Patent ID: 12209492

DETAILED DESCRIPTION

The present disclosure relates to subterranean operations and, more particularly, embodiments disclosed herein provide methods and systems for identifying asphaltenes in a wellbore fluid sample downhole. This may allow for the construction of an asphaltene onset pressure (AOP) map. An AOP map may allow for and aid in determining reservoir simulation and production analysis and decisions with measurements performed downhole. Additionally, an AOP map produced with downhole measurements may be combined with Lab analysis AOP to interpolate AOP to positions not measured, and correlate with other physical or chemical properties of the fluid in order to provide production interpretation information and make production decisions.

The fluid sampling tools, systems and methods described herein may be used with any of the various techniques employed for evaluating a well, including without limitation wireline formation testing (WFT), measurement while drilling (MWD), and logging while drilling (LWD). The various tools and sampling units described herein may be delivered downhole as part of a wireline-delivered downhole assembly or as a part of a drill string. It should also be apparent that given the benefit of this disclosure, the apparatuses and methods described herein have applications in downhole operations other than drilling and may also be used after a well is completed.

FIG.1is a schematic diagram of downhole fluid sampling tool100on a conveyance102. As illustrated, wellbore104may extend through subterranean formation106. In examples, reservoir fluid may be contaminated with well fluid (e.g., drilling fluid) from wellbore104. As described herein, the fluid sample may be analyzed to determine fluid contamination and other fluid properties of the reservoir fluid. As illustrated, a wellbore104may extend through subterranean formation106. While the wellbore104is shown extending generally vertically into the subterranean formation106, the principles described herein are also applicable to wellbores that extend at an angle through the subterranean formation106, such as horizontal and slanted wellbores. For example, althoughFIG.1shows a vertical or low inclination angle well, high inclination angle or horizontal placement of the well and equipment is also possible. It should further be noted that whileFIG.1generally depicts a land-based operation, those skilled in the art will readily recognize that the principles described herein are equally applicable to subsea operations that employ floating or sea-based platforms and rigs, without departing from the scope of the disclosure.

As illustrated, a hoist108may be used to run downhole fluid sampling tool100into wellbore104. Hoist108may be disposed on a vehicle110. Hoist108may be used, for example, to raise and lower conveyance102in wellbore104. While hoist108is shown on vehicle110, it should be understood that conveyance102may alternatively be disposed from a hoist108that is installed at surface112instead of being located on vehicle110. Downhole fluid sampling tool100may be suspended in wellbore104on conveyance102. Other conveyance types may be used for conveying downhole fluid sampling tool100into wellbore104, including coiled tubing and wired drill pipe, for example. Downhole fluid sampling tool100may comprise a tool body114, which may be elongated as shown onFIG.1. Tool body114may be any suitable material, including without limitation titanium, stainless steel, alloys, plastic, combinations thereof, and the like. Downhole fluid sampling tool100may further include one or more sensors116for measuring properties of the fluid sample, reservoir fluid, wellbore104, subterranean formation106, or the like. In examples, downhole fluid sampling tool100may also include a fluid analysis module118, which may be operable to process information regarding fluid sample, as described below. The downhole fluid sampling tool100may be used to collect fluid samples from subterranean formation106and may obtain and separately store different fluid samples from subterranean formation106.

In examples, fluid analysis module118may comprise at least one a sensor that may continuously monitor a reservoir fluid. Such sensors include optical sensors, acoustic sensors, electromagnetic sensors, conductivity sensors, resistivity sensors, selective electrodes, density sensors, mass sensors, thermal sensors, chromatography sensors, viscosity sensors, bubble point sensors, fluid compressibility sensors, flow rate sensors, pressure sensors, and/or nuclear magnetic resonance (NMR) sensors, microfluidic sensor including but not limited to microfluidic pressure, volume, and temperature (PVT) phase behavior sensors. Sensors may measure a contrast between drilling fluid filtrate properties and formation fluid properties. Fluid analysis module118may be operable to derive properties and characterize the fluid sample. By way of example, fluid analysis module118may measure absorption, transmittance, or reflectance spectra and translate such measurements into component concentrations of the fluid sample, which may be lumped component concentrations, as described above. The fluid analysis module118may also measure gas-to-oil ratio, fluid composition, water cut, live fluid density, live fluid viscosity, formation pressure, and formation temperature. Fluid analysis module118may also be operable to determine fluid contamination of the fluid sample and may include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. The absorption, transmittance, or reflectance spectra absorption, transmittance, or reflectance spectra may be measured with sensors116by way of standard operations. For example, fluid analysis module118may include random access memory (RAM), one or more processing units, such as a central processing unit (CPU), or hardware or software control logic, ROM, and/or other types of nonvolatile memory.

Any suitable technique may be used for transmitting signals from the downhole fluid sampling tool100to the surface112. As illustrated, a communication link120(which may be wired or wireless, for example) may be provided that may transmit data from downhole fluid sampling tool100to an information handling system122at surface112. Information handling system122may include a processing unit124, a monitor126, an input device128(e.g., keyboard, mouse, etc.), and/or computer media130(e.g., optical disks, magnetic disks) that can store code representative of the methods described herein. Information handling system122may act as a data acquisition system and possibly a data processing system that analyzes information from downhole fluid sampling tool100. For example, information handling system122may process the information from downhole fluid sampling tool100for determination of fluid contamination. The information handling system122may also determine additional properties of the fluid sample (or reservoir fluid), such as component concentrations, pressure-volume-temperature properties (e.g., bubble point, phase envelop prediction, etc.) based on the fluid characterization. This processing may occur at surface112in real-time. Alternatively, the processing may occur downhole hole or at surface112or another location after recovery of downhole fluid sampling tool100from wellbore104. Alternatively, the processing may be performed by an information handling system in wellbore104, such as fluid analysis module118. The resultant fluid contamination and fluid properties may then be transmitted to surface112, for example, in real-time. Real time may be defined within any range comprising 0.01 seconds to 0.1 seconds, 0.1 seconds to 1 second, 1 second to 1 minute, 1 minute to 1 hour, 1 hour to 4 hours, or any combination of ranges provided.

Referring now toFIG.2, a schematic diagram of downhole fluid sampling tool100disposed on a drill string200in a drilling operation. Downhole fluid sampling tool100may be used to obtain a fluid sample, for example, a fluid sample of a reservoir fluid from subterranean formation106. The reservoir fluid may be contaminated with well fluid (e.g., drilling fluid) from wellbore104. As described herein, the fluid sample may be analyzed to determine fluid contamination and other fluid properties of the reservoir fluid. As illustrated, a wellbore104may extend through subterranean formation106. While the wellbore104is shown extending generally vertically into the subterranean formation106, the principles described herein are also applicable to wellbores that extend at an angle through the subterranean formation106, such as horizontal and slanted wellbores. For example, althoughFIG.2shows a vertical or low inclination angle well, high inclination angle or horizontal placement of the well and equipment is also possible. It should further be noted that whileFIG.2generally depicts a land-based operation, those skilled in the art will readily recognize that the principles described herein are equally applicable to subsea operations that employ floating or sea-based platforms and rigs, without departing from the scope of the disclosure.

As illustrated, a drilling platform202may support a derrick204having a traveling block206for raising and lowering drill string200. Drill string200may include, but is not limited to, drill pipe and coiled tubing, as generally known to those skilled in the art. A kelly208may support drill string200as it may be lowered through a rotary table210. A drill bit212may be attached to the distal end of drill string200and may be driven either by a downhole motor and/or via rotation of drill string200from the surface112. Without limitation, drill bit212may include, roller cone bits, PDC bits, natural diamond bits, any hole openers, reamers, coring bits, and the like. As drill bit212rotates, it may create and extend wellbore104that penetrates various subterranean formations106. A pump214may circulate drilling fluid through a feed pipe216to kelly208, downhole through interior of drill string200, through orifices in drill bit212, back to surface112via annulus218surrounding drill string200, and into a retention pit220.

Drill bit212may be just one piece of a downhole assembly that may include one or more drill collars222and downhole fluid sampling tool100. Downhole fluid sampling tool100, which may be built into the drill collars222may gather measurements and fluid samples as described herein. One or more of the drill collars222may form a tool body114, which may be elongated as shown onFIG.2. Tool body114may be any suitable material, including without limitation titanium, stainless steel, alloys, plastic, combinations thereof, and the like. Downhole fluid sampling tool100may be similar in configuration and operation to downhole fluid sampling tool100shown onFIG.1except thatFIG.2shows downhole fluid sampling tool100disposed on drill string200. Alternatively, the sampling tool may be lowered into the wellbore after drilling operations on a wireline.

Downhole fluid sampling tool100may further include one or more sensors116for measuring properties of the fluid sample reservoir fluid, wellbore104, subterranean formation106, or the like. The one or more sensors116may be disposed within fluid analysis module118. In examples, more than one fluid analysis module may be disposed on drill string200. The properties of the fluid are measured as the fluid passes from the formation through the tool and into either the wellbore or a sample container. As fluid is flushed in the near wellbore region by the mechanical pump, the fluid that passes through the tool generally reduces in drilling fluid filtrate content, and generally increases in formation fluid content. The downhole fluid sampling tool100may be used to collect a fluid sample from subterranean formation106when the filtrate content has been determined to be sufficiently low. Sufficiently low depends on the purpose of sampling. For some laboratory testing below 10% drilling fluid contamination is sufficiently low, and for other testing below 1% drilling fluid filtrate contamination is sufficiently low. Sufficiently low also depends on the nature of the formation fluid such that lower requirements are generally needed, the lighter the oil as designated with either a higher GOR or a higher API gravity. Sufficiently low also depends on the rate of cleanup in a cost benefit analysis since longer pumpout times required to incrementally reduce the contamination levels may have prohibitively large costs. As previously described, the fluid sample may comprise a reservoir fluid, which may be contaminated with a drilling fluid or drilling fluid filtrate. Downhole fluid sampling tool100may obtain and separately store different fluid samples from subterranean formation106with fluid analysis module118. Fluid analysis module118may operate and function in the same manner as described above. However, storing of the fluid samples in the downhole fluid sampling tool100may be based on the determination of the fluid contamination. For example, if the fluid contamination exceeds a tolerance, then the fluid sample may not be stored. If the fluid contamination is within a tolerance, then the fluid sample may be stored in the downhole fluid sampling tool100. In examples, contamination may be defined within fluid analysis module118.

As previously described, information from downhole fluid sampling tool100may be transmitted to an information handling system122, which may be located at surface112. As illustrated, communication link120(which may be wired or wireless, for example) may be provided that may transmit data from downhole fluid sampling tool100to an information handling system111at surface112. Information handling system140may include a processing unit124, a monitor126, an input device128(e.g., keyboard, mouse, etc.), and/or computer media130(e.g., optical disks, magnetic disks) that may store code representative of the methods described herein. In addition to, or in place of processing at surface112, processing may occur downhole (e.g., fluid analysis module118). In examples, information handling system122may perform computations to estimate asphaltenes within a fluid sample.

FIG.3illustrates an example information handling system138which may be employed to perform various steps, methods, and techniques disclosed herein. Persons of ordinary skill in the art will readily appreciate that other system examples are possible. As illustrated, information handling system138includes a processing unit (CPU or processor)302and a system bus304that couples various system components including system memory306such as read only memory (ROM)308and random access memory (RAM)310to processor302. Processors disclosed herein may all be forms of this processor302. Information handling system138may include a cache312of high-speed memory connected directly with, in close proximity to, or integrated as part of processor302. Information handling system138copies data from memory306and/or storage device314to cache312for quick access by processor302. In this way, cache312provides a performance boost that avoids processor302delays while waiting for data. These and other modules may control or be configured to control processor302to perform various operations or actions. Other system memory306may be available for use as well. Memory306may include multiple different types of memory with different performance characteristics. It may be appreciated that the disclosure may operate on information handling system138with more than one processor302or on a group or cluster of computing devices networked together to provide greater processing capability. Processor302may include any general purpose processor and a hardware module or software module, such as first module316, second module318, and third module320stored in storage device314, configured to control processor302as well as a special-purpose processor where software instructions are incorporated into processor302. Processor302may be a self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. Processor302may include multiple processors, such as a system having multiple, physically separate processors in different sockets, or a system having multiple processor cores on a single physical chip. Similarly, processor302may include multiple distributed processors located in multiple separate computing devices but working together such as via a communications network. Multiple processors or processor cores may share resources such as memory306or cache312or may operate using independent resources. Processor302may include one or more state machines, an application specific integrated circuit (ASIC), or a programmable gate array (PGA) including a field PGA (FPGA).

Each individual component discussed above may be coupled to system bus304, which may connect each and every individual component to each other. System bus304may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS) stored in ROM308or the like, may provide the basic routine that helps to transfer information between elements within information handling system138, such as during start-up. Information handling system138further includes storage devices314or computer-readable storage media such as a hard disk drive, a magnetic disk drive, an optical disk drive, tape drive, solid-state drive, RAM drive, removable storage devices, a redundant array of inexpensive disks (RAID), hybrid storage device, or the like. Storage device314may include software modules316,318, and320for controlling processor302. Information handling system138may include other hardware or software modules. Storage device314is connected to the system bus304by a drive interface. The drives and the associated computer-readable storage devices provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data for information handling system138. In one aspect, a hardware module that performs a particular function includes the software component stored in a tangible computer-readable storage device in connection with the necessary hardware components, such as processor302, system bus304, and so forth, to carry out a particular function. In another aspect, the system may use a processor and computer-readable storage device to store instructions which, when executed by the processor, cause the processor to perform operations, a method or other specific actions. The basic components and appropriate variations may be modified depending on the type of device, such as whether information handling system138is a small, handheld computing device, a desktop computer, or a computer server. When processor302executes instructions to perform “operations”, processor302may perform the operations directly and/or facilitate, direct, or cooperate with another device or component to perform the operations.

As illustrated, information handling system138employs storage device314, which may be a hard disk or other types of computer-readable storage devices which may store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, digital versatile disks (DVDs), cartridges, random access memories (RAMs)310, read only memory (ROM)308, a cable containing a bit stream and the like, may also be used in the exemplary operating environment. Tangible computer-readable storage media, computer-readable storage devices, or computer-readable memory devices, expressly exclude media such as transitory waves, energy, carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with information handling system138, an input device322represents any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. Additionally, input device322may take in data from one or more sensors136, discussed above. An output device324may also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with information handling system138. Communications interface326generally governs and manages the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic hardware depicted may easily be substituted for improved hardware or firmware arrangements as they are developed.

As illustrated, each individual component describe above is depicted and disclosed as individual functional blocks. The functions these blocks represent may be provided through the use of either shared or dedicated hardware, including, but not limited to, hardware capable of executing software and hardware, such as a processor302, that is purpose-built to operate as an equivalent to software executing on a general purpose processor. For example, the functions of one or more processors presented inFIG.3may be provided by a single shared processor or multiple processors. (Use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software.) Illustrative embodiments may include microprocessor and/or digital signal processor (DSP) hardware, read-only memory (ROM)308for storing software performing the operations described below, and random-access memory (RAM)310for storing results. Very large-scale integration (VLSI) hardware embodiments, as well as custom VLSI circuitry in combination with a general-purpose DSP circuit, may also be provided.

The logical operations of the various methods, described below, are implemented as: (1) a sequence of computer implemented steps, operations, or procedures running on a programmable circuit within a general use computer, (2) a sequence of computer implemented steps, operations, or procedures running on a specific-use programmable circuit; and/or (3) interconnected machine modules or program engines within the programmable circuits. Information handling system138may practice all or part of the recited methods, may be a part of the recited systems, and/or may operate according to instructions in the recited tangible computer-readable storage devices. Such logical operations may be implemented as modules configured to control processor302to perform particular functions according to the programming of software modules316,318, and320.

In examples, one or more parts of the example information handling system138, up to and including the entire information handling system138, may be virtualized. For example, a virtual processor may be a software object that executes according to a particular instruction set, even when a physical processor of the same type as the virtual processor is unavailable. A virtualization layer or a virtual “host” may enable virtualized components of one or more different computing devices or device types by translating virtualized operations to actual operations. Ultimately however, virtualized hardware of every type is implemented or executed by some underlying physical hardware. Thus, a virtualization compute layer may operate on top of a physical compute layer. The virtualization compute layer may include one or more virtual machines, an overlay network, a hypervisor, virtual switching, and any other virtualization application.

FIG.4illustrates an example information handling system138having a chipset architecture that may be used in executing the described method and generating and displaying a graphical user interface (GUI). Information handling system138is an example of computer hardware, software, and firmware that may be used to implement the disclosed technology. Information handling system138may include a processor302, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processor302may communicate with a chipset400that may control input to and output from processor302. In this example, chipset400outputs information to output device324, such as a display, and may read and write information to storage device314, which may include, for example, magnetic media, and solid-state media. Chipset400may also read data from and write data to RAM310. A bridge402for interfacing with a variety of user interface components404may be provided for interfacing with chipset400. Such user interface components404may include a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to information handling system138may come from any of a variety of sources, machine generated and/or human generated.

Chipset400may also interface with one or more communication interfaces326that may have different physical interfaces. Such communication interfaces may include interfaces for wired and wireless local area networks, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein may include receiving ordered datasets over the physical interface or be generated by the machine itself by processor302analyzing data stored in storage device314or RAM310. Further, information handling system138receive inputs from a user via user interface components404and execute appropriate functions, such as browsing functions by interpreting these inputs using processor302.

In examples, information handling system138may also include tangible and/or non-transitory computer-readable storage devices for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable storage devices may be any available device that may be accessed by a general purpose or special purpose computer, including the functional design of any special purpose processor as described above. By way of example, and not limitation, such tangible computer-readable devices may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other device which may be used to carry or store desired program code in the form of computer-executable instructions, data structures, or processor chip design. When information or instructions are provided via a network, or another communications connection (either hardwired, wireless, or combination thereof), to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable storage devices.

Computer-executable instructions include, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, components, data structures, objects, and the functions inherent in the design of special-purpose processors, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.

In additional examples, methods may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Examples may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. During drilling operations information handling system138may process different types of the real time data which may be utilized to create an asphaltene onset pressure map (AOP).

FIG.5illustrates an example of one arrangement of resources in a computing network500that may employ the processes and techniques described herein, although many others are of course possible. As noted above, an information handling system138, as part of their function, may utilize data, which includes files, directories, metadata (e.g., access control list (ACLS) creation/edit dates associated with the data, etc.), and other data objects. The data on the information handling system138is typically a primary copy (e.g., a production copy). During a copy, backup, archive or other storage operation, information handling system138may send a copy of some data objects (or some components thereof) to a secondary storage computing device504by utilizing one or more data agents502.

A data agent502may be a desktop application, website application, or any software-based application that is run on information handling system138. As illustrated, information handling system138may be disposed at any rig site (e.g., referring toFIG.1) or repair and manufacturing center. Data agent502may communicate with a secondary storage computing device504using communication protocol508in a wired or wireless system. Communication protocol508may function and operate as an input to a website application. In the website application, field data related to pre- and post-operations, generated DTCs, notes, and the like may be uploaded. Additionally, information handling system138may utilize communication protocol508to access processed measurements, operations with similar DTCs, troubleshooting findings, historical run data, and/or the like. This information is accessed from secondary storage computing device504by data agent502, which is loaded on information handling system138.

Secondary storage computing device504may operate and function to create secondary copies of primary data objects (or some components thereof) in various cloud storage sites506A-N. Additionally, secondary storage computing device504may run determinative algorithms on data uploaded from one or more information handling systems138, discussed further below. Communications between the secondary storage computing devices504and cloud storage sites506A-N may utilize REST protocols (Representational state transfer interfaces) that satisfy basic C/R/U/D semantics (Create/Read/Update/Delete semantics), or other hypertext transfer protocol (“HTTP”)-based or file-transfer protocol (“FTP”)-based protocols (e.g., Simple Object Access Protocol).

In conjunction with creating secondary copies in cloud storage sites506A-N, the secondary storage computing device504may also perform local content indexing and/or local object-level, sub-object-level or block-level deduplication when performing storage operations involving various cloud storage sites506A-N. Cloud storage sites506A-N may further record and maintain DTC code logs for each downhole operation or run, map DTC codes, store repair and maintenance data, store operational data, and/or provide outputs from determinative algorithms that are fun at cloud storage sites506A-N. This type of network may be utilized to an asphaltene onset pressure map (AOP).

As such, input layer604may include any number of inputs608. Inputs608may comprise properties of fluid and/or fluid formations such as physical properties (bulk or molecular) such as density, index of refraction, compressibility, bubble point, phase and/or other phase behavior properties measured by sampling tool100. In examples, inputs may also include transport properties such as viscosity or thermal conductivity. Fluid analysis modules118may determine optical, chromatographic, mass spectrometry, density sensor, viscosity sensor, phase change apparatus compressibility sensor resistivity sensor, capacitance or dielectric sensor acoustic sensor, or combinations therein. Additionally, inputs608may also include chemical properties including composition i.e., hydrocarbon composition (methane, ethane propane, butane, pentane, hexane, higher hydrocarbons) and or chemical classes such as but not limited to Saturates, Aromatics, Resins or Asphaltenes chemical classes, and their respective concentrations of the various components, pH, eH, chemical potential, reactivity, fluid compatibility, and/or scaling potential. Fluid analysis modules118may determine optical, chromatographic, mass spectrometry, density sensor, viscosity sensor, phase change apparatus compressibility sensor resistivity sensor, capacitance or dielectric sensor acoustic sensor, or combinations therein. In other examples, inputs may include raw sensor measurements such as temperature, pressure, optical information, acoustic information, and/or electromagnetic information. Fluid analysis modules118may determine optical, chromatographic, mass spectrometry, density sensor, viscosity sensor, phase change apparatus compressibility sensor resistivity sensor, capacitance or dielectric sensor acoustic sensor, or combinations therein. In examples, output layer606may form outputs606. Outputs610may comprise other unmeasured or less well measured physical or chemical properties, and/or correlated sensor measurements. For instance, outputs610may comprise scaling potential, or asphaltene onset pressure if not directly measured. Alternatively, the model may provide outputs610for enhanced resolution, precision or accuracy refinement of a measured property such as bubble point, or asphaltene onset pressure which may be included as an input608but refined as an enhanced measurement as an output610in output layer606. Any of the inputs608or outputs610may be from the current well being evaluated or analogue wells which may be in the field, in the basis, or not so if other characteristics such as but not limited to formation type or formation fluid provide a basis for analogy. During operations, inputs608data are given to neurons612in input layer604. Neurons612,614, and616are defined as individual or multiple information handling systems122connected in a network, which may compute information to make drilling, completion or production decisions such as but not limited how to drill the well, where to drill the well, how to complete a well, or where to complete a well, or how to produce a well, or where to produce a well. Any of computations may be from the current well being evaluated or analogue wells which may be in the field, in the basis, or not so if other characteristics such as but not limited to formation type or formation fluid provide a basis for analogy. The output from neurons612may be transferred to one or more neurons614within one or more hidden layers602. Hidden layers602includes one or more neurons614connected in a network that further process information from neurons612. The number of hidden layers602and neurons612in hidden layer602may be determined by personnel that designs NN600. Hidden layers602is defined as a set of information handling system122assigned to specific processing. Hidden layers602spread computation to multiple neurons606, which may allow for faster computing, processing, training, and learning by NN600. Output layers606may combine the processing in hidden layers602, using neurons616, to form an asphaltene onset pressure (AOP). By any of the modeling methods, output layers606, wherein other methods may use different layer or subfunction structuring, may be coordinated such that simultaneously an AOP may be provided for different outputs each corresponding to a different depths or lateral distance across a field or distance from an injecting well, temperature or other state condition comprising at least formation or concentration of materials. Multiple outputs may be coordinated wherein the multiple outputs are different but related parameters which may include but is not limited to asphaltene onset pressure, and asphaltene stability index, either static for a single state, or as a function independent variable such as but not limited to depth or lateral distance across a field or distance from an injecting well or of state variables such as but not limited to temperature. Other modeling methods include equations of state, kriging methods, random forest methods, classification methods, multivariate analysis methods and combinations therein.

FIG.7illustrates a schematic of fluid sampling tool100. As illustrated, fluid sampling tool100includes a power telemetry section702through which fluid sampling tool100may communicate with other actuators and sensors in a conveyance (e.g., conveyance102onFIG.1or drill string200onFIG.2), the conveyance's communications system, such as information handling system138(e.g., referring toFIG.1). In examples, power telemetry section702may also be a port through which the various actuators (e.g., valves) and sensors (e.g., temperature and pressure sensors) in fluid sampling tool100may be controlled and monitored. In examples, power telemetry section702includes an information handling system that exercises the control and monitoring function. In one example, the control and monitoring function is performed by an information handling system in another part of the drill string or wireline tool (not shown) or by an information handling system at surface112.

Information from fluid sampling tool100may be gathered and/or processed by the information handling system138(e.g., referring toFIGS.1and2). The processing may be performed real-time during data acquisition or after recovery of fluid sampling tool100. Processing may alternatively occur downhole or may occur both downhole and at surface. In some examples, signals recorded by fluid sampling tool100may be conducted to information handling system by way of conveyance. Information handling system may process the signals, and the information contained therein may be displayed for an operator to observe and stored for future processing and reference. Information handling system may also contain an apparatus for supplying control signals and power to fluid sampling tool100.

In examples, fluid sampling tool100may include one or more enhanced probe sections704. Each enhanced probe section may include a dual probe section706or a focus sampling probe section708. Both of which may extract fluid from the reservoir and delivers it to a channel710that extends from one end of fluid sampling tool100to the other. Without limitation, dual probe section706includes two probes712,714which may extend from fluid sampling tool100and press against the inner wall of wellbore104(e.g., referring toFIG.1). Probe channels716,718may connect probes712,714to channel710and allow for continuous fluid flow from the formation106to channel710. A high-volume bidirectional pump720may be used to pump fluids from the formation, through probe channels716,718and to channel710. Alternatively, a low volume pump bi direction piston722may be used to remove reservoir fluid from the reservoir and house them for asphaltene measurements, discussed below. Two standoffs or stabilizers724,726hold fluid sampling tool100in place as probes712,714press against the wall of wellbore104. In examples, probes712,714and stabilizers724,726may be retracted when fluid sampling tool100may be in motion and probes712,714and stabilizers724,726may be extended to sample the formation fluids at any suitable location in wellbore104. As illustrated, probes712,714may be replaced, or used in conjunction with, focus sampling probe section708. Focus sampling probe section708may operate and function as discussed above for probes712,714but with a single probe728. Other probe examples may include, but are not limited to, oval probes, packers, and/or radial or circumferential probes.

In examples, channel710may connect other parts and sections of fluid sampling tool100to each other. For example, Additionally, formation testing tool100may include a second high-volume bidirectional pump730for pumping fluid through channel710to one or more multi-chamber sections732, one or more amide side fluid density modules734, and/or one or more sample side optics analyzers736.

FIG.8illustrates an expanded view of an enhanced probe section704. As illustrated, enhanced probe section704includes low volume pump bi direction piston722, which is utilized for asphaltene measurements. Asphaltenes are large, high-density hydrocarbons that may be the heaviest component in reservoir fluids. The precipitation and deposition of asphaltenes are a nuisance to any petroleum production system since that may lead to reduction in productivity or injectivity of a well. Asphaltene precipitation and ultimate deposition is caused by a number of factors including changes in pressure, temperature, and composition.

As the reservoir inside formation undergoes primary depletion, the pore (also called reservoir pressure) pressure as well as the flowing bottomhole pressure drops. For a constant temperature, as the decreasing pressure in the reservoir and the wellbore104(e.g., referring toFIG.1) reaches the asphaltene precipitation onset pressure, the dissolved asphaltenes start to precipitate and deposit. This deposition may take place in the reservoir, or near/at the sandface, or in wellbore104, or in the tubing, or at the surface facilities. This blockage of production paths causes further pressure drops, which results in higher asphaltene precipitation. Over time, this deposition becomes worse until the bubble point pressure is reached. As the pressure falls further below, the asphaltene begins to redissolve into the liquid phase. The deposition of asphaltene may also be caused by changes in fluid composition, and temperature, as well as the introduction of any incompatible chemicals. Identifying when asphaltene falls out of solution is currently performed by laboratory test. To do this, a reservoir fluid sample is taken by fluid sampling tool170and extracted at the surface. From there the reservoir fluid sample is sent to a laboratory for analyses.

Analyses of asphaltenes may be performed with any number of scientific evaluations. A few a listed here for reference. One such operation is the Colloidal Instability Index (CII) that was created to illustrate a scale of eventual asphaltene deposition during production. The CII is made up of SARA fractional components and described by the following equation:

CII=Saturates⁢⁢%+Asphaltenes⁢⁢%Aromatics⁢%+Resins⁢%(1)
The index is governed by the following criteria:CII≤0.7: asphaltene fraction stable0.7≤CII≤0.9: asphaltene fraction uncertainCII≥0.9: asphaltene fraction unstable
The CII may be utilized with methods below to show pressure indicating stability and instability before and after Asphaltene Onset Pressure (AOP).

Another scientific method to analyze asphaltenes is using a refractive index. A Refractive Index (RI) describes the amount of light bending through a medium. RI is proven to accurately describe fluid properties of a hydrocarbon which may be then applied towards reservoir calculations. The refractive index of oil with respect to a Saturates, Aromatics, Resins and Asphaltenes (SARA) fraction by the following equation:
RIoil=0.01452×(Saturates %)+0.0014982×(Asphaltenes %)+0.0016624×(Resins %+Asphaltenes %)  (2)
At the point of AOP, the RI is described as the Precipitation Refractive Index (PRI). The relation between PRI and RIoildescribe a measure that dictates asphaltene stability by the following equation:
Δ(RI)=RIoilPRI  (3)
The index is governed by the following criteria:Δ(RI)≤0.045: asphaltene unstable0.045≤Δ(RI)≤0.060: asphaltene bordering stabilityΔ(RI)≥0.060: asphaltene stable

To describe the solvency of asphaltenes within an oil mixture, the solubility parameter δ is an important measurement that accounts for molecular forces and energy density of asphaltenes relative to a solution. The Equations below show a relation that describes the solubility parameter of an oil mixture using the oil mixture's refractive index:

δ=52.042FRI+2.904(4)FRI=(RI2-1)(RI2+2)(5)
Where RI is the refractive index of the oil component.

At higher temperatures less amount of asphaltene is precipitated. A corollary effect is that the oil is more soluble and stable for asphaltenes. As such, a parameter defined as the “driving force” is established to dictate the force micro-aggregate asphaltenes have over asphaltenes in solution, which is the difference in solubilities as shown in equation:
Δδ=δasph−δsolution(6)

Another scientific model may be used to find the rate of precipitation for asphaltene. It is assumed proportional to the supersaturation degree of asphaltenes that is defined as the difference between the actual concentration of asphaltenes dissolved in oil and the concentration of asphaltene at equilibrium for a specific temperature and pressure. This rate of precipitation may be described mathematically as:

dCdt=kp(CA-CAeq)(7)
where

dCdt
is the rate at which the concentration of asphaltene precipitate changes (i.e., the rate at which dissolved asphaltenes precipitate forming micro-aggregates), kpis the precipitation kinetic parameter, CAis the actual dissolved concentration of asphaltenes in solution at given operating conditions, and CAeqis the concentration of asphaltenes in solution at equilibrium for the given temperature and pressure.

As evidenced from Equation 7 above, the precipitation process is modeled as a first order reaction based on the degree of supersaturation of asphaltenes. The higher the concentration difference between the dissolved and equilibrium concentration, the higher the precipitation rate becomes. This concentration difference or the degree of supersaturation in the context of precipitation starts at 0 which is right at the precipitation onset. With decreasing pressure, the equilibrium concentration at the operating conditions goes down as well and therefore the supersaturation degree increases leading to an increase in the rate of precipitation. Gradually, as the dissolved concentration goes down, the rate of precipitation stabilizes before going down again. Since the dissolved concentration of asphaltenes at every point is not known in the system, the differential equation above can be solved to come up with an expression for the rate of precipitation as:

dCdt=kp(C0-CAeq)⁢ek⁢pΔ⁢t(8)
where C0is the concentration of dissolved asphaltenes right before the precipitation onset and Δt is the incremental time from that point onwards. Equation 8 may then be used to model the rate of precipitation of asphaltene in a reservoir section once the tuning parameter (kp) is sufficiently known.

Experiments and modeling showed that kpis lower for higher temperatures as well. Therefore, the following relation was derived to relate the kinetic factor, temperature and driving force:

kp=exp(a0⁢exp⁡(-a1T)-b0⁢exp⁡(-b1T)Δδ)(9)
where a0, b0, a1, b1are constants based on fluid dynamics of asphaltene deposition and T is temperature. From this, the following independent correlations may be observed:

kp∝1T,kp∝1Δδ,and⁢Δδp∝1T(10)

As discussed below, a gravimetric method may have a similar effect by destabilizing asphaltenes over time with an increased pressure differential ΔP′ from soluble to precipitate. More specifically:
ΔP′=Pasph−Psolution(11)
where Pasphare where asphaltene concentrations increase due to precipitation, and Psolutionis the baseline pressure at which asphaltenes are in solution.

As illustrated inFIG.8, these laboratory test may be reconstructed downhole using enhanced probe section704. Specifically, testing methods include the use of housing721that includes a low volume bi directional piston722within enhanced probe section704. Housing721allows for low volume bi directional piston722to draw in fluid for measurement, analyses, or testing within the housing. When sampling operations are being performed, as described above, formation fluid is extracted from a reservoir through a probe, such as focus sampling probe section708, and into fluid sampling tool100through probe channels716and718. As illustrated, probe channels716and718may each be connected to independent zero offset pressure gauges800. Fluid sampling tool100includes housing721and low volume bi directional piston722, where housing721may have 100 cc of capacity and the capability to operate up to 20000 psi below hydrostatic pressure, which is monitored by another high-resolution pressure gauge802. Additionally, probe channels716and718have the ability to be isolate from internal flowlines, such as channel710, from the formation through one or more shut in valves804positioned along each probe channels716and718. This allows enhanced probe section704to access fluids from either only in fluid sampling tool100or reservoir fluid taken through a probe.

During measurement operations, the onset of asphaltenes may be measured utilizing probe section704and/or fluid analysis module736. Within fluid analysis module736may be one or more optical measurement tools738that are fluidly connected to channel710. As testing methods are performed with housing721, additional testing methods may analyze reservoir fluid in channel710with one or more optical measurement tools738in fluid analysis module736. Within the fluid analysis module, fluid composition including C1, C2, C3, C4, C5, C6+, Saturates, Aromatics, Resins, and Asphaltene concentrations, Bubble point, viscosity, index of refractions, molecular weight, API gravity, Gas to Oil ratio (GOR), capacitance, dielectric spectroscopy, resistivity, optical throughput may be measured. Changes in these measured properties are many times affected by asphaltene precipitation.

FIG.9is a graph illustrating asphaltene phase envelope denoting the stability regions of asphaltenes during production. As illustrated, Upper Asphaltene boundary900separates asphaltenes in equilibrium denoted “Asphaltene Stable”. As a reservoir starts producing (Flowing Pressure) at the sandface, the reservoir eventually depletes and asphaltenes start precipitating at the Upper Asphaltene Onset Pressure (UAOP)902, where the fluid becomes thermodynamically unstable. As pressure crosses the bubble point (BP)904, gas evolves from solution and is also near where the peak of asphaltene precipitation exists. The Lower Asphaltene Onset Pressure (LAOP)906is the lowest pressure where asphaltenes are out of solution. As the pressure falls further below, the asphaltene begins to redissolve into the liquid and gas phases. This transition is represented with a corresponding increase in asphaltene precipitate from UAOP902to the peak at BP904and then lowest at the LAOP906.

Asphaltenes undergo a series of kinetic phases when destabilizing. On Precipitation, asphaltene molecules initially evolve out of solution at the UAOP902, and they reside as visibly suspended particles. With an increase in precipitation, molecules eventually aggregate and combine in the Flocculation process. If flocculated particles are noticed (or predicted) early enough, they may be easily remediated during production, which will lead to a de-aggregation of flocculated particles is known as Disassociation. However, if flocculation is left without action, they will lead to Deposition. This stage is a considerable threat, where asphaltenes reduce reservoir efficiency by plugging pores in the sandface, depositing on tubing walls. The consequence of not detecting the UAOP902early enough may lead to catastrophic consequences and require considerable costly remediation efforts.

FIGS.10A-10Eillustrate operation of low volume bi directional piston722allows for the measurement and analyze of asphaltenes from reservoir fluid to determine UAOP902, BP904, and/or LAOP906(e.g., referring toFIG.9). Herein, only a single or multiple measurements of UAOP902, BP904, or LAOP906at at least two depths may form an AOP map. Referring toFIG.10A, to begin measurement to analyze asphaltenes at a determined location within wellbore104(e.g., referring toFIG.1), enhanced probe section704is activated to allow fluid sampling tool100to be in fluid communication with a formation through dual probe section706or focus sampling probe708, as described above. After establishing a formation pressure, an optionally taking samples, a gravimetric test is performed.

Measurements taken by zero offset pressure gauges800and high-resolution pressure gauge802may be utilized to perform a gravimetric test on an information handling system to determine asphaltene precipitation (e.g., referring toFIG.8). To perform the gravimetric test, probe channels716and718(e.g., referring toFIG.8) may be in fluid communication with the reservoir in the formation. Additionally, it should be noted, that the one or more shut in valves804(e.g., referring toFIG.8) have been activated to isolate low volume bi directional piston722and housing721(e.g., referring toFIG.8) from other components and devices in fluid sampling tool100(e.g., referring toFIG.8). Using zero offset pressure gauges800and high-resolution pressure gauge802(e.g., referring toFIG.8), flowing pressure, temperature and soluble fluid composition of the oil at a sample point in wellbore104are measured. InFIG.10A, low volume bi directional piston722is drawn down at a preprogrammed constant rate, while reservoir fluid is drawn into housing721by low volume bi directional piston722and is monitored in real time. Herein, the reservoir fluid drawn into housing721may be referred to as fluid sample. As such, the fluid sample is at a pressure greater than UAOP and the fluid sample will resembleFIG.10Awith asphaltenes saluted within the fluid sample. As low volume bi directional piston722continues depressurization within housing721, the fluid sample within housing721may resembleFIG.10B, as the pressure of the fluid sample is lowered to UAOP. As illustrated, asphaltene particles1000start precipitating at the Upper Asphaltene Onset Pressure (UAOP) point within housing721. Disposed along channel710may be at least one fluid analysis sensor (not illustrated). The at least one fluid analysis sensor may observe an inflection sensitive to Asphaltenes, particles, or mass changes. Fluid analysis sensors may comprise density sensors, compositional sensors, and other standard operating sensors.

The respective pressure and asphaltene concentration are detected by one or more zero offset pressure gauges800and/or one or more high-resolution pressure gauges802. In other embodiments, other components may be measured similar to asphaltene particles1000, such as, Saturates, Aromatics, Resins, and/or C1-C5%.

Low volume bi directional piston722may further lower the pressure of the fluid sample until it resemblesFIG.10C, in which the fluid sample pressure is equal to the Asphaltene+Resin-Flocculation Onset (ARFO). Evidence of when the fluid sample reaches ARFO may be evident by precipitated asphaltene particles1000aggregating and flocculating within the flowline with an inflection in the asphaltene weight percentage. This inflection is detected within housing721by fluid analysis sensors as a spike in the first or second derivative. In examples visually or fitting to a knot curve or other suitable curve may identify such an inflection. Subsequently, pressure of the fluid sample may be lowered as previously described until it resembles. InFIG.4Din which the fluid sample pressure is equal to the bubble point (BP), which is shown in all sensor data that is measuring and analyzing asphaltene particles1000within housing721. In addition, further aggregation to asphaltene particles1000occurs as part of flocculation. Finally, pressure of the fluid sample may be lowered as previously described until it resemblesFIG.4E, in which the fluid sample pressure drops below BP. As such, lighter components1002liberate from the system and there is a higher concentration of aggregated flocculates of asphaltene particles1000in housing721. At this stage the test is concluded by design and should be considered in the planning process. It is not intended to further depressurize the system to the Lower Asphaltene Onset Pressure (LAOP) point. During this progression, flocculation of asphaltene particles1000may transition to deposition, and fluid sampling tool100is at risk being plugged and would be inoperable. As a result, no further sampling or pressure tests may be performed, and fluid sampling tool100would have to be pulled out to surface. The Gravimetric test may determine the precisely detect the UAOP, ARFO and BP pressures. Additionally, temperature at each pressure is recorded as well. Herein, AOP measurements may be referred to as UAOP, LAOP, ARFO, or BP measurements or any combination thereof.

Following the Gravimetric test, low volume bi directional piston722is then moved back to the original position within housing721, compressing probes712,716back to the reservoir flowing pressure. Subsequently, the shut in valves804are opened, via power telemetry section702, equalizing fluid sampling tool100, and fluid sampling tool100may be retracted and moved to another location within wellbore104(e.g., referring toFIG.1) for further sample or test operations. Additionally, fluid sampling tool100may move fluid samples to surface112. At the surface112a series of tests may be conducted to repeat fluid measurements made down hole and to provide a more detailed set of fluid properties including a more detailed composition, and physical properties than mentioned above. Additionally, a full PVT and phase behavior analysis may be performed at surface112at a laboratory yielding AOP, UAOP, LAOP, ARFO and BP measurements. Equipment to conduct such analysis include gas chromatographs, liquid chromatographs, mass spectrometers, wet chemistry, PVT cells, viscosimeters, densitometers, and microfluidic systems such as but not limited to PVT microfluidic systems. The above sequences are repeated at every sample point, providing AOP, UAOP, LAOP, ARFO and BP measurements at unique depths and locations within the reservoir independent of the captured fluid sample.

Multiple AOP measurements taken at different positions in a wellbore to form Asphaltene Onset Pressure Map from Asphaltene Onset Pressure Measurements, which are done isothermally. At specific positions within a well the asphaltene onset pressure and or fluid chemical and physical measurements may be made. Additionally, formation measurements may be made. The asphaltene onset pressure map may be formed simply by correlating the AOP measured at various locations to the location or fluid, or rock properties information acquired at the same or similar depths, or modeling may be performed in order to interoperate and extrapolate AOP mapping information. Note that the term mapping may be done digitally as a correlation function or other mathematical function that describes the AOP variation relative to the independent properties such as variations of location. Herein, a location may be defined by vertical depth, lateral distance or extent pressure, temperature, at least one component of reservoir fluid composition, or other state condition. The mapping may take place multidemsionally such that it includes location and geology or compositional information simultaneously and requires a minimum of two distinct AOP measurements. The map result may be a graphical representation, digital representation, mathematical representation, functional representation statistical representation, or other appropriate representation that allows information extraction of the AOP per the mapped properties. An AOP map may be formed as a single dimensional variation with depth, or a two-dimensional (2D) topographical style map with lateral location e.g., north and south. The map may be smooth or jumpy and may also be a contour plot against two dimensions with an AOP, or a color plot on a three-dimensional (3D) surface to demonstrate 3 dimensions with an AOP. The map may also be a multidimensional matrix of data acquired from downhole formation sampling tool, reservoir parameters, geological parameters, and/or petrophysical parameters.

In examples, an equilibrium or disequilibrium composition may be determined within the reservoir. For a continuous reservoir AOP may be smooth as a function of depth or other property that varies smoothly with depth. We will see a discontinuity or abrupt change in first and or second derivative for discontinuities as a function of the independent variable i.e., depth. Same holds for lateral continuity.

In other examples, mechanism for disequilibrium processes may be identified (e.g., Gas migration, gas charging, convection, etc.). This may be performed by modeling the AOP against a property indicative of a process such as gas composition from a gas charge, confirms disequilibrium. Other disequilibrium processes may be water washing, biodegradation faulting, baffling, precipitation, convection. Composition may be a good independent variable to model disequilibrium processes against.

In other examples, identifying behavior of Maltenes (Saturates, Aromatics, Resins) in conjunction with Asphaltenes to quantify chemistry behavior while destabilizing an oil may be performed. For example, identification may be performed by an asphaltene stability index, or phase behavior modeling wherein the phase is the solid asphaltene state. Modified cubic equation of state functions or petrurbed chain statistical associating fluid theory equation of state PC-SAFT EOS have been used, but empirical correlations including machine learning techniques have been used. The composition is an optional input or an optional output for the NN model.

Mapping AOP measurements with isothermal fluid expansion pressure tests may be performed (constant composition expansion) with AOP UAOP, LAOP, ARFO, or BP measurements. Such measurements may be performed by having a fluid analyzer in communication with pressure and rate sensors connected to either a piston or a pump through a flowline or cavity, and a valve that isolates this flowline from the rest of the tool, as discussed above. The section must be in contact directly or indirectly with a pressure measurement and potentially other sensor measurements such as an optical sensor, a compositional sensor, or a density sensor of which are sensitive to the phase of asphaltenes and or concentration of asphaltenes in a specific phase. In the first step, a pump moves reservoir fluid to low contamination values through the formation sampler. Subsequently, a fluid sample is captured not communicated with in the subsequent process. Next a number of valves isolate the flowline connecting the fluid analyzer to the piston (or pump) from the reservoir and rest of the tool. At this point, either the piston or the pump is depressurized at a constant rate selected by the operator. Both the fluid analyzer and pressure sensor measure compositional changes of the fluid with respect to pressure. At a point where asphaltenes are exhibited in the fluid analyzer due to depressurization, the measured pressure is denoted as the Asphaltene Onset Pressure.

Laboratory analysis of recovered fluid samples may be performed. Such Laboratory analysis may comprise finding a laboratory property such as composition by gas chromatography and mass spectrometry and combinations therein, physical properties such as density or viscosity or compressibility, phase behavior analysis, including phase envelops such as but not limited to constant composition expansion, production simulations such as but not limited to differential liberation, fluid compatibility studies, flow assurance studies, slim tube studies for enhanced oil recovery etc. Herein, a laboratory property is defined as at least one measurement in a laboratory, as described above, of at least one fluid sample. Such measurements may comprise compositional properties, physical properties, phase behavior analysis, gravimetric pressure and temperature measurements determined in a laboratory with conventional methods, or any combination thereof. Physical properties are not independent of temperature and pressure but rather directly related. Composition may be related to temperature or pressure if measured as absolute concentration but independent of temperature and pressure if measured as a relative concentration. All physical and chemical properties are interrelated to asphaltene onset pressure. Additionally, multiple fluid samples may be obtained from fluid sampling tool100. As such, multiple laboratory properties may be measured, as described above from the multiple fluid properties.

A relationship between a laboratory property and an AOP map may be developed using a relational model. The relational model may be based on an equation of state which implements any combination of empirical properties, correlative properties, or stochastic properties. Developing the relation model may comprise forming a correlation between the laboratory property, equation of state, and the AOP map. Forming such a correlation may comprise projecting the laboratory property and the equation of state onto the AOP map. Herein, projecting may be defined as comparing the laboratory property combined with the equation of state to the AOP Map and estimating unmeasured data not previously formed on the AOP map or correcting imperfect data on the AOP map to form a correlation. However, an equation of state is not compulsory to forming a correlation or relational model. The correlation derived from said projection may define the relational model. Additionally, the relational model may update or interpolate the AOP map to locations which were not measured or improve its accuracy, forming an updated AOP map. Improved accuracy of the AOP map may be defined as at least one updated AOP within the updated AOP map, which is closer to the actual AOP from the formation than the measured AOP downhole within the original AOP map.

In examples, the relational model is thermodynamic such as but not limited to a PC-SAFT equation of state or cubic equation of state. Relational models may also be empirical such and derived through machine learning, as described inFIG.6. Herein, an update to the AOP map may comprise improving the accuracy of the map with the relationships and correlations developed in the projection. In examples, multiple projections may be derived forming a complex relational model. In further examples, a correlation may also be obtained with a single AOP measured downhole and a laboratory property. Such a correlation may also yield an updated AOP and the updated AOP may be applied to an AOP map, a reservoir simulation, and/or a production analysis.

Methods and systems may perform a reservoir simulation and/or a production analysis with the Relational model and updated AOP map. Generally, the data any AOP map will be digitized, and used to detect when production crosses the threshold of asphaltene precipitation. The fluid properties of the resulting slurry (solid/liquid mixture) may be measured, modeled, assumed, or predicted and used to modify the physical properties of the fluid under various production and or completion scenarios. The scenario modeling will ultimately be used to deploy the physical completion or completion decisions or production analysis from the reservoir simulation. A reservoir simulation may be created from at least the updated AOP map and relational model. Subsequently a production analysis may be developed from the reservoir simulation. As such, if it is desirable to avoid asphaltene precipitation, then the map may be used as a hard boundary for the parameters of the simulation space. Reservoir simulations may be based on finite difference, finite element, analytical modeling, empirical modeling including machine learning analysis techniques and use the relational model derived. Herein, a reservoir simulation may be defined as a prediction or development of the flow of fluids within a formation. In examples a reservoir simulation may comprise the composition of the formation. Additionally, forming the relational model, updating the AOP map, forming a reservoir simulation, and forming a production analysis may implement a NN model as described inFIG.6.

Similar to production analysis, production decisions may be made with reservoir simulations, but also consider production equipment decisions and remediation techniques. Herein, production decisions may be defined as weather or not to produce a zone, or weather or not to commingle a zone through a production tubing. At what rate to produce a zone. What remediation may be necessary eg asphaltene flocculation or scaling inhibitors. Flow in the casing, combability of fluids and fluid behavior in the surface separation equipment and surface handling equipment are all considered for an economical investment of capital and operational expenses against projected future value of the production fluids. Ultimately the asphaltene onset pressure map with respect to physical location axes (e.g., depth or lateral position), chemical axes and including but not limited to those discussed earlier allow the phase behavior of asphaltenes and chemical response of asphaltenes with respect to changing composition or chemical treatments to be considered in the production and completion designs.

In current technology, asphaltene phase behavior and specifically AOP is not measured downhole to form an AOP map. Instead asphaltene phase behavior is measured in a laboratory usually after production has commenced to determine remediation solutions. Additionally, improvements over current technology reside in projecting laboratory analysis onto an AOP map and updating said map. Further the updated AOP may be used in reservoir simulations or completion and production decisions directly. As such, methods and systems described improve the application of AOP measurements to more than determining remediation solutions.

Statement 1. A method which may comprise disposing a fluid sampling tool into a wellbore, taking a plurality of fluid samples with the fluid sampling tool, identifying a plurality of asphaltene onset pressures (AOP) downhole based at the least on the plurality of fluid samples. The method may further comprise forming an AOP map from at least the plurality of AOPs, identifying a laboratory property from at least one of the plurality of fluid samples; and developing a relational model between at least one of the plurality of AOPs and the laboratory property.

Statement 2. The method of statement 1, wherein the relational model is developed by projecting the laboratory property onto the AOP map.

Statement 3. The method of statement 2, wherein an equation of state is projected with the laboratory property onto the AOP map to develop the relational model.

Statement 4. The method of statement 3, wherein the equation of state are empirical properties, correlative properties, stochastic properties, or any combination thereof.

Statement 5. The method of statements 1 or 2, wherein a laboratory measures the plurality of fluid samples to form the laboratory property.

Statement 6. The method of statements 1, 2, or 5, wherein the laboratory property is compositional properties, physical properties, phase behavior analysis, gravimetric pressure, gravimetric temperature measurement, or any combination thereof.

Statement 7. The method of statements 1, 2, 5, or 6, further comprising updating the AOP map with the relational model to form an updated AOP map.

Statement 8. The method of statement 7, wherein the updated AOP map is interpolated to locations in the wellbore which were not sampled or is closer to the actual AOP from the formation than the measured AOP downhole.

Statement 9. The method of statement 7, further comprising forming a reservoir simulation from the updated AOP map.

Statement 10. The method of statement 9, further comprising performing a production decision from the reservoir simulation.

Statement 11. The method of statements 1, 2, or 5-7, wherein identifying the AOP map comprises performing a gravimetric test.

Statement 12. A system which may comprise a fluid sampling tool with one or more probes for injecting the one or more probes into a wellbore and taking a plurality of fluid samples from the wellbore. The system may further comprise an information handling system to identify a plurality of AOPs downhole from at least the plurality of fluid samples, form an AOP map from the plurality of AOPs, receive a laboratory property from at least one of the plurality of fluid samples, and develop a relational model from at least the AOP map or one AOP from the plurality of AOPs and the laboratory property.

Statement 13. The system of statement 12, wherein the relational model is developed by projecting the laboratory property onto the AOP map.

Statement 14. The system of statement 13, wherein an equation of state is projected with the laboratory property onto the AOP map to develop the relational model.

Statement 15. The system of statement 14, wherein the equation of state are empirical properties, correlative properties, stochastic properties, or any combination thereof.

Statement 16. The system of statements 12 or 13, further comprising a laboratory for measuring the laboratory property from at least one fluid sample from the plurality of fluid samples.

Statement 17. The system of statements 12, 13, or 16, wherein the laboratory property is compositional properties, physical properties, phase behavior analysis, gravimetric pressure, gravimetric temperature measurement, or any combination thereof.

Statement 18. The system of statements 12, 13, 16, or 17, wherein the information handling system updates the AOP map with the relational model to form an updated AOP map.

Statement 19. The system of statement 18, wherein the updated AOP map is interpolated to locations in the wellbore which were not sampled or is closer to the actual AOP from the formation than the measured AOP downhole.

Statement 20. The system of statement 18, wherein the information handling system forms a reservoir simulation from the updated AOP map.

The preceding description provides various embodiments of the systems and methods of use disclosed herein which may contain different method steps and alternative combinations of components. It should be understood that, although individual embodiments may be discussed herein, the present disclosure covers all combinations of the disclosed embodiments, including, without limitation, the different component combinations, method step combinations, and properties of the system. It should be understood that the compositions and methods are described in terms of “including,” “containing,” or “including” various components or steps, the compositions and methods can also “consist essentially of” or “consist of” the various components and steps. Moreover, the indefinite articles “a” or “an,” as used in the claims, are defined herein to mean one or more than one of the element that it introduces.

For the sake of brevity, only certain ranges are explicitly disclosed herein. However, ranges from any lower limit may be combined with any upper limit to recite a range not explicitly recited, as well as, ranges from any lower limit may be combined with any other lower limit to recite a range not explicitly recited, in the same way, ranges from any upper limit may be combined with any other upper limit to recite a range not explicitly recited. Additionally, whenever a numerical range with a lower limit and an upper limit is disclosed, any number and any included range falling within the range are specifically disclosed. In particular, every range of values (of the form, “from about a to about b,” or, equivalently, “from approximately a to b,” or, equivalently, “from approximately a-b”) disclosed herein is to be understood to set forth every number and range encompassed within the broader range of values even if not explicitly recited. Thus, every point or individual value may serve as its own lower or upper limit combined with any other point or individual value or any other lower or upper limit, to recite a range not explicitly recited.

Therefore, the present embodiments are well adapted to attain the ends and advantages mentioned as well as those that are inherent therein. The particular embodiments disclosed above are illustrative only, and may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. Although individual embodiments are discussed, the disclosure covers all combinations of all of the embodiments. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. Also, the terms in the claims have their plain, ordinary meaning unless otherwise explicitly and clearly defined by the patentee. It is therefore evident that the particular illustrative embodiments disclosed above may be altered or modified and all such variations are considered within the scope and spirit of those embodiments. If there is any conflict in the usages of a word or term in this specification and one or more patent(s) or other documents that may be incorporated herein by reference, the definitions that are consistent with this specification should be adopted.