flood operation for a hydrocarbon reservoir, the method comprising: establishing a dataset of allowed well connections for at least one production well and at least one injection well of the hydrocarbon reservoir; iteratively modifying the dataset of allowed well connections based on a distance category, a static features category, a dynamic features category, or any combination thereof, wherein the static features category uses static data of the hydrocarbon reservoir and the dynamic features category uses dynamic data of the hydrocarbon reservoir; and after iteratively modifying the dataset of allowed well connections, running capacitance resistance modeling using the modified dataset of allowed well connections as an input. 
     A2. The method of paragraph A1, further comprising using at least one interwell connectivity generated by running capacitance resistance modeling to modify the dataset of allowed well connections. 
     A3. The method of paragraph A1, wherein the hydrocarbon reservoir includes a plurality of zones, and wherein each zone is treated as an injection well. 
     A4. The method of paragraph A1, wherein iteratively modifying the dataset of allowed well connections includes using a priority between the categories. 
     A5. The method of paragraph A1, wherein iteratively modifying the dataset of allowed well connections based on the distance category includes using a priority within the distance category, a sensitivity analysis for the distance category, or both. 
     A6. The method of paragraph A1, wherein iteratively modifying the dataset of allowed well connections based on the static features category includes using a priority within the static features category, a sensitivity analysis for the static features category, or both. 
     A7. The method of paragraph A1, wherein iteratively modifying the dataset of allowed well connections based on the dynamic features category includes using a priority within the dynamic features category, a sensitivity analysis for the dynamic features category, or both. 
     A8. The method of paragraph A1, wherein the static data includes geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, geological boundaries, a processed version of any of these, or any combination thereof. 
     A9. The method of paragraph A1, wherein the dynamic data includes well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, capacitance resistance modeling output data, polygon data, a processed version of any of these, or any combination thereof. 
     A10. The method of paragraph A1, further comprising updating at least one application based on output from the capacitance resistance modeling. 
     A11. A computer implemented method of analyzing a flood operation for a hydrocarbon reservoir, the method comprising: determining a dataset of allowed well connections for at least one production well and the at least one injection well of the hydrocarbon reservoir based on a distance category; determining a dataset of allowed well connections for the at least one production well and the at least one injection well of the hydrocarbon reservoir based on a static features category, wherein the static features category uses static data of the hydrocarbon reservoir; determining a dataset of allowed well connections for the at least one production well and the at least one injection well of the hydrocarbon reservoir based on a dynamic features category, wherein the dynamic features category uses dynamic data of the hydrocarbon reservoir; comparing the determined allowed well connections from the various datasets; combining the determined allowed well connections from the various datasets based on the comparison and a priority into a combined dataset of allowed well connections; and running capacitance resistance modeling using the combined dataset of allowed well connections as an input. 
     A12. The method of paragraph A11, further comprising using at least one interwell connectivity generated by running capacitance resistance modeling to modify the combined dataset of allowed well connections. 
     A13. The method of paragraph A11, wherein the hydrocarbon reservoir includes a plurality of zones, and wherein each zone is treated as an injection well. 
     A14. The method of paragraph A11, wherein determining the dataset of allowed well connections based on the distance category includes using a priority within the distance category, a sensitivity analysis for the distance category, or both. 
     A15. The method of paragraph A11, wherein determining the dataset of allowed well connections based on the static features category includes using a priority within the static features category, a sensitivity analysis for the static features category, or both. 
     A16. The method of paragraph A11, wherein determining the dataset of allowed well connections based on the dynamic features category includes using a priority within the dynamic features category, a sensitivity analysis for the dynamic features category, or both. 
     A17. The method of paragraph A11, wherein the static data includes geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, geological boundaries, a processed version of any of these, or any combination thereof. 
     A18. The method of paragraph A11, wherein the dynamic data includes well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, capacitance resistance modeling output data, polygon data, a processed version of any of these, or any combination thereof. 
     A19. The method of paragraph A1, further comprising updating at least one application based on output from the capacitance resistance modeling. 
     A20. A computing system for analyzing a flood operation for a hydrocarbon reservoir, the system comprising: at least one processor; and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to: establish a dataset of allowed well connections for at least one production well and at least one injection well of the hydrocarbon reservoir; iteratively modify the dataset of allowed well connections based on a distance category, a static features category, a dynamic features category, or any combination thereof, wherein the static features category uses static data of the hydrocarbon reservoir and the dynamic features category uses dynamic data of the hydrocarbon reservoir; and after iteratively modifying the dataset of allowed well connections, run capacitance resistance modeling using the modified dataset of allowed well connections as an input. 
     Comparing Different Flood Operations/PF 
     B1. A computer implemented method of analyzing at least a first flood operation and a second flood operation on a hydrocarbon reservoir having at least one production well and at least one injection well, the method comprising: for each of the first flood operation and the second flood operation: receiving production data for the at least one production well and injection data for the at least one injection well for the flood operation; running capacitance resistance modeling using the received production data and the received injection data for the flood operation to generate a response time and an interwell connectivity per injection well and production well pair for the flood operation; using the generated response times, the generated interwell connectivities, the received production data, the received injection data, or any combination thereof to generate a proxy of pore volume swept per injection well and production well pair for the flood operation; and aggregating the generated proxies of pore volume swept per injection well and production well pair for the flood operation to generate an estimate of pore volume swept at a well level, at a reservoir level, or both for the flood operation; and comparing the generated estimate of pore volume swept for the first flood operation and the generated estimate of pore volume swept for the second flood operation to determine a change in sweep efficiency at the well level, at the reservoir level, or both. 
     B2. The method of paragraph B1, further comprising, for each of the first flood operation and the second flood operation, determining heterogeneity at the well level, the reservoir level, or both. 
     B3. The method of paragraph B2, further comprising comparing the determined heterogeneity of the first flood operation and the determined heterogeneity of the second flood operation to determine a change in heterogeneity at the well level, at the reservoir level, or both. 
     B4. The method of paragraph B3, further comprising comparing the determined change in heterogeneity and the determined change in sweep efficiency to verify accuracy of the determined change in sweep efficiency. 
     B5. The method of paragraph B1, wherein the hydrocarbon reservoir includes a plurality of zones, and wherein each zone is treated as an injection well. 
     B6. The method of paragraph B1, wherein the production data includes production rate and flowing pressure data as a function of time, and wherein the injection data includes injection rate and flowing pressure data as a function of time. 
     B7. The method of paragraph B1, wherein the first flood operation, the second flood operation, or both is a polymer flood operation, further comprising accounting for rheology of the polymer used in the polymer flood operation in the running of the capacitance resistance modeling. 
     B8. The method of paragraph B7, wherein accounting for the rheology in running capacitance resistance modeling includes separating each injection well and production well pair of the polymer flood operation into at least three tanks, wherein the three tanks include a near injection well tank, a near production well tank, and a middle tank between the near injection well tank and the near production well tank. 
     B9. The method of paragraph B8, wherein the production data includes production rate and flowing pressure data as a function of time, and wherein the injection data includes injection rate and flowing pressure data as a function of time, and wherein accounting for the rheology in running capacitance resistance modeling includes using (i) material balance equations for each of the tanks, (ii) the injection and production rates, (iii) the injection and production flowing pressure data, and (iv) a polymer rheology. 
     B10. The method of paragraph B8, wherein accounting for the rheology in running capacitance resistance modeling includes using a capacitance resistance modeling polymer formulation: 
                     τ   _     j     ⁢     τ   j     ⁢     ∂     ∂   t       ⁢     (         ∂     (         q     p   ,   j     n     ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )         ∂   t       +       J   j     ⁢       ∂     P     wf   ,   j           ∂   t           )       +         τ   _     j     ⁢     ∂     ∂   t       ⁢     (         q     p   ,   j       ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )       +       (           τ   _     j     ⁢         J   _     j       J   j         +     τ   j       )     ⁢     (         ∂     (         q     p   ,   j     n     ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )         ∂   t       +       J   j     ⁢       ∂     P     wf   ,   j           ∂   t           )       +     (         q     p   ,   j       ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )       =       ∑     i   =   1     Ni     ⁢           ⁢       f     i   ,   j       ⁡     (         i     i   ,   p       ⁡     (   t   )       +       τ   i     ⁡     (         ∂       i     i   ,   p     n     ⁡     (   t   )           ∂   t       -       J   i     ⁢       ∂     P     wf   ,   i           ∂   t           )         )               
wherein τ i  is a time constant for the near injection well tank associated with injection well i, τ j  is a time constant for the near production well tank associated with production well j,  τ   j  is a time constant for the middle tank, J i  is an injectivity index for injection well i, J j  is a productivity index for production well j,  J   j  is a productivity index for the middle tank, P wf,j  is a well flowing pressure for production well j, P wf,i  is a well flowing pressure for injection well i, i i,p (t) is a flow rate of the polymer in injection well i, q p,j (t) is a polymer flow rate in production well j, q o,j (t) is an oil flow rate in production well j and n defines the polymer rheology.
 
     B11. A computing system for analyzing at least a first flood operation and a second flood operation on a hydrocarbon reservoir having at least one production well and at least one injection well, the system comprising: at least one processor; and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to perform a method comprising: for each of the first flood operation and the second flood operation: receiving production data for the at least one production well and injection data for the at least one injection well for the flood operation; running capacitance resistance modeling using the received production data and the received injection data for the flood operation to generate a response time and an interwell connectivity per injection well and production well pair for the flood operation; using the generated response times, the generated interwell connectivities, the received production data, the received injection data, or any combination thereof to generate a proxy of pore volume swept per injection well and production well pair for the flood operation; and aggregating the generated proxies of pore volume swept per injection well and production well pair for the flood operation to generate an estimate of pore volume swept at a well level, at a reservoir level, or both for the flood operation; and comparing the generated estimate of pore volume swept for the first flood operation and the generated estimate of pore volume swept for the second flood operation to determine a change in sweep efficiency at the well level, at the reservoir level, or both. 
     B12. The computing system of paragraph B11, wherein the computer executable instructions are further configured to, for each of the first flood operation and the second flood operation, determine heterogeneity at the well level, the reservoir level, or both. 
     B13. The computing system of paragraph B12, wherein the computer executable instructions are further configured to compare the determined heterogeneity of the first flood operation and the determined heterogeneity of the second flood operation to determine a change in heterogeneity at the well level, at the reservoir level, or both. 
     B14. The computing system of paragraph B13, wherein the computer executable instructions are further configured to compare the determined change in heterogeneity and the determined change in sweep efficiency to verify accuracy of the determined change in sweep efficiency. 
     B15. The computing system of paragraph B11, wherein the hydrocarbon reservoir includes a plurality of zones, and wherein each zone is treated as an injection well. 
     B16. The computing system of paragraph B11, wherein the first flood operation, the second flood operation, or both is a polymer flood operation, and wherein the computer executable instructions are further configured to account for rheology of the polymer used in the polymer flood operation in the running of the capacitance resistance modeling. 
     B17. A computer implemented method of analyzing a polymer flood operation on a hydrocarbon reservoir having at least one injection well and at least one production well, the method further comprising: receiving production data for the at least one production well and injection data for the at least one injection well for the flood operation; and running capacitance resistance modeling using the received production data and the received injection data for the polymer flood operation to generate a response time and an interwell connectivity per injection well and production well pair for the polymer flood operation, wherein running capacitance resistance modeling includes accounting for rheology of the polymer used in the polymer flood operation in the running of the capacitance resistance modeling. 
     B18. The method of paragraph B17, wherein accounting for the rheology in running capacitance resistance modeling includes separating each injection well and production well pair of the polymer flood operation into at least three tanks, wherein the three tanks include a near injection well tank, a near production well tank, and a middle tank between the near injection well tank and the near production well tank. 
     B19. The method of paragraph B18, wherein the production data includes production rate and flowing pressure data as a function of time, and wherein the injection data includes injection rate and flowing pressure data as a function of time, and wherein accounting for the rheology in running capacitance resistance modeling includes using (i) material balance equations for each of the tanks, (ii) the injection and production rates, (iii) the injection and production flowing pressure data, and (iv) a polymer rheology. 
     B20. The method of paragraph B18, wherein accounting for the rheology in running capacitance resistance modeling includes using a capacitance resistance modeling polymer formulation: 
                     τ   _     j     ⁢     τ   j     ⁢     ∂     ∂   t       ⁢     (         ∂     (         q     p   ,   j     n     ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )         ∂   t       +       J   j     ⁢       ∂     P     wf   ,   j           ∂   t           )       +         τ   _     j     ⁢     ∂     ∂   t       ⁢     (         q     p   ,   j       ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )       +       (           τ   _     j     ⁢         J   _     j       J   j         +     τ   j       )     ⁢     (         ∂     (         q     p   ,   j     n     ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )         ∂   t       +       J   j     ⁢       ∂     P     wf   ,   j           ∂   t           )       +     (         q     p   ,   j       ⁡     (   t   )       +       q     o   ,   j       ⁡     (   t   )         )       =       ∑     i   =   1     Ni     ⁢           ⁢       f     i   ,   j       ⁡     (         i     i   ,   p       ⁡     (   t   )       +       τ   i     ⁡     (         ∂       i     i   ,   p     n     ⁡     (   t   )           ∂   t       -       J   i     ⁢       ∂     P     wf   ,   i           ∂   t           )         )               
wherein τ i  is a time constant for the near injection well tank associated with injection well i, τ j  is a time constant for the near production well tank associated with production well j,  τ   j  is a time constant for the middle tank, J i  is an injectivity index for injection well i, J j  is a productivity index for production well j,  J   j  is a productivity index for the middle tank, P wf,j  is a well flowing pressure for production well j, P wf,i  is a well flowing pressure for injection well i, i i,p (t) is a flow rate of the polymer in injection well i, q p,j (t) is a polymer flow rate in production well j, q o,j (t) is an oil flow rate in production well j and n defines the polymer rheology.
 
     Pattern Management 
     C1. A computer implemented method of using producer centered polygons to identify at least one infill drilling location in a hydrocarbon reservoir having a plurality of producers and at least one injector, the method comprising: load well locations, reservoir boundary, and injection rate and production rate histories; creating producer-centered polygons based on the producer locations and the reservoir boundary; calculating an area (A i ) of any given producer by each polygon associated with each producer based on geometry or the geological boundary of the polygon; for each producer, calculating cumulative oil production (Qi) and a ratio between Qi and Ai; ranking the producers from smallest to largest Q/A ratio; and locating infill drilling places in the polygons with high-ranking producers. 
     C2. The method of paragraph C1, wherein the producer centered polygons are producer centered Voronoi polygons. 
     C3. The method of paragraph C1, wherein the area is a drainage area, a pore volume, a proxy for OOIP, or any combination thereof. 
     C4. A computer implemented method of using polygons to choose between a first infill candidate in a first hydrocarbon reservoir and a second infill candidate in a different second hydrocarbon reservoir, wherein each of the hydrocarbon reservoirs has a plurality of producers and at least one injector, the method comprising: for the first hydrocarbon reservoir: loading well locations, reservoir boundary, and injection rate and production rate histories; creating producer-centered polygons based on the producer locations and the reservoir boundary; calculating an area covered (Ai) by each polygon associated with each producer based on geometry of the polygon; for each producer, calculating cumulative oil production (Qi) and rank producers from largest Q to smallest Q; for each producer, calculating cumulative oil production (Qi) and a ratio between Qi and Ai; ranking the producers from smallest to largest Q/A ratio; calculating a norm area and norm cumulative oil production in the first reservoir; calculating an index of the uneven sweep (IUS) of the first reservoir. 
     C5. The method of paragraph C4, further comprising repeat steps of paragraph C1 for the second reservoir. 
     C6. The method of paragraph C5, further comprising selecting the reservoir for infill drilling opportunity with the largest IUS. 
     C7. The method of paragraph C4, wherein the producer centered polygons are producer centered Voronoi polygons. 
     C8. The method of paragraph C4, wherein the area is a drainage area, a pore volume, a proxy for OOIP, or any combination thereof. 
     C9. The method of paragraph C4, wherein the norm area is pore volume. 
     C10. A computer implemented method of using streamgrids for pattern realignment for a hydrocarbon reservoir having at least one producer and at least one injector, the method comprising: loading well locations and injection rate and production rate histories for all wells; creating streamgrid based on the well locations; calculating allocation factors for injector and producers based an allocation method; calculating allocated water injection and water production within each streamgrid between connected injector-producer pair) calculating water cycling between connected injector-producer pair for each streamgrid; defining a threshold for the water cycling based on a distribution; and identifying at least one streamgrid that has water cycling above the threshold for converting the producer in the identified streamgrid for pattern realignment 
     C11. The method of paragraph C10, wherein the allocation method is an injection angle, a producer angle, or any combination thereof. 
     C12. The method of paragraph C10, wherein the allocated water injection and water production within each streamgrid are calculated between connected injector-producer pair. 
     C13. A computer implemented method of using streamgrid allocation factors to initiate capacitance resistance modeling interwell connectivity, the method comprising: loading well locations and injection rate and production rate histories for all wells of a hydrocarbon reservoir having at least one producer and at least one injector; creating streamgrid based on well locations; calculating allocation factors for injector based on and allocation method; exporting the allocation factors as initial values of interwell connectivity between well pairs in the capacitance resistance modeling. 
     C14. The method of paragraph C13, wherein the allocation method is an injection angle, number of producers, injector Area, or any combination thereof. 
     C15. A computer implemented method of estimating maximal areal sweep by zone and by reservoir with populated streamgrids, the method comprising: for any given zone of a reservoir: getting the reservoir boundary in that zone and calculating its total area (St); getting contact point of all wells that penetrate and are perforated in that zone; creating streamgrid with well-zone contact locations; calculating total area (S) covered by populated streamgrids; and estimating maximal areal sweep efficiency in that zone, wherein the maximal areal sweep efficiency equals S/St. 
     C16. The method of paragraph C15, further comprising repeating steps of paragraph C1 for all remaining zones in the reservoir to get the S and St for each zone. 
     C17. The method of paragraph C16, further comprising calculating a ratio between summation of S from all zones and summation of St from all zones to get a maximum areal sweep efficiency for the reservoir. 
     Conformance Control 
     D1. A computer implemented method of identifying conformance candidates, the method comprising: for a first entity: solving an injection entity index to generate an injection entity index value, wherein the injection entity index includes injection efficiency, value of injected fluid, and pore volume injected for a period of time; solving a production entity index to generate a production entity index value, wherein the production entity index includes estimate of remaining movable oil in place; evaluating an operation entity index that represents operation status to generate an operation entity index value; and combining the injection entity index value, the production entity index value, and the operation entity index value to generate a conformance problem index value for the first entity. 
     D2. The method of paragraph D1, further comprising comparing the generated conformance problem index value for the first and a threshold to determine if the first entity is a conformance candidate. 
     D3. The method of paragraph D1, further comprising generating a conformance problem index value for at least one other entity. 
     D4. The method of paragraph D3, further comprising ranking the first entity and the at least one other entity based on the generated conformance problem index values. 
     D5. The method of paragraph D4, wherein a higher generated conformance problem index value indicates a higher likelihood of a conformance problem. 
     D6. The method of paragraph D4, wherein the first entity has the highest generated conformance problem index value, and wherein the first entity is at a field level, further comprising generating a conformance problem index value at a reservoir level, well level, a zone level, or any combination thereof. 
     D7. The method of paragraph D4, wherein the first entity has the highest generated conformance problem index value, and wherein the first entity is at a reservoir level, further comprising generating a conformance problem index value at a well level, a zone level, or any combination thereof. 
     D8. The method of paragraph D4, wherein the first entity has the highest generated conformance problem index value, and wherein the first entity is at a well level, further comprising generating a conformance problem index value at a zone level. 
     D9. The method of paragraph D1, wherein the first entity is at a field level, a reservoir level, a well level, a zone level, or any combination thereof. 
     D10. The method of paragraph D4, wherein the first entity has the highest generated conformance problem index value, and wherein the first entity includes a plurality of zones, further comprising: receiving data for each zone of the plurality of zones, the received data to be used to determine a residence time distribution for each zone; determining the residence time distribution for each zone of the plurality of zones using the received data; identifying at least one zone of the plurality of zones to be treated with a conformance agent by comparing the determined residence time distributions of the zones and a residence time distribution threshold; and recommending a first conformance control treatment at breakthrough time of the slowest identified zone for the at least one identified zone. 
     D11. The method of paragraph D10, further comprising recommending a slug size for the first conformance control treatment, wherein the slug size is determined by calculating injected volume for a period of time which is not more than 50% of a breakthrough time for the fastest identified zone. 
     D12. The method of paragraph D10, further comprising recommending a concentration of the conformance agent for the first conformance control treatment, wherein the concentration of the conformance agent is based on a resistance factor of the conformance agent and a rheology of the conformance agent. 
     D13. The method of paragraph D10, further comprising: receiving updated data for each zone of the plurality of zones after the first conformance control treatment; determining an updated residence time distribution for each zone of the plurality of zone after the first conformance control treatment; identifying at least one zone of the plurality of zones after the first conformance control treatment to be treated with a conformance agent in a second conformance control treatment based on a comparison of the updated residence time distribution of each zone and the residence time distribution threshold; and recommending a second conformance control treatment at an updated breakthrough time of the slowest identified zone after the first conformance control treatment for the at least one identified zone after the first conformance control treatment. 
     D14. A computer implemented method of determining a conformance control treatment for a well of a hydrocarbon reservoir, wherein the well is in fluidic communication with a plurality of zones of the hydrocarbon reservoir, the method comprising: receiving data for each zone of the plurality of zones, the received data to be used to determine a residence time distribution for each zone; determining the residence time distribution for each zone of the plurality of zones using the received data; identifying at least one zone of the plurality of zones to be treated with a conformance agent by comparing the determined residence time distributions of the zones and a residence time distribution threshold; and recommending a first conformance control treatment at breakthrough time of the slowest identified zone for the at least one identified zone. 
     D15. The method of paragraph D14, further comprising recommending a slug size for the first conformance control treatment, wherein the slug size is determined by calculating injected volume for a period of time which is not more than 50% of a breakthrough time for the fastest identified zone. 
     D16. The method of paragraph D14, further comprising recommending a concentration of the conformance agent for the first conformance control treatment, wherein the concentration of the conformance agent is based on a resistance factor of the conformance agent and a rheology of the conformance agent. 
     D17. The method of paragraph D16, wherein the resistance factor of the conformance agent is sufficient to reduce original velocity of the fastest identified zone by a factor of about 10. 
     D18. The method of paragraph D14, further comprising: receiving updated data for each zone of the plurality of zones after the first conformance control treatment; determining an updated residence time distribution for each zone of the plurality of zone after the first conformance control treatment; identifying at least one zone of the plurality of zones after the first conformance control treatment to be treated with a conformance agent in a second conformance control treatment based on a comparison of the updated residence time distribution of each zone and the residence time distribution threshold; and recommending a second conformance control treatment at an updated breakthrough time of the slowest identified zone after the first conformance control treatment for the at least one identified zone after the first conformance control treatment. 
     D19. The method of paragraph D18, further comprising recommending a slug size for the second conformance control treatment, wherein the slug size for the second conformance control treatment is determined by calculating injected volume for a period of time which is not more than 50% of an updated breakthrough time for the fastest identified zone. 
     D20. The method of paragraph D18, further comprising recommending a concentration of the conformance agent for the second conformance control treatment, wherein the concentration of the conformance agent for the second conformance control treatment is based on a resistance factor of the conformance agent for the second conformance control treatment and a rheology of the conformance agent for the second conformance control treatment. 
     D21. The method of paragraph D20, wherein the resistance factor of the conformance agent of the second conformance control treatment is sufficient to reduce updated velocity of the fastest identified zone after the first conformance control treatment by a factor of about 10. 
     D22. A computing system for identifying conformance candidates, the system comprising: at least one processor; and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to perform a method comprising: for a first entity: solving an injection entity index to generate an injection entity index value, wherein the injection entity index includes injection efficiency, value of injected fluid, and pore volume injected for a period of time, solving an production entity index to generate an production entity index value, wherein the production entity index includes estimate of remaining movable oil in place; evaluating an operation entity index that represents operation status to generate an operation entity index value; and combining the injection entity index value, production entity index value, and the operation entity index value to generate a conformance problem index value for the first entity. 
     Heavy Oil Flood Operation 
     E1. A computer implemented method of analyzing a flood operation on a hydrocarbon reservoir having heavy oil, the method comprising: for a first injection well of the hydrocarbon reservoir: receiving injection rate data and production rate data for production wells communicating with the first injection well for a period of time; establishing a plurality of time windows based on breakthrough time of each production well that has broken through until the current date, wherein the production rate data is indicative of breakthrough; running capacitance resistance modeling for each established time window to generate interwell connectivities for each well pair; solving a continuous function for each well pair using the capacitance resistance modeling generated interwell connectivity of the well pair over time to estimate interwell connectivity for each well pair in the future; using the production rate data to solve an oil-cut model for each production well that has broken through to estimate oil-cut value for each production well in the future; and determining a value of injected fluid for the first injection well as a function of time in the future using the continuous function estimated interwell connectivity and the estimated oil-cut value. 
     E2. The method of paragraph E1, further comprising: establishing at least one sliding scale time window between two established time windows; and running the capacitance resistance modeling for each established sliding time window to generate interwell connectivities for each well pair, wherein the generated interwell connectivities derived from the sliding time window are included in the solving of the continuous function. 
     E3. The method of paragraph E1, wherein the continuous function is bell shaped, log-normal, betta, logistic curve, or any combination thereof. 
     E4. The method of paragraph E1, further comprising comparing the generated average forecast value of injected fluid for the first injection well and a threshold to determine if the first injection well is a candidate for stimulation. 
     E5. The method of paragraph E1, further comprising generating an average forecast value of injected fluid for at least one other injection well. 
     E6. The method of paragraph E5, further comprising ranking the first injection well and the at least one other injection well based on the generated average forecast values of injected fluid. 
     E7. The method of paragraph E1, wherein all of the productions wells communication with the first injection well have broken through. 
     E8. The method of paragraph E1, wherein less than all of the productions wells communication with the first injection well have broken through. 
     E9. The method of paragraph E1, wherein determining the value of injected fluid as a function of time for the first injection well includes using an equation, wherein the equation is: VOIF(t)=Σ j   N  f ij (t) f oj  (t) wherein f oj  (t) is oil cut as a function of time and f ij  (t) is interwell connectivity as a function of time. 
     E10. A computing system for analyzing a flood operation on a hydrocarbon reservoir having heavy oil, the system comprising: at least one processor; and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to perform a method comprising: for a first injection well of the hydrocarbon reservoir: receiving injection rate data and production rate data for production wells communicating with the first injection well for a period of time; establishing a plurality of time windows based on breakthrough time of each production well that has broken through until the current date, wherein the production rate data is indicative of breakthrough; running capacitance resistance modeling for each established time window to generate interwell connectivities for each well pair; solving a continuous function for each well pair using the capacitance resistance modeling generated interwell connectivity of the well pair over time to estimate interwell connectivity for each well pair in the future; using the production rate data to solve an oil-cut model for each production well that has broken through to estimate oil-cut value for each production well in the future; and determining a value of injected fluid for the first injection well as a function of time in the future using the continuous function estimated interwell connectivity and the estimated oil-cut value. 
     E11. The system of paragraph E10, wherein the computer executable instructions are further configured to establish at least one sliding scale time window between two established time windows; and run the capacitance resistance modeling for each established sliding time window to generate interwell connectivities for each well pair, wherein the generated interwell connectivities derived from the sliding time window are included in the solving of the continuous function. 
     E12. The system of paragraph E10, wherein the continuous function is bell shaped, log-normal, betta, logistic curve, or any combination thereof. 
     E13. The system of paragraph E10, wherein the computer executable instructions are further configured to compare the generated average forecast value of injected fluid for the first injection well and a threshold to determine if the first injection well is a candidate for stimulation. 
     E14. The system of paragraph E10, wherein the computer executable instructions are further configured to generate an average forecast value of injected fluid for at least one other injection well. 
     E15. The system of paragraph E14, wherein the computer executable instructions are further configured rank to the first injection well and the at least one other injection well based on the generated average forecast values of injected fluid. 
     E16. The system of paragraph E10, wherein all of the productions wells communication with the first injection well have broken through. 
     E17. The system of paragraph E10, wherein less than all of the productions wells communication with the first injection well have broken through. 
     E18. The system of paragraph E10, wherein determining the value of injected fluid as a function of time for the first injection well includes using an equation, wherein the equation is: VOIF(t)=Σ j   N  f ij (t) f oj  (t) 
     wherein f oj  (t) is oil cut as a function of time and f ij  (t) is interwell connectivity as a function of time. 
     E19. A computer-readable storage medium comprising computer-executable instructions which, when executed by a computing system, cause the computing system to perform a method of analyzing a flood operation on a hydrocarbon reservoir having heavy oil, the method comprising: for a first injection well of the hydrocarbon reservoir: receiving injection rate data and production rate data for production wells communicating with the first injection well for a period of time; establishing a plurality of time windows based on breakthrough time of each production well that has broken through until the current date, wherein the production rate data is indicative of breakthrough; running capacitance resistance modeling for each established time window to generate interwell connectivities for each well pair; solving a continuous function for each well pair using the capacitance resistance modeling generated interwell connectivity of the well pair over time to estimate interwell connectivity for each well pair in the future; using the production rate data to solve an oil-cut model for each production well that has broken through to estimate oil-cut value for each production well in the future; and determining a value of injected fluid for the first injection well as a function of time in the future using the continuous function estimated interwell connectivity and the estimated oil-cut value. 
     E20. The computer-readable storage medium of paragraph E19, further comprising generating an average forecast value of injected fluid for at least one other injection well; and ranking the first injection well and the at least one other injection well based on the generated average forecast values of injected fluid. 
     VOIF 
     F1. A computer implemented method of determining a value of injected fluid for a flood operation on a hydrocarbon reservoir, the method comprising: for a first injection well of the hydrocarbon reservoir: receiving injection rate data for the first injection well and production rate data for at least one production well communicating with the injection well; running capacitance resistance modeling using the received data to generate an interwell connectivity for each injection well and production well pair; determining a value of injected fluid for each injection well and production well pair using the generated interwell connectivity for the well pair and an oil-cut value for the production well of the pair; and aggregating the generated values of injected fluid per pair to determine a value of injected fluid for the first injection well. 
     F2. The method of paragraph F1, further comprising generating a value of injected fluid for at least one other injection well. 
     F3. The method of paragraph F2, further comprising ranking the first injection well and the at least one other injection well based on the values of injected fluid. 
     F4. The method of paragraph F3, further comprising generating a net value of injected fluid for the first injection well and the at least one other injection well. 
     F5. The method of paragraph F4, further comprising re-ranking the first injection well and the at least one other injection well based on the net values of injected fluid. 
     F6. The method of paragraph F1, wherein the value of injected fluid for the first injection well is determined at a steady state. 
     F7. The method of paragraph F1, wherein the value of injected fluid for the first injection well is determined as transient. 
     F8. The method of paragraph F1, wherein the value of injected fluid for the first injection well is determined using sensitivity analysis. 
     F9. The method of paragraph F1, wherein the hydrocarbon reservoir may include a plurality of zones, and each zone is treated as an injection well. 
     F10. The method of paragraph F1, further comprising recommending a conformance candidate or a stimulation candidate. 
     F11. The method of paragraph F1, further comprising recommending a target injection rate. 
     F12. A computing system for determining a value of injected fluid for a flood operation on a hydrocarbon reservoir, the system comprising: at least one processor; and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to perform a method comprising: for a first injection well of the hydrocarbon reservoir: receiving injection rate data for the first injection well and production rate data for at least one production well communicating with the injection well; running capacitance resistance modeling using the received data to generate an interwell connectivity for each injection well and production well pair; determining a value of injected fluid for each injection well and production well pair using the generated interwell connectivity for the well pair and an oil-cut value for the production well of the pair; and aggregating the generated values of injected fluid per pair to determine a value of injected fluid for the first injection well. 
     F13. The system of paragraph F12, wherein the computer executable instructions are further configured to generate a value of injected fluid for at least one other injection well. 
     F14. The system of paragraph F13, wherein the computer executable instructions are further configured to rank the first injection well and the at least one other injection well based on the values of injected fluid. 
     F15. The system of paragraph F14, wherein the computer executable instructions are further configured to generate a net value of injected fluid for the first injection well and the at least one other injection well. 
     F16. The system of paragraph F15, wherein the computer executable instructions are further configured to re-rank the first injection well and the at least one other injection well based on the net values of injected fluid. 
     F17. The system of paragraph F12, wherein the value of injected fluid for the first injection well is determined at a steady state, as transient, using sensitivity analysis, or any combination thereof. 
     F18. The system of paragraph F12, wherein the hydrocarbon reservoir may include a plurality of zones, and each zone is treated as an injection well. 
     F19. The system of paragraph F12, further comprising recommending a conformance candidate, a stimulation candidate, a target injection rate, or any combination thereof. 
     F20. A computer-readable storage medium comprising computer-executable instructions which, when executed by a computing system, cause the computing system to perform a method of determining a value of injected fluid for a flood operation on a hydrocarbon reservoir, the method comprising: for a first injection well of the hydrocarbon reservoir: receiving injection rate data for the first injection well and production rate data for at least one production well communicating with the injection well; running capacitance resistance modeling using the received data to generate an interwell connectivity for each injection well and production well pair; determining a value of injected fluid for each injection well and production well pair using the generated interwell connectivity for the well pair and an oil-cut value for the production well of the pair; and aggregating the generated values of injected fluid per pair to determine a value of injected fluid for the first injection well. 
     CRM Zonal 
     G1. A computer implemented method for analyzing a flood operation for a hydrocarbon reservoir having a plurality of zones, the method comprising: receiving injection profile data (ILT) and injection rates; using the received injection profile data and injection rates to split each injection well into multiple zonal level injectors; and running capacitance resistance modeling treating each zonal level injector as a single injector, wherein running capacitance resistance modeling includes generating interwell connectivities at the zonal level. 
     G2. The method of paragraph G1, further comprising determining if there is crossflow between at least two zones. 
     G3. The method of paragraph G1, further comprising generating at least one PLT. 
     G4. The method of paragraph G1, further comprising generating continuous production profile (PLT). 
     G5. The method of paragraph G4, wherein continuous production profile (PLT) is generated if secondary recovery is contributing in production, which can be obtained from injection rates and zonal level connectivities by summing up contribution of zonal injectors and their connectivities to at least one of the producers. 
     G6. The method of paragraph G4, wherein continuous production profile from primary and secondary production is obtained by using the primary and secondary recovery portion of CRM estimate once the CRM zonal is performed. 
     G7. The method of paragraph G6, wherein the primary portion of production profile is estimated from CRM zonal level primary portion which is an exponential decline. 
     G8. The method of paragraph G6, wherein the secondary portion of production rates at zonal level are obtained from summing the multiplication of injection rates and connectivities at zonal level from all injectors contributing in the production of a given producer. 
     G9. The method of paragraph G1, further comprising interpolating for PLT, ILT, or both. 
     G10. A computing system for analyzing a flood operation for a hydrocarbon reservoir having a plurality of zones, the system comprising: at least one processor; and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to perform a method comprising: receiving injection profile data (ILT) and injection rates; using the received injection profile data and injection rates to split each injection well into multiple zonal level injectors; and running capacitance resistance modeling treating each zonal level injector as a single injector, wherein running capacitance resistance modeling includes generating interwell connectivities at the zonal level. 
     G11. The system of paragraph G10, wherein the computer executable instructions are further configured to generate a value of injected fluid for at least one other injection well. 
     G12. The system of paragraph G10, wherein the computer executable instructions are further configured to determine if there is crossflow between at least two zones. 
     G13. The system of paragraph G10, wherein the computer executable instructions are further configured to generate at least one PLT. 
     G14. The system of paragraph G10, wherein the computer executable instructions are further configured to generate continuous production profile (PLT). 
     G15. The system of paragraph G14, wherein the continuous production profile (PLT) is generated if secondary recovery is contributing in production, which can be obtained from injection rates and zonal level connectivities by summing up contribution of zonal injectors and their connectivities to at least one of the producers. 
     G16. The system of paragraph G14, wherein the continuous production profile from primary and secondary production is obtained by using the primary and secondary recovery portion of CRM estimate once the CRM zonal is performed. 
     G17. The system of paragraph G16, wherein the primary portion of production profile is estimated from CRM zonal level primary portion which is an exponential decline, 
     G18. The system of paragraph G16, wherein the secondary portion of production rates at zonal level are obtained from summing the multiplication of injection rates and connectivities at zonal level from all injectors contributing in the production of a given producer. 
     G19. The system of paragraph G10, wherein the computer executable instructions are further configured to interpolate for PLT, ILT, or both. 
     G20. A computer-readable storage medium comprising computer-executable instructions which, when executed by a computing system, cause the computing system to perform a method of analyzing a flood operation for a hydrocarbon reservoir having a plurality of zones, the method comprising: receiving injection profile data (ILT) and injection rates; using the received injection profile data and injection rates to split each injection well into multiple zonal level injectors; and running capacitance resistance modeling treating each zonal level injector as a single injector, wherein running capacitance resistance modeling includes generating interwell connectivities at the zonal level. 
     from first provisional patent application: 
     H1. A computer implemented method of using Capacitance Resistance Model (CRM) to identify candidate injector wells, the method comprising: receiving injector data for a plurality of injector wells, producer data for a plurality of producer wells, and flowing pressure data as well as relative bottomhole location, injection and production profile data, polygons indicating geo-bodies, geological boundaries or structural boundaries such as fault or fractures from 3D seismic and/or 4D seismic data; pressure transient testing data including any of pulse test, interwell tracer information indicating well pair in communication; receiving phase data for phases; performing CRM analysis using the received data; repeating the CRM analysis; identifying a plurality of candidate injector wells from injector well properties including any of value of injection, priorities workover, conformance control characteristics both in areal and vertical sweep efficiency; and determining at least a candidate injector well from the plurality of injector wells. 
     H2. The method of paragraph H1, further comprising identifying a plurality of candidate injector wells and a plurality of producer wells responsive to connectivity maps. 
     H3. The method of paragraph H2, wherein the at least a candidate injector well is determined from the plurality of injector wells and the plurality of producer wells. 
     H4. The method of paragraph H1, further comprising ranking the plurality of candidate injector wells. 
     H5. The method of paragraph H1, wherein performing CRM analysis comprises performing sensitivity analysis by a) adding noise based on measurement accuracy to injection, production and pressure data to generate many data set, b) representing different earth model by allowing or limiting connectivities compared to a base case model; c) perform sliding time window based analysis to attain interactivity as a function of time; and d) screening connectivities based on their confidence level. 
     H6. The method of paragraph H1, wherein the connectivity maps indicate high connectivity of &gt;0.5, with high confidence as demonstrated by coefficient of variation less than 10%, and wherein the connectivities that exist are common in all history-matched model with similar history-matched error post adding noise to the input data. 
     H7. The method of paragraph H6, further comprising identifying the candidate injector well zone, wells and area as targets for conformance operations. 
     H8. The method of paragraph H1, wherein the connectivity maps indicate low connectivity (&gt;0.25), with high confidence as demonstrated by coefficient of variation less than 10%, and wherein the connectivities that exist are common in all history-matched model with similar history-matched error post adding noise to the input data. 
     H9. The method of paragraph H8, further comprising identifying the candidate injector well zone, wells and area as targets for enhanced oil recovery operations. 
     H10. The method of paragraph H9, wherein the enhanced oil recovery operations include at least one of chemical flooding or polymer flooding. 
     H11. The method of paragraph H1, wherein the injector data includes injector profile data over time, wherein the injector profile data includes a distribution of injection for each zone. 
     H12. The method of paragraph H1, wherein the producer data includes producer profile data over time, wherein the producer profile data includes a distribution of production for each zone. 
     H13. The method of paragraph H1, further comprising receiving at least one of geological data, tracer data, or pulse test data; and using it in one or more of the analyses. 
     H14. The method of paragraph H1, wherein the phase data includes at least one of total injected fluid. 
     H15. The method of paragraph H14, wherein the injected fluid is selected from any of water, hydrocarbons (gas or oil); CO 2 , N 2 , and combinations thereof. 
     H16. The method of paragraph H15, wherein the injected fluid is injected in sequence, and alternating between different injection fluids. 
     H17. The method of paragraph H15, wherein the injected fluid is sour gas. 
     H18. The method of paragraph H1, further comprising determine an injection value for all injectors, and wherein the injector value for each injector indicates the amount of oil being produced in offset producers by injecting in the injector. 
     H19. The method of paragraph H18, wherein the injector value is a measure of ranking, prioritizing work over, acid jobs, increasing injection to improve reservoir performance. 
     H20. The method of paragraph H19, further comprising quantifying contribution of injectors as a function of oil production to evaluate external factors. 
     H21. The method of paragraph H20, wherein the external factor is from an aquifer as a driving force. 
     H22. The method of paragraph H20, further comprising identifying an injector well with the highest injection value. 
     H23. A computer implemented method of generating zonal split data, the method comprising: receiving first profile from either ILT (injection logging tools) or PLT (production logging tools) data for a first date; receiving a second ILT/PLT data for a second date; and interpolating between the first PLT data and the second PLT data to dynamically generate zonal split data between the first date and the second date. 
     H24. A computer implemented method of generating zonal split data, the method comprising: receiving first ILT data for a first date; receiving second ILT data for a second date; and interpolating between the first ILT data and the second ILT data to dynamically generate zonal split data between the first date and the second date. 
     H25. The method of paragraph H24, further comprising generating a continuous production profile based on interwell connectivity model obtained using CRM at zonal level. 
     H26. A method performing flooding analysis, the method comprising using a combination of CRM, a zonal splits generator, and a streamgrid. 
     H27. A method performing flooding analysis, the method comprising: using interpreted seismic data (3D or 4D) to condition CRM connectivity model to the more likely scenario of the earth model and potential communicating well pairs indicating location of geological or pressure barriers. 
     H28. The method of paragraph H27, further comprising identifying interwell cumulative injected fluid agreement with saturation change from 4D seismic data during history matching. 
     H29. A method performing flooding analysis, the method comprising dynamically generating and redefining patterns for a plurality of injector wells and a plurality of producer wells. 
     H30. The method of paragraph H29, further comprising using at least one of Delaunay triangulation or Voronoi. 
     from second provisional patent application: 
     I1. A computer implemented method of analyzing a flooding operation for a subterranean hydrocarbon bearing reservoir, the method comprising: establishing a dataset of allowed well connections for a plurality of injection wells and production well of the subterranean reservoir; iteratively modifying the dataset of allowed well connections based on a distance category, a static features category, a dynamic features category, or any combination thereof, wherein the static features category uses static data of the subterranean reservoir and the dynamic features category uses dynamic data of the subterranean reservoir; and after iteratively modifying the dataset of allowed well connections, running capacitance resistive modeling (CRM) using the modified dataset of allowed well connections as an input. 
     I2. The method of paragraph I1, wherein iteratively modifying the dataset of allowed well connections according to a priority, wherein the priority includes using an order, wherein the order is the distance category first, the static features category second, and the dynamic features category third. 
     I3. The method of paragraph I1, wherein iteratively modifying the dataset of allowed well connections according to a priority, wherein the priority includes using an order, wherein the order is the dynamic features category first, the static features second, and the distance category third. 
     I4. The method of paragraph I1, wherein there is a priority within the distance category. 
     I5. The method of paragraph I1, wherein there is a priority within the static features category. 
     I6. The method of paragraph I1, wherein there is a priority within the dynamic features category. 
     I7. The method of paragraph I1, wherein the static data includes at least one of geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, or a processed version of any of these. 
     I8. The method of paragraph I1, wherein the dynamic data includes at least one of well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, a processed version of any of these, or any combination thereof. 
     I9. The method of paragraph I1, wherein running capacitance resistive modeling (CRM) is constrained by at least one geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, a processed version of any of these, or any combination thereof. 
     I10. The method of paragraph I1, further comprising updating at least one application or component based on output from the running of CRM. 
     I11. The method of paragraph I1, wherein modifying the dataset of allowed well connections includes modifying a value of an allowed well connection in response to receiving user input indicating that the value should be changed. 
     I12. A computer system for analyzing a flooding operation for a subterranean hydrocarbon bearing reservoir, the system comprising: at least one processor; and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to perform the method of paragraph I1. 
     I13. A computer program product including a non-transitory computer-readable medium having computer-readable code on it, the computer readable code being configured to implement the method of paragraph I1. 
     I14. A computer implemented method of analyzing a flooding operation for a subterranean hydrocarbon bearing reservoir, the method comprising: determining a dataset of allowed well connections for a plurality of injection wells and production well of the subterranean reservoir based on a distance category; determining a dataset of allowed well connections for a plurality of injection wells and production well of the subterranean reservoir based on a static features category, wherein the static features category uses static data of the subterranean reservoir; determining a dataset of allowed well connections for a plurality of injection wells and production well of the subterranean reservoir based on a static features category, wherein the dynamic features category uses dynamic data of the subterranean reservoir; comparing the determined well connections; combining the determined well connections based on the comparisons and a priority to determine allowed well connections; and running capacitance resistive modeling (CRM) using the allowed well connections as an input. 
     I15. The method of paragraph I14, wherein the priority includes the dynamic features category having the highest priority, the static features category having the next highest priority, and the distance category having the next highest priority. 
     I16. The method of paragraph I14, wherein there is a priority within the distance category. 
     I17. The method of paragraph I14, wherein there is a priority within the static features category. 
     I18. The method of paragraph I14, wherein there is a priority within the dynamic features category. 
     I19. The method of paragraph I14, wherein the static data includes at least one of geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, or a processed version of any of these. 
     I20. The method of paragraph I14, wherein the dynamic data includes at least one of well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, a processed version of any of these, or any combination thereof. 
     I21. The method of paragraph I14, wherein running capacitance resistive modeling (CRM) is constrained by at least one geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, a processed version of any of these, or any combination thereof. 
     I22. The method of paragraph I14, further comprising updating at least one application or component based on output from the running of CRM. 
     I23. The method of paragraph I14, further comprising modifying a value of an allowed well connection in response to receiving user input indicating that the value should be changed. 
     I24. A computer system for analyzing a flooding operation for a subterranean hydrocarbon bearing reservoir, the system comprising: at least one processor; and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to perform the method of paragraph I14. 
     I25. A computer program product including a non-transitory computer-readable medium having computer-readable code on it, the computer readable code being configured to implement the method of paragraph I14. 
     I26. A computer implemented method of analyzing a flooding operation for a subterranean hydrocarbon bearing reservoir, the method comprising: receiving field data for the subterranean reservoir; processing the field data for the subterranean reservoir; determining allowed well connections; and running capacitance resistive modeling (CRM) using the allowed well connections as an input. 
     I27. The method of paragraph I26, wherein determining allowed well connections includes using at least one of geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, a processed version of any of these, or any combination thereof. 
     I28. The method of paragraph I26, wherein running capacitance resistive modeling (CRM) is constrained by at least one geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, a processed version of any of these, or any combination thereof. 
     I29. The method of paragraph I26, further comprising updating at least one application or component based on output from the running of CRM. 
     I30. A computer system for analyzing a flooding operation for a subterranean hydrocarbon bearing reservoir, the system comprising: at least one processor; and at least one memory containing computer executable instructions, that when executed by the at least one processor, cause the computing system to perform the method of paragraph I26. 
     I31. A computer program product including a non-transitory computer-readable medium having computer-readable code on it, the computer readable code being configured to implement the method of paragraph I26. 
     I32. A method of analyzing a flooding operation for a subterranean hydrocarbon bearing reservoir, the method comprising: injecting material into the reservoir for the flooding operation; determining allowed well connections from data from the reservoir, wherein the allowed well connections are input to capacitance resistive modeling (CRM); and making at least one decision that is likely to improve the flooding operation based on the output of CRM. 
     I33. The method of paragraph I32, wherein the at least one decision is related to optimization, history matching, conformance control, changing a parameter of the flooding operation, or any combination thereof. 
     I34. The method of paragraph I32, wherein determining the allowed well connections includes using at least one of geological data, seismic data, three dimensional (3D) seismic data, faults, fractures, geobodies, well production data, injection rate data, pressure data, location data, fluid production geochemistry data, tracer data, log data, production log data, well log data, well test data, time lapse seismic data, four dimensional (4D) seismic data, maps of change in pressure and saturation, maps of pressure and saturation, preliminary well connections, pressure transient, pulse test, a processed version of any of these, or any combination thereof. 
     I35. The method of paragraph I32, wherein determining the allowed well connections includes: establishing a dataset of allowed well connections for a plurality of injection wells and production well of the subterranean reservoir; modifying the dataset of allowed well connections based on a distance category, a static features category, a dynamic features category, or any combination thereof, wherein the static features category uses static data of the subterranean reservoir and the dynamic features category uses dynamic data of the subterranean reservoir, wherein the modified dataset provides the allowed well connections that are input to capacitance resistive modeling (CRM). 
     I36. The method of paragraph I32, wherein determining the allowed well connections includes: determining a dataset of allowed well connections for a plurality of injection wells and production well of the subterranean reservoir based on a distance category; determining a dataset of allowed well connections for a plurality of injection wells and production well of the subterranean reservoir based on a static features category, wherein the static features category uses static data of the subterranean reservoir; determining a dataset of allowed well connections for a plurality of injection wells and production well of the subterranean reservoir based on a static features category, wherein the dynamic features category uses dynamic data of the subterranean reservoir; comparing the determined well connections; and combining the determined well connections based on the comparisons and a priority to determine allowed well connections.