Patent Application
20230272711
The present disclosure applies to optimizing hydrocarbon recovery.
Well placement decisions made for wells to be fractured require the knowledge of geomechanics, how the stress changes and geomechanics interact with injection and production both spatially and temporally, and the location, direction, and spacing of the wells. Lack of a complete understanding of this knowledge can lead to poor decisions in well placement. In conventional systems and in current applications, geomechanics is taken into account locally when drilling a new well, without the well-and field-level knowledge of future temporal and spatial predictions of relationships between geomechanics and well depletion.
The present disclosure describes techniques that can be used for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs. In some implementations, a computer-implemented method includes the following. Stress change correlations are received over space and time for injection/production of fluids to/from a reservoir. A stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. Fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. Fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical, and flow characteristics. Changes in the stress distribution in the reservoir are determined through injection/production of fluids. An optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.
The previously described implementation is implementable using a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer-implemented system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method, the instructions stored on the non-transitory, computer-readable medium.
The subject matter described in this specification can be implemented in particular implementations, so as to realize one or more of the following advantages. Techniques of the present disclosure can enable teams to make better decisions in well placement, hydraulic fracturing, (re)fracturing, reservoir management, and depletion decisions. This can lead to better field development decisions through improved knowledge and understanding of relationships between injection/production of fluids from a reservoir and the geomechanical behavior/stress changes that control the fracture growth/orientation, and thus well placement. The techniques can use the data/tools (e.g., reservoir simulation and machine learning) from proven processes in the industry and based on reservoir simulation studies that are conducted using geomechanics with industry/benchmark simulators.
The details of one or more implementations of the subject matter of this specification are set forth in the Detailed Description, the accompanying drawings, and the claims. Other features, aspects, and advantages of the subject matter will become apparent from the Detailed Description, the claims, and the accompanying drawings.
Like reference numbers and designations in the various drawings indicate like elements.
The following detailed description describes techniques for optimizing hydrocarbon recovery using hydraulic fractures in hydrocarbon reservoirs. In some implementations, the well placement, fracturing, and fracture design can be optimized based on optimum injection and production of reservoir fluids to/from the reservoir to exploit stress distribution for better placement of well and fractures. Optimum results or optimization can be defined or measured, for example, as achieving results that provide increases in production above a pre-determined threshold (e.g., volume or percentage). In some implementations, techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs can be implemented as software applications that run on a processor of a computing device. Various modifications, alterations, and permutations of the disclosed implementations can be made and will be readily apparent to those of ordinary skill in the art, and the general principles defined may be applied to other implementations and applications, without departing from scope of the disclosure. In some instances, details unnecessary to obtain an understanding of the described subject matter may be omitted so as to not obscure one or more described implementations with unnecessary detail and inasmuch as such details are within the skill of one of ordinary skill in the art. The present disclosure is not intended to be limited to the described or illustrated implementations, but to be accorded the widest scope consistent with the described principles and features.
In some implementations, techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs can include steps for determining the relationship between the stress changes and the injection and production of fluids to/from a reservoir through geomechanical reservoir simulation. The techniques can include modeling and predicting the stress distribution in the reservoir in time and space with new wells drilled and associated reservoir rock depleted, and reservoir characteristics to determine fracture design and orientation needed for optimum well placement for maximum recovery of hydrocarbons. Stress distribution in the reservoir can be exploited through the injection/production of fluids with the understanding of stress distribution vertically/areally and in time.
As previously described, although usually effective, hydraulic fractures need to be designed under the light of reservoir characteristics. Moreover, fracking jobs should be performed carefully because, despite some advantages, controlling the growth and size and maintaining the desired orientation are typically difficult due to rock’s geomechanical behavior being susceptible to changes in stress distributions. The stress distributions include locations in which undesired and/or uncontrollable changes in orientation of fractures may happen along with compaction and dilation. This can result in poor reservoir management and well failures due to stress changes originating from injection and production of fluids into/from the reservoir rock. Due to low matrix permeability, the majority of the hydrocarbon recovery comes from the parts of the reservoir that fractures extend and serve as a conduit for flow. In this sense, the control of fracture size and fracture orientation on the effects of orientation are significant in optimizing recovery.
Implementations of the techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs described in this specification solve such problems by optimizing the mechanisms of recovery associated with differing fracture orientations along with the physics. This causes fracture re-orientation due to stress changes in the rock originating from injection and withdrawal of fluids. The techniques use not only the spatial components but also temporal components involved in the problem. The techniques are useful in optimizing fracture orientations in fracturing of replacement/development wells and/or re-fracturing of existing wells leading to higher recovery of hydrocarbons through more optimum fracture orientations.
Implementations of the techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs described in the present disclosure differ from and improve upon currently existing techniques. In particular, some implementations of the techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs differ by using reservoir stress distribution and inter-well connectivity information to optimize fractures.
In addition, some implementations of the techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs improve upon the currently existing options by using stress measurements so that fracturing and fracture sizes can be optimized by using optimum injection/production of reservoir fluids into/from the reservoir to adjust stress distribution for better placement of fractures.
The techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs of the present disclosure can include various workflows. The lists of possible constituent steps of workflows and methods is intended to be exemplary only and not intended to limiting when optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs. Persons having ordinary skill in the art relevant to the present disclosure will understand that equivalent steps can be substituted without changing the essential function or operation of techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs.
In some implementations, a workflow can include the following. Relationship data is obtained that defines relationships between stress changes in space and time and injection/production of fluids to/from reservoir. Stress distribution data for the reservoir is obtained using reservoir geomechanical modeling tools. Fracture growth/propagation behavior under existing stress distributions data is obtained using fracture modeling software and geomechanical properties to optimize treatment. Relationships are analyzed between: fluid injection/withdrawal and geomechanical changes and resulting stress distributions, the stress distribution in the reservoir, and reservoir geomechanical and flow characteristics. The analysis can be used to determine fracture design and orientation needed for optimum recovery of hydrocarbons. The techniques can use machine learning to recognize patterns and relationships between the injection/production and stress changes/distributions using the geomechanical reservoir simulation inputs and outputs in time, and then to optimize well placement and fracture design without the need for reservoir modelling. Stress distribution in the reservoir can be exploited using injection/production of fluids and by optimizing injection/production and placement of wells, with accordingly-designed fractures. The resulting data can be analyzed, e.g., using machine learning, to adjust injection and production of fluids to/from the reservoir. As a result, an optimum stress distribution can be obtained for placing new wells and fractures in terms of orientation and size to maximize recovery of hydrocarbons.
Various elements of a workflow (e.g.,
The workflow of
Conceptual models can be developed to determine or predict the effect of stress and strain in each fracture in a horizontal well. Initially, a first model, for example, can be established for a single well, and analysis can be performed for a single well. A second model can be established for a pair of wells, including a parent well and a child well, and the analysis can be compared to the analysis of the single well and the first model.
Models can be built, for example, using a Gaussian Emulation Machine (GEM) Simulator and Sensitivity approach designed for 100 datasets each. Both models can use a public data set associated with the Eagle Ford shale reservoir located in South Texas, US. The model type is Cartesian (211 *211 *5). Table 1 shows example values of initial conditions of components of gas condensate. The properties of each component for oil and condensate gas are shown in Tables 2 and 3.
In an example, a base case of a single well model can have a fracture width 50 feet (ft), an intrinsic effective permeability of 50 millidarcies (mD), and a tip permeability of 5 mD. Other parameters in this example include a half-length of fracture of 500 ft, a grid cell width of 50 ft, a bottom hole pressure (BHP) of 4,500 pounds per square inch (psi), and a well head pressure (WHP) of 1,300 psi.
At 102, reservoir and geomechanical data are collected to build a geomechanical reservoir simulation model. At 104, relationship data is obtained between stress changes and distributions in time and space and with respect to injection and production of fluids to and from the reservoir. Stress distribution in the reservoir can be obtained by running geomechanical reservoir simulation models. At 106, fracture growth/propagation behavior under existing stress distributions is generated using fracture modeling software and geomechanical properties to optimize well treatment. At 108, an analysis is performed on the relationship, the stress distribution in the reservoir, and reservoir characteristics to determine optimum time and location for well placement, fracture design and orientation needed for maximum recovery of hydrocarbons. At 110, well injection and production data are collected for each reservoir layer that is completed. At 112, the injection and production data is evaluated to adjust the injection and production to/from the reservoir to obtain an optimum stress distribution to place new wells and fractures in terms of orientation and size to maximize recovery of hydrocarbons using ML. For example, stress distribution in the reservoir can be exploited using injection and production of fluids with the previous information on the relationship and stress distribution, vertically and areally. ML is used to recognize the pattern and relationship between the injection/production and stress changes/distributions using the geomechanical reservoir simulation inputs and outputs in time. At 114, steps 102-112 are continuously repeated to optimize the well placement and subsequent fracture design without the need for reservoir modelling.
Table 2 shows example values of compositional data of an example hydrocarbon-producing geological formation extending over a large region (e.g., Eagle Ford Oil). The values include a molecular weight (MW), a critical temperature (Tc), a critical pressure (Pc) in pounds per square inch absolute (psia), and a critical volume (Vc) measured in cubic feet per pound mass (cft/lbm) for each composition.
Table 3 shows another set of example values of compositional data of an example hydrocarbon-producing geological formation extending over a large region (e.g., Eagle Ford Condensate).
A next stage is to evaluate BHP sensitivity in the three models base cases. As an example, a CMOST simulator can be used to run the sensitivity to save time in the running model. The sensitivity uses 100 different BHP datasets, starting from BHP 3,000 psi through 10,000 psi, with an increment of 70 psi. In this case, the experimental designs that is used include 100 experiments with identifiers (IDs) matching the 100 BHP dataset. The final results to be obtained are shear strain and shear stress in certain grid blocks for 6, 12, 60, and 120 months. The grid blocks investigated in the model are shown in
Another part of the case study is to evaluate the impact of a child well onto the parent well in terms of cumulative production as well as shear strain and shear strain (
The different phenomenon of shear strain and shear stress can be shown to occur in the pair model. The child well has a higher shear strain and shear stress than the parent well (
The parent and child wells are set up with the same method, but the results of the simulation show that the well head pressure (WHP) alteration of the child well has a dynamic rhythm compare to the parent well (
Different types of machine learning techniques can be applied to the dataset. For example, ML techniques can include: Ada Boost Regressor, Bagging Regressor, Bayesian Ridge, Decision Tree Regressor, Dummy Regressor, Elastic Net, Elastic Net cross-validation (CV), Extra Tree Regressor, Extra Trees Regressor, Gamma Regressor, Gaussian Process Regressor, Generalized Linear Regressor, Gradient Boosting Regressor, Histogram (Hist) Gradient Boosting Regressor, Huber Regressor, KNeighbors (k-nearest neighbors) Regressor, Kernel Ridge, Lars, Lars CV, Lasso, Lasso CV, Lasso Lars, Lasso Lars CV, Lasso Lars IC, Linear Regression, Linear Support Vector Regression (SVR), Multi-layer Perceptron (MLP) Regressor, NuSVR, Orthogonal Matching Pursuit, Orthogonal Matching Pursuit CV, Passive Aggressive Regressor, Poisson Regressor, RANdom SAmple Consensus (RANSAC) Regressor, Random Forest Regressor, Ridge, Ridge CV, SGD Regressor, SVR, Transformed Target Regressor, Tweedie Regressor, Extreme Gradient Boosting (XGB) Regressor, and Light Gradient Boosted Machine (LGBM) Regressor.
At 3102, stress change correlations over space and time are received for injection/production of fluids to/from a reservoir. For example, the data collected over time for a reservoir can include data described with reference to
At 3104, a stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. For example, stress distributions can correspond to the shear stresses described with reference to
At 3106, fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. For example, fracture growth can correspond to the fracture growth described with reference to
At 3108, fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical and flow characteristics. From 3108, method 3100 proceeds to 3110.
At 3110, changes in the stress distribution in the reservoir are determined through injection/production of fluids. For example, stress distributions can correspond to the shear stresses described with reference to
At 3112, an optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. From 3112, method 3100 proceeds to 3114.
At 3114, an optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells. After 3114, method 3100 can stop.
In some implementations, method 3100 further includes generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model (for example, as described with reference to
In some implementations, method 3100 further includes generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress (for example, as described with reference to
In some implementations, method 3100 further includes generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of the well within the IJ direction (for example, as described with reference to
In some implementations, method 3100 further includes generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well, according to some implementations of the present disclosure (for example, as described with reference to
In some implementations, method 3100 further includes generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well, according to some implementations of the present disclosure (for example, as described with reference to
In some implementations, method 3100 further includes generating, for display in a user interface, a 3D plot of shear stress in a parent well and a child well, according to some implementations of the present disclosure (for example, as described with reference to
In some implementations, in addition to (or in combination with) any previously-described features, techniques of the present disclosure can include the following. Customized user interfaces can present intermediate or final results of the above described processes to a user. The presented information can be presented in one or more textual, tabular, or graphical formats, such as through a dashboard. The information can be presented at one or more on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or “app”), or at a central processing facility. The presented information can include suggestions, such as suggested changes in parameters or processing inputs, that the user can select to implement improvements in a production environment, such as in the exploration, production, and/or testing of petrochemical processes or facilities. For example, the suggestions can include parameters that, when selected by the user, can cause a change or an improvement in drilling parameters (including speed and direction) or overall production of a gas or oil well. The suggestions, when implemented by the user, can improve the speed and accuracy of calculations, streamline processes, improve models, and solve problems related to efficiency, performance, safety, reliability, costs, downtime, and the need for human interaction. In some implementations, the suggestions can be implemented in real-time, such as to provide an immediate or near-immediate change in operations or in a model. The term real-time can correspond, for example, to events that occur within a specified period of time, such as within one minute or within one second. In some implementations, values of parameters or other variables that are determined can be used automatically (such as through using rules) to implement changes in oil or gas well exploration, production/drilling, or testing. For example, outputs of the present disclosure can be used as inputs to other equipment and/or systems at a facility. This can be especially useful for systems or various pieces of equipment that are located several meters or several miles apart, or are located in different countries or other jurisdictions.
The computer 3202 can serve in a role as a client, a network component, a server, a database, a persistency, or components of a computer system for performing the subject matter described in the present disclosure. The illustrated computer 3202 is communicably coupled with a network 3230. In some implementations, one or more components of the computer 3202 can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments.
At a top level, the computer 3202 is an electronic computing device operable to receive, transmit, process, store, and manage data and information associated with the described subject matter. According to some implementations, the computer 3202 can also include, or be communicably coupled with, an application server, an email server, a web server, a caching server, a streaming data server, or a combination of servers.
The computer 3202 can receive requests over network 3230 from a client application (for example, executing on another computer 3202). The computer 3202 can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer 3202 from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.
Each of the components of the computer 3202 can communicate using a system bus 3203. In some implementations, any or all of the components of the computer 3202, including hardware or software components, can interface with each other or the interface 3204 (or a combination of both) over the system bus 3203. Interfaces can use an application programming interface (API) 3212, a service layer 3213, or a combination of the API 3212 and service layer 3213. The API 3212 can include specifications for routines, data structures, and object classes. The API 3212 can be either computer-language independent or dependent. The API 3212 can refer to a complete interface, a single function, or a set of APIs.
The service layer 3213 can provide software services to the computer 3202 and other components (whether illustrated or not) that are communicably coupled to the computer 3202. The functionality of the computer 3202 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 3213, can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (XML) format. While illustrated as an integrated component of the computer 3202, in alternative implementations, the API 3212 or the service layer 3213 can be stand-alone components in relation to other components of the computer 3202 and other components communicably coupled to the computer 3202. Moreover, any or all parts of the API 3212 or the service layer 3213 can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.
The computer 3202 includes an interface 3204. Although illustrated as a single interface 3204 in
The computer 3202 includes a processor 3205. Although illustrated as a single processor 3205 in
The computer 3202 also includes a database 3206 that can hold data for the computer 3202 and other components connected to the network 3230 (whether illustrated or not). For example, database 3206 can be an in-memory, conventional, or a database storing data consistent with the present disclosure. In some implementations, database 3206 can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. Although illustrated as a single database 3206 in
The computer 3202 also includes a memory 3207 that can hold data for the computer 3202 or a combination of components connected to the network 3230 (whether illustrated or not). Memory 3207 can store any data consistent with the present disclosure. In some implementations, memory 3207 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. Although illustrated as a single memory 3207 in
The application 3208 can be an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. For example, application 3208 can serve as one or more components, modules, or applications. Further, although illustrated as a single application 3208, the application 3208 can be implemented as multiple applications 3208 on the computer 3202. In addition, although illustrated as internal to the computer 3202, in alternative implementations, the application 3208 can be external to the computer 3202.
The computer 3202 can also include a power supply 3214. The power supply 3214 can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply 3214 can include power-conversion and management circuits, including recharging, standby, and power management functionalities. In some implementations, the power-supply 3214 can include a power plug to allow the computer 3202 to be plugged into a wall socket or a power source to, for example, power the computer 3202 or recharge a rechargeable battery.
There can be any number of computers 3202 associated with, or external to, a computer system containing computer 3202, with each computer 3202 communicating over network 3230. Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer 3202 and one user can use multiple computers 3202.
Described implementations of the subject matter can include one or more features, alone or in combination.
For example, in a first implementation, a computer-implemented method includes the following. Stress change correlations are received over space and time for injection/production of fluids to/from a reservoir. A stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. Fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. Fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical, and flow characteristics. Changes in the stress distribution in the reservoir are determined through injection/production of fluids. An optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.
The foregoing and other described implementations can each, optionally, include one or more of the following features:
A first feature, combinable with any of the following features, the method further including generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model.
A second feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress.
A third feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of a well within an IJ direction.
A fourth feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well.
A fifth feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well.
A sixth feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a 3D plot of shear stress in a parent well and a child well.
In a second implementation, a non-transitory, computer-readable medium stores one or more instructions executable by a computer system to perform operations including the following. Stress change correlations are received over space and time for injection/production of fluids to/from a reservoir. A stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. Fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. Fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical, and flow characteristics. Changes in the stress distribution in the reservoir are determined through injection/production of fluids. An optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.
The foregoing and other described implementations can each, optionally, include one or more of the following features:
A first feature, combinable with any of the following features, the operations further including generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model.
A second feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress.
A third feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of a well within an IJ direction.
A fourth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well.
A fifth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well.
A sixth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a 3D plot of shear stress in a parent well and a child well.
In a third implementation, a computer-implemented system includes one or more processors and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors. The programming instructions instruct the one or more processors to perform operations including the following. Stress change correlations are received over space and time for injection/production of fluids to/from a reservoir. A stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. Fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. Fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical, and flow characteristics. Changes in the stress distribution in the reservoir are determined through injection/production of fluids. An optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.
The foregoing and other described implementations can each, optionally, include one or more of the following features:
A first feature, combinable with any of the following features, the operations further including generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model.
A second feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress.
A third feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of a well within an IJ direction.
A fourth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well.
A fifth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well.
Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. For example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.
The terms “data processing apparatus,” “computer,” and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatuses, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, such as LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.
A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code, can be written in any form of programming language. Programming languages can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as stand-alone programs, modules, components, subroutines, or units for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files storing one or more modules, sub-programs, or portions of code. A computer program can be deployed for execution on one computer or on multiple computers that are located, for example, at one site or distributed across multiple sites that are interconnected by a communication network. While portions of the programs illustrated in the various figures may be shown as individual modules that implement the various features and functionality through various objects, methods, or processes, the programs can instead include a number of sub-modules, third-party services, components, and libraries. Conversely, the features and functionality of various components can be combined into single components as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.
The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.
Computers suitable for the execution of a computer program can be based on one or more of general and special purpose microprocessors and other kinds of CPUs. The elements of a computer are a CPU for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from (and write data to) a memory.
Graphics processing units (GPUs) can also be used in combination with CPUs. The GPUs can provide specialized processing that occurs in parallel to processing performed by CPUs. The specialized processing can include artificial intelligence (AI) applications and processing, for example. GPUs can be used in GPU clusters or in multi-GPU computing.
A computer can include, or be operatively coupled to, one or more mass storage devices for storing data. In some implementations, a computer can receive data from, and transfer data to, the mass storage devices including, for example, magnetic, magneto-optical disks, or optical disks. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive.
Computer-readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer-readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read-only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer-readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal/removable disks. Computer-readable media can also include magneto-optical disks and optical memory devices and technologies including, for example, digital video disc (DVD), CD-ROM, DVD+/-R, DVD-RAM, DVD-ROM, HD-DVD, and BLU-RAY. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, the memory can include logs, policies, security or access data, and reporting files. The processor and the memory can be supplemented by, or incorporated into, special purpose logic circuitry.
Implementations of the subject matter described in the present disclosure can be implemented on a computer having a display device for providing interaction with a user, including displaying information to (and receiving input from) the user. Types of display devices can include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), and a plasma monitor. Display devices can include a keyboard and pointing devices including, for example, a mouse, a trackball, or a trackpad. User input can also be provided to the computer through the use of a touchscreen, such as a tablet computer surface with pressure sensitivity or a multi-touch screen using capacitive or electric sensing. Other kinds of devices can be used to provide for interaction with a user, including to receive user feedback including, for example, sensory feedback including visual feedback, auditory feedback, or tactile feedback. Input from the user can be received in the form of acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to, and receiving documents from, a device that the user uses. For example, the computer can send web pages to a web browser on a user’s client device in response to requests received from the web browser.
The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including, but not limited to, a web browser, a touch-screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.
Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server. Moreover, the computing system can include a front-end component, for example, a client computer having one or both of a graphical user interface or a Web browser through which a user can interact with the computer. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication) in a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20 or a combination of protocols), all or a portion of the Internet, or any other communication system or systems at one or more locations (or a combination of communication networks). The network can communicate with, for example, Internet Protocol (IP) packets, frame relay frames, asynchronous transfer mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses.
The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship.
Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from aclaimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as deemed appropriate.
Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations. It should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of the present disclosure.
Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium.
