Systems and methods for optimizing oil production

A method for optimizing oil production includes receiving historical production data for one or more wells. A first model is generated based at least partially upon the historical production data. The production in the one or more wells is predicted based at least partially upon the first model. A second model is generated based at least partially on the predicted production. A production allocation is determined in the one or more wells to maximize production based at least partially upon the second model. The production allocation is then implemented.

BACKGROUND

As oil and gas companies continue deal with volatility in oil prices, production optimization and maintenance play a role in driving operations for the industry while maintaining acceptable profit margins. They focus on reducing the cost-per-barrel to survive the volatile oil prices. This may involve reducing the operating costs, increasing the uptime, and increasing the number of barrels produced.

SUMMARY

A method for optimizing oil production is disclosed. The method includes receiving historical production data for one or more wells. A first model is generated based at least partially upon the historical production data. The production in the one or more wells is predicted based at least partially upon the first model. A second model is generated based at least partially on the predicted production. A production allocation is determined in the one or more wells to maximize production based at least partially upon the second model. The production allocation is then implemented.

A computing system is also disclosed. The computing system includes one or more processors a memory system. The memory system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computer system to perform operations. The operations include receiving historical production data for one or more wells. A first model is generated based at least partially upon the historical production data. A curve based is generated at least partially upon the first model, wherein the curve corresponds to the historical production data. One or more data points in the historical production data that are outliers with respect to the curve are identified and removed. An uncertainty of the historical production data is determined based at least partially upon the first model after the one or more data points are removed. Production in the one or more wells is predicted based at least partially upon the first model and the uncertainty of the historical production data. An uncertainty of the predicted production is determined based at least partially upon the predicted production and the uncertainty of the historical production. A second model is generated based at least partially on the predicted production and the uncertainty of the predicted production. A production allocation is determined in the one or more wells to maximize production based at least partially upon the second model. The production allocation is then implemented.

A non-transitory computer-readable media is also disclosed. The media stores instructions that, when executed by one or more processors of a computer system, cause the computer system to perform operations. The operations include receiving historical production data for one or more wells. A first model is generated based at least partially upon the historical production data. A curve is generated based at least partially upon the first model. The curve corresponds to the historical production data. One or more data points in the historical production data that are outliers with respect to the curve are identified and removed. An uncertainty of the historical production data is determined based at least partially upon the first model after the one or more data points are removed. Production in the one or more wells is predicted based at least partially upon the first model and the uncertainty of the historical production data. An uncertainty of the predicted production is determined based at least partially upon the predicted production and the uncertainty of the historical production. A second model is generated based at least partially on the predicted production and the uncertainty of the predicted production. A production allocation is determined in the one or more wells to maximize production based at least partially upon the second model. The production allocation is then implemented.

DETAILED DESCRIPTION

Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and/or the order of some operations may be changed.

FIG. 1illustrates an example of a system100that includes various management components110to manage various aspects of a geologic environment150(e.g., an environment that includes a sedimentary basin, a reservoir151, one or more faults153-1, one or more geobodies153-2, etc.). For example, the management components110may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment150. In turn, further information about the geologic environment150may become available as feedback160(e.g., optionally as input to one or more of the management components110).

In the example ofFIG. 1, the management components110include a seismic data component112, an additional information component114(e.g., well/logging data), a processing component116, a simulation component120, an attribute component130, an analysis/visualization component142and a workflow component144. In operation, seismic data and other information provided per the components112and114may be input to the simulation component120.

In an example embodiment, the simulation component120may rely on entities122. Entities122may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system100, the entities122can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities122may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data112and other information114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

In an example embodiment, the simulation component120may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Wash.), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

In the example ofFIG. 1, the simulation component120may process information to conform to one or more attributes specified by the attribute component130, which may include a library of attributes. Such processing may occur prior to input to the simulation component120(e.g., consider the processing component116). As an example, the simulation component120may perform operations on input information based on one or more attributes specified by the attribute component130. In an example embodiment, the simulation component120may construct one or more models of the geologic environment150, which may be relied on to simulate behavior of the geologic environment150(e.g., responsive to one or more acts, whether natural or artificial). In the example ofFIG. 1, the analysis/visualization component142may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component120may be input to one or more other workflows, as indicated by a workflow component144.

As an example, the simulation component120may include one or more features of a simulator such as the ECLIPSE′ reservoir simulator (Schlumberger Limited, Houston Tex.), the INTERSECT′ reservoir simulator (Schlumberger Limited, Houston Tex.), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc.).

In an example embodiment, the management components110may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (Schlumberger Limited, Houston, Tex.). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

In an example embodiment, various aspects of the management components110may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (Schlumberger Limited, Houston, Tex.) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Wash.) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

FIG. 1also shows an example of a framework170that includes a model simulation layer180along with a framework services layer190, a framework core layer195and a modules layer175. The framework170may include the commercially available OCEAN® framework where the model simulation layer180is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization.

As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

In the example ofFIG. 1, the model simulation layer180may provide domain objects182, act as a data source184, provide for rendering186and provide for various user interfaces188. Rendering186may provide a graphical environment in which applications can display their data while the user interfaces188may provide a common look and feel for application user interface components.

As an example, the domain objects182can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

In the example ofFIG. 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer180may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer180, which can recreate instances of the relevant domain objects.

In the example ofFIG. 1, the geologic environment150may include layers (e.g., stratification) that include a reservoir151and one or more other features such as the fault153-1, the geobody153-2, etc. As an example, the geologic environment150may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment152may include communication circuitry to receive and to transmit information with respect to one or more networks155. Such information may include information associated with downhole equipment154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment156may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example,FIG. 1shows a satellite in communication with the network155that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1also shows the geologic environment150as optionally including equipment157and158associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment157and/or158may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

As mentioned, the system100may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).

Embodiments of the system and method disclosed herein integrate exposed analogue models (e.g., into PETREL®) with other types of input data (e.g., seismic and well logs), while adding confidence and reducing uncertainty. As described in greater detail below, the system and method include automated and quantitative analysis of exposed analogues; automated geostatistical analysis of the reservoir properties; sedimentary forward modelling; synthetic seismic generation and matching to subsurface seismic data; and training data generation for subsurface interpretation constrained by geological rules derived from analogues. The resulting reservoir model is then the input for fluid flow simulation technologies. The optimal integration of multi-type datasets may improve the knowledge transfer from ground to sub-surface, increasing the efficiency and consistency in modeling complex reservoirs to ultimately reduce exploration risks and improve reservoir management.

The present disclosure includes an intelligent self-learning system and method that transform the way both conventional and unconventional assets are managed and production operations are optimized. As used herein, “assets” refers to oil fields including one or more producing wells. The system and method may model the data that flows through different functional workflows and hybridize engineering models through an artificial intelligence (AI) system incorporating machine learning techniques to learn patterns in the data and find opportunities through which the efficiency and effectiveness can be improved in production operations. This may also speed up actions and decision making processes.

The system may include a robust forecasting engine enabling a user to accurately report data to the Securities and Exchange Commission (SEC) to reduce a production engineer's analysis time to 0.001% of what it is today. The system may also include an intuitive framework that seamlessly integrates subsurface and production solutions. The system may also incorporate user feedback and enable proactive risk assessment. The system may also process and understand well potential and introduce visibility into drivers of well performance.

FIG. 2illustrates schematic view of a workflow200including three phases, according to an embodiment. The three phases are discussed in detail below.

Phase 1—Automated Forecasting Engine

The automated forecasting engine may be an unbiased engine that provides automated forecasts for production data. Analyzing well performance at a point in time in the future may be used to facilitate decision-making about production potential. The production potential can be calculated using forecasting.

The automated forecasting may include receiving data. The data may be or include historical production data (e.g., oil/gas production per unit time). The automated forecasting may include preprocessing the historical production data (e.g., data cleaning, removal of outliers, etc.). For example,FIG. 3Aillustrates a graph310of the historical production data showing a plurality of outliers, according to an embodiment. More particularly, the graph310shows time on the X-axis and production on the Y-axis, so that the production (e.g., number of barrels) per unit time (e.g., day, week, month, etc.) can be monitored. As may be seen, a curve312may be generated that closely approximates most of the data points on the graph310. However, some of the data points314are more than a predetermined distance away from the curve312, making them outliers. These outliers314may be identified and removed from the data.

The automated forecasting may also include event segmentation. More particularly, events, such as workovers, may be performed on producing wells from time to time. As used herein, a workover may be or include the repair or stimulation of an existing production well for the purpose of restoring, prolonging, or enhancing the production of hydrocarbons (i.e., oil and/or gas). A workover may include performing maintenance or remedial treatments on the well, such as the removal and replacement of the production tubing string after the well has been killed and a workover rig has been placed on location. The events may impact production in the past and/or future. The events are considered and modeled as part of the forecasting workflow.FIG. 3Billustrates a graph320showing event segmentation of the historical production data (e.g., after the outliers314have been removed), according to an embodiment. The graph320shows a first event322and a second event324. In an example, the first event322may be or include a decline happening in an unconventional well when it started producing that may cause the production to fall off dramatically, and the second event324may be or include a workover performed on the well that may cause the production to increase again.

The automated forecasting may also include generating a forecast model. Bayesian modelling may be used to build a forecast model using the data (e.g., in graphs310,320). A forecast is then generated using the model parameters.

The automated forecasting may also include generating an uncertainty assessment. There is uncertainty associated with any forecast (i.e., prediction). The uncertainty is modeled using the forecast model and a variance of the input data.FIG. 3Cillustrates a graph330showing uncertainty quantification of the production forecast, according to an embodiment. The uncertainty assessment provides confidence bands (10thpercentile and 90thpercentile) within which 80% of the production prediction may lie in the future.

FIG. 3Dillustrates a graph340(e.g., a histogram) showing the accumulated production that may be used to estimate the estimated ultimate recovery (EUR) and reserves in the subterranean formation, according to an embodiment. Once the forecasts are generated, EUR and reserves may be estimated. Since there is uncertainty about the forecast, there is uncertainty associated with EUR and reserves as well. As such, a distribution (e.g., histogram) may be used to explain the EUR instead of a single value. The outcome of this phase is an automated forecasting engine that provides automated forecasts for production data. Users of this engine can generate an unbiased estimate of their production potential, which helps them with their SEC reserves reporting and company valuation.

Phase 2: Integrate Subsurface Data and Production Data Through a Statistical Framework

This phase may include an intuitive statistical framework that stitches subsurface data to the (e.g., historical) production data. The intuitive engine may predict events (e.g., workovers), suggest workover upside, rank drivers of well performance, cluster similar wells and entities of interest to an asset manager, and investigate what-if scenarios. These automated operations may help production engineers proactively address risks in real-time production operations. The engine may assimilate different sources of data such as static data (e.g., reservoir properties), dynamic data (e.g., petrophysical properties), operating constraints (e.g., client-specific constraints), and the production history and prediction generated from phase 1. These sources of data may be used to model causal-effect relationships, group similar candidates, and identify and rank drivers of influence and subsurface-production integration. In this phase, the system may begin with unsupervised predictions, but feedback may be used so that the models can train themselves and become more robust over time.

The models for these features may help solve actual problems in the real-time production world. In one example, the models may be used for sweet spot identification. As used herein, the “sweet spot” refers to a target location or volume within a reservoir that represents the best production or potential production.FIG. 4Aillustrates a graph410showing sweet spot identification in the subterranean formation, according to an embodiment. The wells may be aggregated. Then, fertile zones may be identified from the production history data. As shown, the zone412is fertile (e.g., oil and/or gas), and the zone414is not fertile (e.g., water). Then, the machine-learning model may be applied. From this, zones with maximum drainage area(s) may be determined. In response to this, the sweet spot(s)412in the subterranean formation may be identified.

In another example, the models may be used to determine production allocation.FIG. 4Billustrates production allocation between different stages422A,422B a well420, to an embodiment. As shown, the first (e.g., upper) stage422A may be in fluid communication with oil/gas, and the second (e.g., lower) stage422B may be in fluid communication with an aquifer (e.g., water). The graph424A shows the production of oil/gas if the first stage422A produces. The graph424B shows the production of oil/gas and water if the first and second stages422A,422B produce. As may be seen, allocating production between both the first and second stages422A,422B may reduce the amount of oil/gas that is produced/recovered. Thus, the user may decide to produce from the first stage422A but not the second stage422B.

In at least one embodiment, the production may be back-allocated. This may include reviewing the production history, finding events of interest, and mapping the events to static and/or dynamic data. Then, one or more zones of interest may be identified (e.g., proximate to the first stage422A). The machine-learning model may then be applied to the zone(s) of interest. A workover may be planned using results of the model.

In another example, the models may be used to select which stages and/or wells may benefit from being re-fractured.FIG. 4Cillustrates a schematic view of two wells430A,430B that are candidates for re-fracturing, according to an embodiment. The first well430A may include two stages432A,434A, and the second well430B may include three stages432B,434B,436B. The machine-learning model may be applied to analyze the production potential at one or more of the stages432A,434A,432B,434B,436B. The graph438A shows the production potential for the first well430A if one or more of the stages432A,434A is re-fractured, and the graph438B shows the production potential for the second well430B if one or more of the stages432B,434B,436B is re-fractured. As shown, the first graph438A may have a smaller spike439A, and the second graph438B may have a larger spike439B. The spikes438A,438B may indicate increased production (e.g., in response to re-fracturing). Thus, from this, the user may determine that it may be beneficial to re-fracture the first well430A, but it may be even more beneficial to fracture the second well430B. The model may calculate the production potential of the zones of interest and use the information about oil produced during one or more fracking cycles to determine the potential producibility of each stage during re-fracking.

In yet another example, the models may be used to design well spacing.FIG. 4Dillustrates a top view of two wells440A,440B spaced laterally apart from one another, according to an embodiment. The wells440A,440B may be aggregated. Each well440A,440B may have one or more stages442A,442B. Fertile zones may be identified from the production history. Then, the machine-learning model may be applied. Then, a well location for a new well may be suggested.

Phase 3: Autonomous Oilfield Advisor

As the system is trained intelligently over time, it may evolve from being an unsupervised system to a supervised system. With that, the user may be able to identify clusters of wells. In an example, one cluster may have wells that are similar based on features derived from rock properties, and another cluster may have wells that are similar based on production signatures. This can help speed up actions or decisions. In addition, to reduce unit cost/barrel and to keep wells healthy and running longer, workovers may be reduced, thereby reducing downtime. Currently, there is a lot of dependency on engineers, field personnel, lease operators, and foremen to accomplish this. They rely on their intuition and their experience, but there is a bias that comes with it, which may translate into remedial actions that may or may not be helpful. The system and method disclosed herein may study the features of the ML engine over time to identify candidates for workovers. The system and method may help manage by exception, and intervene when there is a problem. The system and method may not suggest a workover just because the production seems to have declined when it may be just a natural decline. Rather, the system and method may selectively intervene when a predetermined threshold is reached. Further, the same learning can be applied to wells with artificial lifts to proactively predict and prevent an impending failure from occurring and prescribe a remedial action. In this manner, the system and method can keep wells healthy and running for longer and act as an advisor for optimizing production operations. By the third phase, it may mimic the brain, intuition, and over time, inculcate experience of field personnel.

Elements of Artificial Intelligence (AI)

The AI may include active learning. Thus, the system may learn from the data and model complex phenomena and build an intuition for it. The system may also be able to predict what is going to happen. The system may also provide recommended actions. The system may also perform contextual adaptation. More particularly, the system may apply learning from one entity (e.g., a well) and apply it to another entity (e.g., another well). When a new scenario is encountered, the system may look into the past to find the best course of action at that time.

In an example, in the first phase, an automated system for production forecasting may be built. Intelligence and automation may be embedded in one of the workflows for production and improve efficiency/effectiveness of the production engineers. The system may start to understand complex data, understand outliers, bad (i.e., inaccurate) sensor data, flow regimes, and events and also build its predictive capability.

The system may also have the potential to improve production operations. The second phase focuses on hybridizing engineering models through AI and leveraging engines to help operators make meaningful decisions, optimize operations, and make better decisions. In this wave, the AI system may make progress on the elements by learning more about well characteristics, predict where the sweet spot is, which stage(s) should be re-fracked, suggest where the new well should be placed, and build more intelligence around what is the best action on the well.

The third phase may build it into a completely autonomous oilfield advisor. To build a completely self-learning system, the system and method may use training data and training of a large number of intelligent components.

The system and method disclosed herein may reshape the way production problems are solved. As the system and method evolve, they may help reduce cost and increase production and uptime. By understanding reservoirs and the subsurface better, knowing the production potential with more certainty, modeling drivers of well performance, predicting the impact of interventions in advance, the user can better design surface facilities and optimize decision and actions taken on the surface.

FIG. 5illustrates a flowchart of a method500for optimizing oil production, according to an embodiment. The method500may include receiving historical production data for one or more wells, as at502. This is shown inFIG. 3A. As described above, the historical production data may describe the production rate (e.g., of oil and/or gas) over time from the one or more wells. The historical production data may include a plurality of data points, each at a different time. For example, each data point may correspond to production during a particular hour, day, week, etc. The historical production data may be measured/captured by sensors placed at wellhead controllers measuring the production rate. Water cut meters/sensors may also be used which measure the water cut of the flowing oil. Knowing the water cut and total production, the amount of oil and water produced may be calculated. The data goes into the data acquisition systems, the models may be built using the data stored in databases within the data repositories.

The method500may also include generating a first model using the historical production data, as at504. The first model may be generated by using statistical modeling techniques to fit a non-linear parametric model to the historical production data. The method500may also include generating a curve312using the first model, as at506. The curve may correspond to the historical production data. This is also shown inFIG. 3A. The curve312may be generated by using a non-linear parametric fit of the historical production data that is closest to the actual data behavior.

The method500may also include identifying and removing data points in the historical production data that are outliers314(e.g., with respect to the curve312), as at508. This is also shown inFIG. 3A. This may include removing any data points that are more than a predetermined distance from the curve312. In at least one embodiment, the first model and/or the curve312may be generated (or re-generated) using the remaining data points after the outliers314are removed.

The method500may also include segmenting the historical production data into one or more portions322,324corresponding to different events (e.g., workovers), as at510. This is shown inFIG. 3B. This may occur after the data points314are removed. The segmenting may help the user understand when the well that was undergoing natural or uninterrupted production decline had workover operations performed on it that caused production to increase. By isolating the production history into different workover events, the user may better understand when the latest workover event occurred, fit the curve312, and/or predict the future production using the historical production data from the last (i.e., most recent) workover event. The method500may also include determining an uncertainty of the historical production data, as at512. The uncertainty may be determined based at least partially upon the first model. In at least one embodiment, confidence bands (e.g., 10thpercentile-90thpercentile) may be used to determine the uncertainty estimates of the historical production data.

The method500may also include predicting production from the one or more wells (e.g., in the future), as at514. The production may be predicted based at least partially upon the historical production data (e.g., after the outliers are removed), the first model, the events that occurred during the history, and/or the uncertainty of the historical production data. In one embodiment, the first model may be a cure fit model that is used to predict the future production.

The method500may also include determining an uncertainty of the predicted production, as at516. The uncertainty of the predicted production may be determined based at least partially upon the first model, the uncertainty of the historical production data, and/or the predicted production.

The method500may also include estimating reserves in the subterranean formation, as at518. The reserves may be estimated based at least partially upon the first model, the predicted production, and/or the uncertainty of the predicted production. Estimating the reserves may include determining overall production potential of the well from first day of production to the end of life of the well. Then, EUR estimates may be determined based at least partially upon the historical production data and the predicted production.

The method500may also include generating a second model based at least partially upon the predicted production, as at520. In at least one embodiment, the second model may also be generated based at least partially upon the historical production data (e.g., after the outliers are removed), the first model, the events that occurred during the history, the uncertainty of the historical production data, the uncertainty of the predicted production, and/or the estimated reserves.

In at least one embodiment, the second model may also be generated based at least partially upon static data, dynamic data, and/or operating constraints. The static data may be or include reservoir properties such as rock and fluid properties, porosity, permeability, and compressibility. The dynamic data may be or include petrophysical properties such as resistivity, density, and reservoir properties (e.g., pressure). The dynamic data may be part of one or more well logs. The static data and/or the dynamic data may be captured by a downhole tool in one of the wells (e.g., a measurement-while-drilling (MWD) tool or a logging-while-drilling (LWD) tool), or captured at the surface. The operating constraints may be or include (e.g., production) tubing diameters, choke pressures, rates of water injection (e.g., into the wells), motor current in cases of wells with an artificial lift.

The method500may also include determining a sweet spot in the subterranean formation, as at522. The sweet spot may be determined at least partially based upon the second model. In one example, the sweet spot may be determined by using spatial locations of zones with producible oil. This can be determined by modeling the historical production of the one or more wells in a basin, the completions design, the geophysical properties, and/or the geological maps.

The method500may also include determining a production allocation in the one or more wells to optimize/maximize (e.g., oil) production, as at524. Determining how to allocate production may be based at least partially upon the second model and/or the location of the sweet spot. The allocation may be determined by using surface production measured across oil and water and back-calculating it to determine the production across zones of interest. For example, as described above with respect toFIG. 4B, the well420may include one or more stages422A,422B, and the production may be allocated between the stages422A,422B (e.g., 80% from the first stage422A and 20% from the second stage422B). The production may be allocated to maximize production from one particular well or to maximize an aggregate production from a plurality of wells, and the allocations selected for these two options may be different. The allocation may be physically performed (i.e., implemented) by selecting the best completion design to produce from the zone of interest. In another embodiment, the allocation may be physically performed by running and setting one or more plugs in the well and/or actuating one or more valves.

The method500may also include determining whether to re-fracture one or more stages in the one or more wells, as at526. This may be shown inFIG. 4C. The determination of which stage(s) to re-fracture may be based at least partially upon the second model, the location of the sweet spot, and/or the production allocation. By modeling the production behavior of the wells and their frac stages during the first fracking cycle, knowing the completion design and the reservoir's drainage area, the machine learning model may learn the characteristics of each zone, and the model can then be used in time to predict the performance during or after re-fracking. The one or more wells may be physically re-fractured, in response to block526, by injecting pressurized fluid into the one or more wells.

The method500may also include determining an optimal spacing between the one or more wells and a new well, as at528. The optimal spacing may be based upon the second model, the location of the sweet spot, the production allocation, and/or the re-fracturing. The spacing may be optimal when it is such that none of the sweet spots or producible zones are left undrained. However, at the same time, the spacing between two wells may be such that these wells do not interfere with each other's producible zones. The new well may be drilled in response to block524. More particularly, a downhole tool may drill the new well, and the downhole tool may be steered such that a trajectory of the new well maintains the optimal spacing.

FIG. 6illustrates an example of such a computing system600, in accordance with some embodiments. The computing system600may include a computer or computer system601A, which may be an individual computer system601A or an arrangement of distributed computer systems. The computer system601A includes one or more analysis module(s)602configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module602executes independently, or in coordination with, one or more processors604, which is (or are) connected to one or more storage media606. The processor(s)604is (or are) also connected to a network interface607to allow the computer system601A to communicate over a data network609with one or more additional computer systems and/or computing systems, such as601B,601C, and/or601D (note that computer systems601B,601C and/or601D may or may not share the same architecture as computer system601A, and may be located in different physical locations, e.g., computer systems601A and601B may be located in a processing facility, while in communication with one or more computer systems such as601C and/or601D that are located in one or more data centers, and/or located in varying countries on different continents).

A processor can include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

The storage media606can be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment ofFIG. 6storage media606is depicted as within computer system601A, in some embodiments, storage media606may be distributed within and/or across multiple internal and/or external enclosures of computing system601A and/or additional computing systems. Storage media606may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLU-RAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above can be provided on one computer-readable or machine-readable storage medium, or in another embodiment, can be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture can refer to any manufactured single component or multiple components. The storage medium or media can be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

In some embodiments, computing system600contains one or more production optimization module(s)608. In the example of computing system600, computer system601A includes the production optimization module608. In some embodiments, a single production optimization module may be used to perform at least some aspects of one or more embodiments of the method500. In other embodiments, a plurality of production optimization modules may be used to perform at least some aspects of the method500.

It should be appreciated that computing system600is one example of a computing system, and that computing system600may have more or fewer components than shown, may combine additional components not depicted in the example embodiment ofFIG. 6, and/or computing system600may have a different configuration or arrangement of the components depicted inFIG. 6. The various components shown inFIG. 6may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods are illustrated and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principals of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.