Patent Application
20250230736
The present disclosure applies to techniques for drilling wells, such as oil and gas wells. Specifically, the present disclosure relates to hydraulic fracturing operations including design, planning and execution.
During the past few decades, technology developments in horizontal drilling and multistage hydraulic fracturing have played an important role in shale gas recovery such as deep and tight gas recovery. Drilling multiple horizontal wells from a pad has increasingly become a common approach in unconventional resource development. Drilling multiple horizontal wells can reduce drilling costs, shorten drilling times, and reduce negative impacts on land and the environment. The combination of horizontal drilling and hydraulic fracturing can significantly increase the production of reservoirs.
The present disclosure describes techniques that can be used for horizontal drilling and multistage hydraulic fracturing. A data processing system is configured to generate and calibrate a multi-component fraccability predictor (FP). The data processing system calibrates a relationship between the FP and the normalized maximum gas short-term production. The data processing system is configured to generate a control signal or generate a visualization that indicates a best interval to place or avoid for cluster depth and hydraulic fracturing stage intervals.
The one or more embodiments described in this specification can enable one or more of the following advantages. The data processing system is configured to identify a relationship among the between subsurface evaluation of the reservoir and reservoir engineering, and well production. The data processing system determines how the rock geomechanics environment impact the hydraulic fracturing execution and the expected early stage production. The data processing system enables better well design by selecting with more accuracy perforation cluster depth and best hydraulic fracturing stages intervals with maximum short-term gas production. The data processing system is configured to identify and rank which geomechanics factors have a greatest impact on hydraulic fracturing and execution.
The one or more foregoing advantages can be enabled by one or more of the following embodiments.
In an aspect, a process is configured for preparing a well for hydraulic fracturing. The process includes receiving well production data from one or more fractured wells in a reservoir. The process includes estimating, using data from fracking tests on previous wells, values of geomechanics factors for the one or more fractured wells in the reservoir. The process includes generating, based on the well production data and the values of the geomechanics factors, a weighting value associated with each geomechanics factor for the reservoir, the weighting value relating a change in a short term production value of a fractured well to a change in the value of that geomechanics factor at the fractured well. The process includes selecting a horizontal well in the reservoir. The process includes generating fraccability predictor values representing ease of fracturing at respective intervals of the horizontal well, the fraccability predictor values each being based of the weighting value associated with each geomechanics factor for the reservoir. The process includes determining based on the ease of fracturing represented by the fraccability predictor values of the respective intervals, a production prediction for the horizontal well. The process includes, based on the production prediction for the horizontal well, determining a cluster spacing in the horizontal well, a stage depth in the horizontal well, or both the cluster spacing and the stage depth in the horizontal well for performing hydraulic fracturing.
In some implementations, the process includes determining a well geometry for each of the one or more fractured wells in the reservoir. The process includes determining a geometry weighting value relating the short term production value of the fractured well to the well geometry of the fractured well. The process includes generating the fraccability predictor values based on the geometry weighting value.
In some implementations, the values of the geomechanics factors include values of in-situ stresses and maximum horizontal stress directions of the one or more fractured wells in the reservoir.
In some implementations, the process includes fracturing the horizontal well based on the cluster spacing in the horizontal well, the stage depth in the horizontal well, or both the cluster spacing and the stage depth in the horizontal well.
In some implementations, the geomechanics factors include a borehole breakdown factor, a minimum horizontal stress factor, a vertical stress anisotropy factor, a minimum horizontal stress azimuth factor, a ductility factor, a horizontal stress anisotropy factor, an elastic and strength brittleness index factor, and a pore pressure factor.
In some implementations, the process includes generating a prediction of a maximum production value for each of the respective intervals. In some implementations, the prediction of the maximum production value for each of the respective intervals is normalized per choke.
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 details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims.
Like reference numbers and designations in the various drawings indicate like elements.
The systems and processes described herein are for predicting short-term well hydrocarbon production after multistage fracturing is applied to a subsurface region. A fraccability predictor (FP) includes a data processing engine that evaluates a set of geomechanics components that are each calibrated against successful and failed stage fracturing of the subsurface. The FP is configured to generate data flagging an interval (such as a best interval) in a well for fracturing. The FP output represents an easiness of fracturing at the interval. A completion geomechanics production predictor (CGPP) generates an index including the interval with the best production generated by the FP. Specifically, the CGPP index specifies, for a location in the horizontal well, a relative likelihood of high hydrocarbon production for a drilled well with respect to other locations in the horizontal well. Specifically, the CGPP generates predictions for early stage well production based on the selection of clusters depth and stage depth. Generally, higher index values represent a higher predicted production for respective selected clusters depth and stage depth.
Hydraulic fracturing of wells can be performed in stages. For example, in a cluster perforation which is commonly used in staged fracturing, a number of hydraulic fractures are generated simultaneously at shooting points that then extend in subsurface formations. When horizontal well staged cluster fracturing is applied in shale gas reservoirs, cluster spacing can affect fracturing performance. For example, if the cluster spacing is below a particular threshold distance, an area that is stimulated between major fractures can be overlapped. The efficiency of fracturing stimulation is decreased due to the overlapping. If the cluster spacing larger than a threshold distance, an area between major fractures cannot be stimulated completely and reservoir recovery can be adversely impacted.
The number of clusters per stage includes the number of perforation guns used per fracture stage. A number of clusters used can be generally in the range of three up to eight clusters per stage. A lower number of clusters per stage may result in longer fracture networks because the hydraulic fracturing energy is limited to fewer entry points. A larger number of clusters can generate a larger surface area near the wellbore per stage. The larger surface area can increase initial hydrocarbon recovery.
A cluster spacing includes a distance between the perforations per lateral length during hydraulic fracturing. In an example, spacing between the clusters can vary from 25 ft up to 100 ft. As the spacing is decreased, the completion size is considered large as more clusters are needed to complete the required area.
A well spacing represents a distance between two wellbores. The location of wells can enhance well productivity when the locations are optimized. The methods and processes described herein are configured to optimize stage intervals and clusters depths placement for stage fracturing operations to increase early stage well production for wells that have begun being drilled in the subsurface. The data processing system helps determine best intervals for the stages and the depth for clusters.
A data processing system executes logic based on the FP to generate data flagging well intervals. The FP identifies well intervals based on a set of weighted geomechanics components. The data processing system weights each of the geomechanics components for the FP based on data gathered from hydraulic fracturing performed at or near the reservoir in which the horizontal well is being drilled. The data processing system combines the weighted geomechanics components to generate, for various points in the horizontal well, predictions for how easily fracturing occurs at the interval. The FP can flag intervals with good fracturing prospects for further production operations.
The data processing system to executes logic comprising the CGPP. The CGPP includes the estimate of the short-term production after multistage fracturing calculated from the fraccability predictor of the horizontal well. Each prediction value is associated with a location in the horizontal well. Specifically, the data processing system generates, based on the flagged intervals, for various points in the horizontal well, predictions for whether there is a higher estimated production or a relatively lower estimated production from that respective point in the well. An index value represents the estimated prediction at each location. The data processing system can normalize the index value to a value between 0 and 1. The data processing system can use the normalized value to weight a fracturing selection process for various locations in the reservoir.
The data processing system calibrates the CGPP based on data representing short-term production from existing wells in the reservoir or in other reservoirs. The CGPP can be initially calibrated from the FP and short term prediction of existing wells and then can be applied for subsequent wells. The resolution for these predictions can be nearly continuous or continuous in the horizontal well. The predictions use the FP as an input along the well to enable continuous resolution of the prediction values. The data processing system executes the logic of the CGPP for optimizing depth selection of clusters and stage for future development wells. The optimal depth selection of the clusters ensures successful hydraulic fracturing design and optimum short-term production.
The data processing system can estimate production for different reservoir types. The CGPP can be calibrated based on a particular reservoir. For example, a reservoir can include heterogeneous late Ordovician tight sand reservoir characterized by relatively high heterogeneity and rapid facies variations. In some implementations, vertical and/or horizontal wells can be drilled in these reservoirs followed by multi-stage hydraulic fracturing operations in order to produce gas from these unconventional resources based on the predictions of the CGPP.
Hydraulic fracturing is a well completion operation used to crack a target reservoir formation 106 via injection of high-pressure water to prepare the well 100 for production and improve the flow of hydrocarbons to the wellbore, for example, in low permeability formations. Fractures 110 are created by cracking or perforating the rocks in the target reservoir formation 106 along the horizontal portion 108 of the well 100. High-pressure water can then be pumped into the fractures 110 to enlarge the fracture width and extent. Once a target reservoir formation 106 is fractured, proppants 112 are pumped into these fractures 106 to keep them open after the hydraulic pressure is reduced.
The control center 132 is shown in
The data processing system 200 of the control center 132 includes a fraccability predictor (FP) engine 202 and a CGPP engine 204. The FP engine 202 is configured to generate the best well interval of the fraccability predictor. The CGPP engine 204 is configured to generate the production index based on the flagged best interval of the FP engine 202. A controller 206 is configured to receive the FP and/or the CGPP and generate a control signal for controlling drilling or hydraulic fracturing operations.
As previously discussed, the FP of the data processing system 200 represents a set of geomechanics factors. The geomechanics factors are weighted based on how much that geomechanics factor impacts a hydraulic fracturing stimulation. Each of the geomechanics factors are determined along a lateral depth of the horizontal well and used to compute the FP. The FP is calculated as shown in Equation (1).
where FP is the Fraccability Predictor value, Sh is a minimum horizontal stress magnitude, FI is a fracture initiation at the borehole wall, B19 is a brittleness index, TSHBrt is an elastic and strength index, FC is a horizontal stress anisotropy, VA is a vertical stress anisotropy, DHaz is a horizontal stress direction, Ss is a minimum horizontal stress and Young's Modulus, and Du is ductility, and where α, β, γ, δ, ε, Σ, θ, μ, and φ are weighting factors. Each of the geomechanics factors is subsequently described in further detail. In some implementations, the equation also includes a factor of borehole geometry (BGeom) and a corresponding weighting value. In some implementations, the equation also includes a factor of pore pressure (Pp) and a corresponding weighting value.
The FP geomechanics component equation is calibrated for generating weighting values. The data processing system performs weighting based on successful and failed stages. The minimum horizontal stress magnitude (Sh) is a geomechanics component that represents that a hydraulic fracture job uses more energy as the minimum horizontal stress magnitude increases. The fracture initiation at the borehole wall (FI) is a geomechanics component that represents that wellbore and/or perforation stresses concentration facilitate or challenge initiation of a hydraulic fracture. In some implementations, a higher weight corresponds to a greater fracture facilitation. A horizontal stress direction (DHaz) is a geomechanics component that specifies that a hydraulic fracture from a wellbore with an azimuth of more than 30 degrees from the minimum horizontal stress reorients itself toward the maximum stress direction. This reorientation creates tortuosity along the hydraulic fracture plane with an increased risk of early screen outs. The vertical stress anisotropy, or risk of horizontal fracture (VA), is a geomechanics component that represents that hydraulic fractures in a high stress environment close to a reverse stress regime are most likely to develop horizontal fractures. A high stress environment will result in a low value of FP. A pore pressure (Pp) is expected to be uniform along lateral and among stages. Differences in pore pressure due to reservoir depletion/injection or other geological reason affect the hydraulic fracture stimulation. The change of pore pressure will be captured by the change of Sh because pore pressure and Sh are inherently dependent. A brittleness index is a geomechanics component that represents a brittleness behavior of a rock. The brittleness component value discriminates stages that can create complex fracture network during stimulation. A greater discrimination does not necessarily impact the weighting factor. The weighting value for each parameter has been calibrated based on successful and unsuccessful hydraulic fracturing jobs. The weighting factor is related to the importance of each factor to the success of placing a good fracture. A horizontal stress anisotropy represents a hydraulic fracture complexity. The hydraulic fracture in a close to isotropic horizontal stress tends to create more complex fracture network than hydraulic fracture stimulation of high horizontal stress anisotropy. A more complex fracture network can result in a higher value for a tight sands subsurface. A higher horizontal stress anisotropy results in a higher value because in term of “easiness” it is easier to create a bi-wing hydraulic fracturing than a complex fracture network. However, for a laminated fracture shale, the weighting coefficient might change in an opposite manner such that the weighting is lower.
The data processing system 200 is configured to compute a value for each of the geomechanics factors based on the data obtained from the well 100 in the subsurface for different locations in the well 100. Generally, higher values for each component represent a more desirable location (depth) for clustering and stage fracturing in the well 100.
The data processing system 200 executes logic of the CGPP engine 204 to generate a value for the CGPP at various depths in the well 100. The CGPP engine 204 generates the CGPP values based on the FP that is calibrated using short-term gas production data from the well 100 or similar wells. For example, each of the geomechanics factors is calibrated based on a correlation found between short term production data and different values of each geomechanics factor in the reservoir.
The calibration can be established from a data set. For example, the calibration is generated from a data set of an area of 20 wells with stages exhibiting high treating pressure, screen outs and/or stage skipped. A geomechanics model was built and calibrated to capture pore pressure, stresses and mechanical parameters. In an example, the stages with difficulties are exhibited in order of importance the following factors: (1) fracture initiation, 2) minimum horizontal stress, 3) vertical stress anisotropy, 4) azimuth of horizontal in comparison to well azimuth. The exact magnitude of the calibrations of the weighting factors are local but the ranking is universal, especially for FI and Sh, which are used to initiate and propagate a fracture.
The data processing system 200 generates, for performing hydraulic fracturing, a relationship of the fraccability predictor that combines mechanical properties and stresses of the subsurface to observed early production values. The data processing system 200 is configured to select clusters depth and stage depth in which the well is expected to produce the highest early production. Specifically, the controller 206 of the data processing system 200 can output one or more control signals for controlling clusters depths and stage depths for hydraulic fracturing. In some implementations, the controller 206 can output a visualization showing the selected locations in the well 100, as subsequently described. In some implementations, the control signals can be transmitted to a remote system through a communication interface 208.
The data processing system 200 is configured to perform a qualitative ranking of depths int eh well 100 to place clustering and stage operations and estimate corresponding expected early stage production based on the selected depths. In some implementations, the data processing system 200 is configured for planning and completion design including depth selection. The data processing system calibrates the FP from wells that have been hydraulically fractured. The result is a prediction based on geomechanics factors in which factors that ensure a good hydraulic fracture stimulation are more heavily weighted in the selection process.
In some implementations, the data processing system 200 is configured to flag the best intervals to perform hydraulic fracturing. The factors for the best intervals can include stress anisotropy, rock mechanics anisotropy and near wellbore anisotropy, as previously described. The best interval with the best geomechanics properties correlated with optimal early production is selected or flagged by the data processing system 200. The data processing system 200 is configured to perform calibration based on previously identified best intervals. The data processing system 200 uses existing data to generate the predictions for a planning phase. The predictions include a best depth to place clusters and stages.
The parameter weightings can be calibrated based on existing successful or failed hydraulic fracture stimulation. The success or failure are represented by data specifying fracture length, early screen out, skipped stages, and so forth. In some implementations, the data processing system 200 can receive data that represents determined formation pressure distribution of the horizontal well after multi-stage fracturing production according to a seepage model. The data processing system can receive data representing a formation pressure field distribution of the horizontal well after multi-stage fracturing production. The data processing system 200 can generate the FP based on the results of the previous fracturing as represented by these models. For example, the weighting factors for horizontal stress anisotropy (FC) and the elastic and strength brittleness index (B19, TSHbrt) can be set at 0.01 and 0.02, respectively. Even though these factors are calibrated as secondary with relatively small weights, these parameters can be of greater importance if the subsurface changes. For example, for heterogeneous rock, parameters may have varying weights that make a difference for intervals with similar stresses or near wellbore stresses. In some implementations, the FP represents an interval for the easiest generation of a hydraulic fracture. For example, there is a good correlation between the maximum gas observed after 3-5 days flowback and the FP. Higher values for the FP correlate to higher production output. More specifically, the highest prediction values of the CGPP correspond to the interval with the greatest output.
The data processing system 200 described herein is configured to perform normalization of the FP based on multiple parameters. These parameters include geomechanics factors, a borehole geometry, and the well trajectory, as previously described. The normalization is estimated for each factor. The fracture initiation parameter is normalized based on the maximum fracking capacity of equipment (e.g., a maximum of about 20000 psi). Sh is normalized based on the maximum stress gradient observed to start generating problems (e.g., a high treating pressure) ˜0.95 psi/ft. A vertical stress anisotropy is normalized based on vertical stress magnitude (e.g., about 1.1 psi/ft). In some implementations, 1000-2000 psi above Sh is used to place a hydraulic fracture. Sh+2000 psi close to the vertical stress is flagged as detrimental for the FP. Stress shadow, ductility, stress anisotropy and elastic strength and brittleness are normalized based on measured mean and maximum values in a laboratory or from well testing.
The data processing system 200 is configured to generate an estimation of mechanical and stress anisotropy. A full geomechanically model is used for generation of the estimation. The data processing system uses the calibration to estimate pressure, stresses, and mechanical parameters for estimation of the anisotropy.
The data processing system 200 performs calibration of the weighting factors based on the hydraulic fracture performance. The calibration is established from a data set of an area of 20 wells with stages exhibiting high treating pressure, screen outs and/or stage skipped. A geomechanics model was built and calibrated to capture pore pressure, stresses and mechanical parameters. Stages with difficulties exhibited in order of importance the following factors: (1) fracture initiation, 2) minimum horizontal stress, 3) vertical stress anisotropy, 4) azimuth of horizontal in comparison to well azimuth, and then other factors are included. The weights are adjusted until the best prediction was obtained from successful and unsuccessful stages, while the ranking is kept static. The data processing system 200 performs a normalization of maximum gas production based on the data from the wells in graph 300.
The data processing system 200 uses the relationship of graph 300 between maximum gas production (normalized per choke) and the FP to determine the CGPP. The data processing system 200 uses the generated CGPP for selection of clusters and stage of the completion of horizontal wells. The CGGP value is used for estimating the volume of gas in million standard cubic feet (MMscf). The CGGP value is normalized per choke. For example, if the CGPP value is 0.7, a production of 0.7*24″˜ 17 MMscf for a choke of 24″ size is expected. The fraccability predictor is normalized between 0-1. The following calibrated equation predicts the volume of gas from the FP: GM_Prod=0.0518*exp (4.3194*FP)*24.
Table 1 summarizes validated geomechanics factors, example weightings, and impacts on the hydraulic fracturing execution.
The process 700 includes receiving (702) well production data from one or more fractured wells in a reservoir. The process 700 includes estimating (704), using data from fracking tests on previous wells, values of geomechanics factors for the one or more fractured wells in the reservoir. The process 700 includes generating (706), based on the well production data and the values of the geomechanics factors, a weighting value associated with each geomechanics factor for the reservoir. The weighting value relates a change in a short term production value of a fractured well to a change in the value of that geomechanics factor at the fractured well. The process 700 includes selecting (708) a horizontal well in the reservoir. The process 700 includes generating (710) fraccability predictor values for respective intervals of the horizontal well, the fraccability predictor values each being based of the weighting value associated with each geomechanics factor for the reservoir. The process 700 includes determining (712), based on the fraccability predictor values of the respective intervals, a cluster spacing in the horizontal well, a stage depth in the horizontal well, or both the cluster spacing and the stage depth in the horizontal well for performing hydraulic fracturing.
Examples of field operations 810 include forming/drilling a wellbore, hydraulic fracturing, producing through the wellbore, injecting fluids (such as water) through the wellbore, to name a few. In some implementations, methods of the present disclosure can trigger or control the field operations 810. For example, the methods of the present disclosure can generate data from hardware/software including sensors and physical data gathering equipment (e.g., seismic sensors, well logging tools, flow meters, and temperature and pressure sensors). The methods of the present disclosure can include transmitting the data from the hardware/software to the field operations 810 and responsively triggering the field operations 810 including, for example, generating plans and signals that provide feedback to and control physical components of the field operations 810. Alternatively, or in addition, the field operations 810 can trigger the methods of the present disclosure. For example, implementing physical components (including, for example, hardware, such as sensors) deployed in the field operations 810 can generate plans and signals that can be provided as input or feedback (or both) to the methods of the present disclosure.
Examples of computational operations 812 include one or more computer systems 820 that include one or more processors and computer-readable media (e.g., non-transitory computer-readable media) operatively coupled to the one or more processors to execute computer operations to perform the methods of the present disclosure. The computational operations 812 can be implemented using one or more databases 818, which store data received from the field operations 810 and/or generated internally within the computational operations 812 (e.g., by implementing the methods of the present disclosure) or both. For example, the one or more computer systems 820 process inputs from the field operations 810 to assess conditions in the physical world, the outputs of which are stored in the databases 818. For example, seismic sensors of the field operations 810 can be used to perform a seismic survey to map subterranean features, such as facies and faults. In performing a seismic survey, seismic sources (e.g., seismic vibrators or explosions) generate seismic waves that propagate in the earth and seismic receivers (e.g., geophones) measure reflections generated as the seismic waves interact with boundaries between layers of a subsurface formation. The source and received signals are provided to the computational operations 812 where they are stored in the databases 818 and analyzed by the one or more computer systems 820.
In some implementations, one or more outputs 822 generated by the one or more computer systems 820 can be provided as feedback/input to the field operations 810 (either as direct input or stored in the databases 818). The field operations 810 can use the feedback/input to control physical components used to perform the field operations 810 in the real world.
For example, the computational operations 812 can process the seismic data to generate three-dimensional (3D) maps of the subsurface formation. The computational operations 812 can use these 3D maps to provide plans for locating and drilling exploratory wells. In some operations, the exploratory wells are drilled using logging-while-drilling (LWD) techniques which incorporate logging tools into the drill string. LWD techniques can enable the computational operations 812 to process new information about the formation and control the drilling to adjust to the observed conditions in real-time.
The one or more computer systems 820 can update the 3D maps of the subsurface formation as information from one exploration well is received and the computational operations 812 can adjust the location of the next exploration well based on the updated 3D maps. Similarly, the data received from production operations can be used by the computational operations 812 to control components of the production operations. For example, production well and pipeline data can be analyzed to predict slugging in pipelines leading to a refinery and the computational operations 812 can control machine operated valves upstream of the refinery to reduce the likelihood of plant disruptions that run the risk of taking the plant offline.
In some implementations of the computational operations 812, customized user interfaces can present intermediate or final results of the above-described processes to a user. 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 feedback, such as changes in parameters or processing inputs, that the user can select to improve a production environment, such as in the exploration, production, and/or testing of petrochemical processes or facilities. For example, the feedback can include parameters that, when selected by the user, can cause a change to, or an improvement in, drilling parameters (including drill bit speed and direction) or overall production of a gas or oil well. The feedback, 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 feedback 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 (or similar terms as understood by one of ordinary skill in the art) means that an action and a response are temporally proximate such that an individual perceives the action and the response occurring substantially simultaneously. For example, the time difference for a response to display (or for an initiation of a display) of data following the individual's action to access the data can be less than 1 millisecond (ms), less than 1 second(s), or less than 8 s. While the requested data need not be displayed (or initiated for display) instantaneously, it is displayed (or initiated for display) without any intentional delay, accounting for processing limitations of a described computing system and time required to, for example, gather, accurately measure, analyze, process, store, or transmit the data.
Events can include readings or measurements captured by downhole equipment such as sensors, pumps, bottom hole assemblies, or other equipment. The readings or measurements can be analyzed at the surface, such as by using applications that can include modeling applications and machine learning. The analysis can be used to generate changes to settings of downhole equipment, such as drilling equipment. 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 in different countries or other jurisdictions.
The computer 902 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 902 is communicably coupled with a network 924. In some implementations, one or more components of the computer 902 can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments.
At a high level, the computer 902 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 902 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 902 can receive requests over network 924 from a client application (for example, executing on another computer 902). The computer 902 can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer 902 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 902 can communicate using a system bus 904. In some implementations, any or all of the components of the computer 902, including hardware or software components, can interface with each other or the interface 906 (or a combination of both), over the system bus 904. Interfaces can use an application programming interface (API) 914, a service layer 916, or a combination of the API 914 and service layer 916. The API 914 can include specifications for routines, data structures, and object classes. The API 914 can be either computer-language independent or dependent. The API 914 can refer to a complete interface, a single function, or a set of APIs.
The service layer 916 can provide software services to the computer 902 and other components (whether illustrated or not) that are communicably coupled to the computer 902. The functionality of the computer 902 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 916, 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 902, in alternative implementations, the API 914 or the service layer 916 can be stand-alone components in relation to other components of the computer 902 and other components communicably coupled to the computer 902. Moreover, any or all parts of the API 914 or the service layer 916 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 902 includes an interface 906. Although illustrated as a single interface 906 in
The computer 902 includes a processor 908. Although illustrated as a single processor 908 in
The computer 902 also includes a database 920 that can hold data (such geomechanics data 922) for the computer 902 and other components connected to the network 924 (whether illustrated or not). For example, database 920 can be in-memory or a database storing data consistent with the present disclosure. In some implementations, database 920 can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to implementations of the computer 902 and the described functionality. Although illustrated as a single database 920 in
The computer 902 also includes a memory 910 that can hold data for the computer 902 or a combination of components connected to the network 924 (whether illustrated or not). Memory 910 can store any data consistent with the present disclosure. In some implementations, memory 910 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to implementations of the computer 902 and the described functionality. Although illustrated as a single memory 910 in
The application 912 can be an algorithmic software engine providing functionality according to implementations of the computer 902 and the described functionality. For example, application 912 can serve as one or more components, modules, or applications. Further, although illustrated as a single application 912, the application 912 can be implemented as multiple applications 918 on the computer 902. In addition, although illustrated as internal to the computer 902, in alternative implementations, the application 912 can be external to the computer 902.
The computer 902 can also include a power supply 918. The power supply 918 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 918 can include power-conversion and management circuits, including recharging, standby, and power management functionalities. In some implementations, the power-supply 918 can include a power plug to allow the computer 902 to be plugged into a wall socket or a power source to, for example, power the computer 902 or recharge a rechargeable battery.
There can be any number of computers 902 associated with, or external to, a computer system including the computer 902, with each computer 902 communicating over network 924. 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 902 and one user can use multiple computers 902.
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. The example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to 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 apparatus, 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, for example LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.
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.
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.
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 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 a claimed 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.
Several 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 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, and the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Furthermore, any claimed implementation is 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 comprising 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.
Several embodiments of these systems and methods have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of this disclosure. Accordingly, other embodiments are within the scope of the following claims.
