OPTIMIZING HYDROCARBON RECOVERY THROUGH INTEGRATED UTILIZATION OF GEOMECHANICS AND INJECTION/PRODUCTION USING MACHINE LEARNING

Systems and methods include a computer-implemented method for optimized injection/production and placement of wells. 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 stress change correlations. Fracture growth/propagation behavior for the reservoir is determined 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 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. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.

TECHNICAL FIELD

The present disclosure applies to optimizing hydrocarbon recovery.

BACKGROUND

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.

SUMMARY

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.

DETAILED DESCRIPTION

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.,FIG.1) for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs of the present disclosure can be related in the following way, as an example. The steps of the workflow are not intended to limit the scope or nature of the relationships between the various elements, and the following examples are presented as illustrative examples only. The relationships and the stress distribution in the reservoir are required to determine the time and location of well placement, fracture design, and orientation needed for optimum recovery of hydrocarbons. Then, application can be made of injection/production of fluids with the previous information and stress distribution, both vertically and spatially. The process can be repeated for all patterns or blocks in the reservoir.

The workflow ofFIG.1for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs generally can include the following. Relationship information is obtained that is associated with relationships between fluids withdrawal/depletion and geomechanics/stress distribution. The relationship information is based on data collected from injection/production of wells and the stress distribution in the reservoir. The relationship information can be used to provide information about azimuths and maximum/minimum stress directions, existing fracture orientations, and fracture size and orientation needed for optimum recovery of hydrocarbons, based on an analysis of the relationship. The relationship information can define stress distributions in the reservoir, and can be used to modify stress distributions in the reservoir, e.g., based on an application of injection/production of fluids with the previous information on stress distribution, both vertically and areally.

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.

FIG.1is a flow diagram of an example of a workflow100for a single model in two dimensions (2D), according to some implementations of the present disclosure. The workflow100can be used to optimize hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs. The workflow100can also be used to manage and optimize field development, depletion, and well placement plans. This can provide not only well-level but a field-wide prior knowledge of geomechanics/fracture behavior and well depletion/placement coupled with machine learning (ML). The ML can be initially trained and fed with data from geomechanical reservoir simulation models and fracture propagation models. The ML can be also be provided with field data as it becomes available to update, in order to run scenarios to optimize future well placement and for depletion planning. As ML captures the complex relationship between the important inputs and outputs, an ML model can be initially fed and trained by simulation data and field data as it becomes available.

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.

At102, reservoir and geomechanical data are collected to build a geomechanical reservoir simulation model. At104, 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. At106, fracture growth/propagation behavior under existing stress distributions is generated using fracture modeling software and geomechanical properties to optimize well treatment. At108, 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. At110, well injection and production data are collected for each reservoir layer that is completed. At112, 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. At114, steps102-112are 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).

FIG.2is a graph200showing an example of a single-well model, according to some implementations of the present disclosure. The single well model contains a lateral section and a fracture in the vicinity of a wellbore (FIG.1).

FIG.3is a three-dimensional plot300showing a location308of a single well in the model, according to some implementations of the present disclosure. The plot300is plotted relative to an i-direction302, a j-direction304, a k-direction306, and intensity keys310and312.

FIG.4is a plot400showing an example of a single well pressure distribution410for a single well model, according to some implementations of the present disclosure. The distribution is plotted relative to an i-direction402, a j-direction404, and k-direction406, with intensities indicated by a pressure intensity key408. The fracture permeability nearby the wellbore is 20 md, and the fracture tip is 5 md. During analysis of the techniques of the present disclosure, a horizontal well model was developed using a Prosper simulator to generate a tubing curve (FIG.5). In this case, the well model uses a correlation of Kuchuk and Goode models.

FIG.5is a graph500showing an example of tubing performance plots502, according to some implementations of the present disclosure. In this example, two modified models have been set up by multiplying the matrix permeability of a base-case for 10× and 100×. The plots502are plotted relative to a gas rate axis504(e.g., in cubic feet per day (ft3/day) and a bottom hole pressure (BHP)506(e.g., in psi). The results of three models inFIG.6andFIG.7show that there is significant difference in oil cumulative and gas cumulative performance. The difference of gas cumulative is up to 400,000 cubic feet, and oil cumulative is up to 4,000 bbl.

FIG.6is a diagram600showing an example of a first part of a case study, according to some implementations of the present disclosure. The study starts with a base case602(one well), with one permutation604, 10 permutations606, and 100 permutations608.

FIG.7is a graph700showing examples of cumulative oil plots702,704, and706for three base cases, according to some implementations of the present disclosure. The plots702,704, and706are plotted relative to dates708and cumulative oil710(e.g., in bbl).

FIG.8is a graph800showing examples of cumulative gas plots802,804, and806for three base cases, according to some implementations of the present disclosure. The plots802,804, and806are plotted relative to dates808and cumulative gas810(e.g., in standard cubic feet (scft)).

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 inFIG.9.

FIG.9is a diagram900showing examples of grid names902-910for a shear strain and shear stress investigation, according to some implementations of the present disclosure. The grid names902-910form aggregate912.

FIG.10is a diagram1000showing one of the grids investigated for shear strain and shear stress, according to some implementations of the present disclosure. In this example, CMOST results show the optimum solution that can be obtained as a gas cumulative of 9.8×108ft3(FIG.11). From the optimum solution, the shear strain and shear stress can be investigated in the certain grid blocks. For example, regarding grid block 106,106,3,FIG.12exhibits the shear strain in block 106,106,3 in three directions. The shear strain in the IJ direction is a positive. The shear strain in the JK direction1016is a negative. The shear strain in the IK direction has an alternating direction from a negative to a positive direction, relative to points and axes1002to1016.

FIG.11is a graph1100showing example experimental design results by CMOST, according to some implementations of the present disclosure. Results for a base case1102, general solutions1104, and an optimal solution1106are plotted relative to experiment identifier (ID)1108and cumulative gas1110.

FIG.12is a graph1200showing example shear strains in block 106,106,3, according to some implementations of the present disclosure. The shear strains, in the IJ direction1202, in the IK direction1204, and in the JK direction1206are plotted relative to time1208and shear strain1210.

FIG.13is a three-dimensional plot1300showing examples of different phenomena of shear strain1310between the toe and the heel of the well within the IJ direction, according to some implementations of the present disclosure. One side of the heel measures the positive number of shear strain, but the toe measures the negative one. Conversely, the phenomena occurs in an opposite way for other side. Shear strains are plotted relative to i-coordinates1302, j-coordinates1304, and k-coordinates1306. Intensities of the shear strains are indicated by shading in an intensities key1308.

FIG.14is a graph1400showing example the shear stress in block 106,106,3 for three axes, according to some implementations of the present disclosure. The shear stress in IJ direction is also positive. The shear stress in JK direction is a negative. The shear stress in the IK direction changes from negative to positive. The shear strains, in the IJ direction1402, in the IK direction1404, and in the JK direction1406are plotted relative to time1408and shear strain1410.

FIG.15is a three-dimensional virtual plot1500showing examples of a dominance of the shear stress in the IJ direction within layers of grid 2 and grid 3, according to some implementations of the present disclosure. In this example, the shear stresses approach 6 psi, as indicated by shading in an intensities key1508. Shear strains are plotted relative to an i-direction1502, a j - direction1504, and a k- direction1506.

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 (FIG.16). Another horizontal well can be established beside the parent well. The new well can have a similar profile as its parent. For example, the distance between two wells can be 1500 ft, and the distance between tips of fracture can be 500 ft.

FIG.16is a diagram1600showing an example of a second part of a case study (parent and child), according to some implementations of the present disclosure. The study starts with a base case602(one well), with one permutation604, 10 permutations606, and 100 permutations608. The position of the child well is right next to the parent well. The child well has the same completion as the parent well with 20 fractures. The horizontal length of the two wells is 5000 ft. The same technique has been conducted for the three models with respect to variant matrix permeability, 100 dataset pressure, and the grid blocks for shear strain and shear stress.

FIG.17is a plot1700showing an example of a gas saturation distribution of a parent well1708and a child well1710, according to some implementations of the present disclosure. The gas saturation distribution is shown along both the wellbore and the vicinity of the wellbore. The pressure drawdown in the child well1710(“PROD2”) is greater than the parent well1708. The impact of pressure drawdown is that gas production from the child well1710is greater than the parent well1708. The distribution is plotted relative to an i-direction1702, a j-direction1704, and a k-direction1706, with intensities indicated by a pressure intensity key1712.

FIGS.18A and18Bare graphs1800,1850showing examples of cumulative gas plots for the parent well and the child well, according to some implementations of the present disclosure. Referring toFIG.18Afor the parent well, the graph1800includes plots1802,1804, and1806for three base cases. The plots1802,1804, and1806are plotted relative to time1808and cumulative gas1810(e.g., in millions of cubic feet (mcft)). Referring toFIG.18Bfor the child well, the graph1850includes plots1802,1804, and1806for the three base cases. The plots1802,1804, and1806are plotted relative to time1808and cumulative gas1810(e.g., in millions of cubic feet (mcft)).

FIG.19is a plot1900showing an example of a gas saturation distribution of a parent well1908and a child well1910, according to some implementations of the present disclosure.FIG.19shows a significant difference of both performance of gas cumulative. The child well has a production over 10× the production of the parent one, which is due to the high pressure drawdown in the child well. The gas saturation distribution is shown inFIG.19along both the wellbore and the vicinity of the wellbore. The pressure drawdown in the child well1910(“PROD2”) is greater than the parent well1908. The impact of pressure drawdown is that gas production from the child well1910is greater than the parent well1908. The gas saturation distribution is plotted relative to an i-direction1902, a j-direction1904, and a k-direction1906, with intensities indicated by a pressure intensity key1912(e.g., in psi).

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 (FIGS.20and22). Meanwhile, the block 106,106,3 has the same performance for both of shear strain and shear stress (FIGS.21and23).

FIG.20is a 3D view showing examples of shear strain in a parent well2002and a child well2004, according to some implementations of the present disclosure. The shear strain is plotted relative to an i-direction2006, a j-direction2008, and a k-direction2010, with intensities indicated by a shear strain key2012.

FIG.21is a graph2100showing example shear strains in block106,106,3 from the parent and child model, according to some implementations of the present disclosure. The shear strains, in the IJ direction2102, in the IK direction2104, and in the JK direction2106are plotted relative to time2108and shear strain2110.

FIG.22is a 3D view showing examples of shear stress in a parent well2202and a child well2204, according to some implementations of the present disclosure. The shear stress is plotted relative to an i-direction2206, a j-direction2208, and a k-direction2210, with intensities indicated by a shear strain key2212.

FIG.23is a graph2300showing example shear stress in block106,106,3 from the parent and child model, according to some implementations of the present disclosure. The shear stresses, in the IJ direction2302, in the IK direction2304, and in the JK direction2306are plotted relative to time2308and shear strain2310.

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 (FIG.23).

FIG.24is a graph2400showing example well head pressures2202and2204of the parent well and the child well, according to some implementations of the present disclosure. The well head pressures2202and2204are plotted relative to time2406and WHP2408.

Machine Learning Model Application

FIGS.25-30show example results of using models and predictions, according to some implementations of the present disclosure. For example, the results can be achieved using ML models applied to the data described previously, where cumulative production, pressure change, strain changes are modeled and predicted.

FIG.25includes a graph2500showing examples of machine learning model errors for different methods for cumulative condensate production at 12 months, according to some implementations of the present disclosure. Errors for different types of machine learning techniques2502are plotted in the graph2500relative to an error2504normalized to one and an error percentage2506.

FIG.26includes a graph2600showing examples of predictions made using machine learning models for cumulative condensate production at 12 months, according to some implementations of the present disclosure. Predictions of cumulative (cum) condensate production2606are made for different cases2604for different types of ML techniques2602.

FIG.27includes a graph2700showing examples of machine learning model errors for different methods for pressure change estimation at 500-ft away from a wellbore at 12 months, according to some implementations of the present disclosure. Adjusted R-squared errors for different types of machine learning techniques2702are plotted in the graph2700relative to an error2704normalized to one and an error percentage2706.

FIG.28includes a graph2800showing examples of predictions made using machine learning models for pressure change estimation at 500-ft away from wellbore at 12 months, according to some implementations of the present disclosure. Pressure change predictions2806for different types of machine learning techniques2802are plotted in the graph2800relative to cases2804.

FIG.29includes a graph2900showing examples of machine learning model errors for different methods for strain change in z-direction Estimation at 500-ft away from wellbore at 12 months, according to some implementations of the present disclosure. Adjusted R-squared errors for different types of machine learning techniques2902are plotted in the graph2900relative to an error2904normalized to one and an error percentage2906.

FIG.30includes a graph3000showing examples of prediction of machine learning models methods for strain change in a z-direction, according to some implementations of the present disclosure. Estimations are at 500 feet away from wellbore at 12 months. Strain change predictions3006for different types of machine learning techniques3002are plotted in the graph3000relative to cases3004.

FIG.31is a flowchart of an example of a method3100for determining optimum stress distributions for the placement of new wells, according to some implementations of the present disclosure. For clarity of presentation, the description that follows generally describes method3100in the context of the other figures in this description. However, it will be understood that method3100can be performed, for example, by any suitable system, environment, software, and hardware, or a combination of systems, environments, software, and hardware, as appropriate. In some implementations, various steps of method3100can be run in parallel, in combination, in loops, or in any order.

At3102, 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 toFIG.14. From3102, method3100proceeds to3104.

At3104, 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 toFIGS.14,15,22, and23. From3104, method3100proceeds to3106.

At3106, 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 toFIGS.1,4,15, and16. From3106, method3100proceeds to3108.

At3108, 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. From3108, method3100proceeds to3110.

At3110, 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 toFIGS.14,15,22, and23. From3110, method3100proceeds to3112.

At3112, 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. From3112, method3100proceeds to3114.

At3114, an optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells. After3114, method3100can stop.

In some implementations, method3100further 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 toFIG.4).

In some implementations, method3100further 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 toFIG.10).

In some implementations, method3100further 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 toFIG.13).

In some implementations, method3100further 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 toFIG.17).

In some implementations, method3100further 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 toFIG.20).

In some implementations, method3100further 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 toFIG.22).

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.

FIG.32is a block diagram of an example computer system3200used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures described in the present disclosure, according to some implementations of the present disclosure. The illustrated computer3202is intended to encompass any computing device such as a server, a desktop computer, a laptop/notebook computer, a wireless data port, a smart phone, a personal data assistant (PDA), a tablet computing device, or one or more processors within these devices, including physical instances, virtual instances, or both. The computer3202can include input devices such as keypads, keyboards, and touch screens that can accept user information. Also, the computer3202can include output devices that can convey information associated with the operation of the computer3202. The information can include digital data, visual data, audio information, or a combination of information. The information can be presented in a graphical user interface (UI) (or GUI).

The computer3202can 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 computer3202is communicably coupled with a network3230. In some implementations, one or more components of the computer3202can 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 computer3202is 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 computer3202can 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 computer3202can receive requests over network3230from a client application (for example, executing on another computer3202). The computer3202can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer3202from 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 computer3202can communicate using a system bus3203. In some implementations, any or all of the components of the computer3202, including hardware or software components, can interface with each other or the interface3204(or a combination of both) over the system bus3203. Interfaces can use an application programming interface (API)3212, a service layer3213, or a combination of the API3212and service layer3213. The API3212can include specifications for routines, data structures, and object classes. The API3212can be either computer-language independent or dependent. The API3212can refer to a complete interface, a single function, or a set of APIs.

The service layer3213can provide software services to the computer3202and other components (whether illustrated or not) that are communicably coupled to the computer3202. The functionality of the computer3202can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer3213, 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 computer3202, in alternative implementations, the API3212or the service layer3213can be stand-alone components in relation to other components of the computer3202and other components communicably coupled to the computer3202. Moreover, any or all parts of the API3212or the service layer3213can 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 computer3202includes an interface3204. Although illustrated as a single interface3204inFIG.32, two or more interfaces3204can be used according to particular needs, desires, or particular implementations of the computer3202and the described functionality. The interface3204can be used by the computer3202for communicating with other systems that are connected to the network3230(whether illustrated or not) in a distributed environment. Generally, the interface3204can include, or be implemented using, logic encoded in software or hardware (or a combination of software and hardware) operable to communicate with the network3230. More specifically, the interface3204can include software supporting one or more communication protocols associated with communications. As such, the network3230or the interface’s hardware can be operable to communicate physical signals within and outside of the illustrated computer3202.

The computer3202includes a processor3205. Although illustrated as a single processor3205inFIG.32, two or more processors3205can be used according to particular needs, desires, or particular implementations of the computer3202and the described functionality. Generally, the processor3205can execute instructions and can manipulate data to perform the operations of the computer3202, including operations using algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure.

The computer3202also includes a database3206that can hold data for the computer3202and other components connected to the network3230(whether illustrated or not). For example, database3206can be an in-memory, conventional, or a database storing data consistent with the present disclosure. In some implementations, database3206can 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 computer3202and the described functionality. Although illustrated as a single database3206inFIG.32, two or more databases (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer3202and the described functionality. While database3206is illustrated as an internal component of the computer3202, in alternative implementations, database3206can be external to the computer3202.

The computer3202also includes a memory3207that can hold data for the computer3202or a combination of components connected to the network3230(whether illustrated or not). Memory3207can store any data consistent with the present disclosure. In some implementations, memory3207can 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 computer3202and the described functionality. Although illustrated as a single memory3207inFIG.32, two or more memories3207(of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer3202and the described functionality. While memory3207is illustrated as an internal component of the computer3202, in alternative implementations, memory3207can be external to the computer3202.

The application3208can be an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer3202and the described functionality. For example, application3208can serve as one or more components, modules, or applications. Further, although illustrated as a single application3208, the application3208can be implemented as multiple applications3208on the computer3202. In addition, although illustrated as internal to the computer3202, in alternative implementations, the application3208can be external to the computer3202.

The computer3202can also include a power supply3214. The power supply3214can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply3214can include power-conversion and management circuits, including recharging, standby, and power management functionalities. In some implementations, the power-supply3214can include a power plug to allow the computer3202to be plugged into a wall socket or a power source to, for example, power the computer3202or recharge a rechargeable battery.

There can be any number of computers3202associated with, or external to, a computer system containing computer3202, with each computer3202communicating over network3230. 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 computer3202and one user can use multiple computers3202.

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.

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.

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.

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.

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.

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.