Patent Application
20250207487
Hydraulic fracturing may be employed to enhance fluid flow from a reservoir. In various instances, hydraulic fracturing may provide for unlocking fluid reserves from otherwise uneconomical or challenging geological formations. In deciding whether or not to employ hydraulic fracturing, various factors may be taken into consideration. For example, efficiency and efficacy of hydraulic fracturing operations can weigh in favor of employing hydraulic fracturing. In various instances, efficiency and/or efficacy may be constrained, for example, by geological complexities, operational constraints, and/or economic considerations. To assess such constraints with a view of optimizing hydraulic fracturing, one or more numerical or analytical techniques may be employed, which tend to be computationally intensive, time-consuming, and often, unable to capture the realm of multi-dimensional complexities of real-world reservoir conditions. Moreover, such techniques might not effectively handle the vast amount of data generated during oil and gas operations; thereby, missing out on valuable insights that could be harnessed for improved decision-making and optimization, along with actual field implementation.
Various technologies, techniques, etc., described herein pertain to hydraulic fracturing operations, and optimization thereof, using data-driven modeling. Such an approach can improve hydraulic fracturing operations and, for example, production of fluid from a reservoir.
A method can include receiving data for a well in a field and parameter values for hydraulic fracturing of the well in the field; predicting production data responsive to the hydraulic fracturing of the well using at least a portion of the data and at least a portion of the parameter values as input to a machine learning model, where the machine learning model is trained using historical data for the field; and outputting the predicted production data.
A system can include a processor; a memory operatively coupled to the processor; and processor-executable instructions stored in the memory and executable to instruct the system to: receive data for a well in a field and parameter values for hydraulic fracturing of the well in the field; predict production data responsive to the hydraulic fracturing of the well using at least a portion of the data and at least a portion of the parameter values as input to a machine learning model, where the machine learning model is trained using historical data for the field; and output the predicted production data.
A computer-readable storage medium can include processor-executable instructions executable by a system to instruct the system to: receive data for a well in a field and parameter values for hydraulic fracturing of the well in the field; predict production data responsive to the hydraulic fracturing of the well using at least a portion of the data and at least a portion of the parameter values as input to a machine learning model, where the machine learning model is trained using historical data for the field; and output the predicted production data.
Various other apparatuses, systems, methods, etc., are also disclosed. This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.
Features and advantages of the described implementations can be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.
The following description includes the best mode presently contemplated for practicing the described implementations. This description is not to be taken in a limiting sense, but rather is made merely for the purpose of describing the general principles of the implementations. The scope of the described implementations should be ascertained with reference to the issued claims.
Hydraulic fracturing operations to enhance fluid production from a reservoir can depend on various factors, which may include constraints associated with geological complexities, operations, resources expended, resources produced, etc. As explained, numerical or analytical techniques may be computationally intensive, time-consuming, and often, unable to capture the realm of multi-dimensional complexities of real-world reservoir conditions. For example, a workflow to optimize hydraulic fracturing operations may involve generating a numerical model of a reservoir where fluid dynamics and geomechanics are simulated using the numerical model to generate simulation results. In such an example, a single simulation run may take a substantial amount of time to execute and may provide simulation results for a single set of operational parameters. To optimize the operational parameters, multiple simulation runs may be required, which may take a number of days to complete. Hence, resources (e.g., time, people, computers, etc.) expended to perform a numerical model-based simulation approach may be factors that weigh against effective decision making and/or optimization of hydraulic fracturing operations.
As an example, a data-driven approach can be implemented to improve decision making, optimization, implementation, etc., of hydraulic fracturing operations. As an example, one or more machine learning (ML) techniques may be utilized where learning can be based at least in part on data from field operations. For example, a framework may leverage one or more ML techniques to ingest and process relatively large datasets to uncover one or more of various intricate patterns and/or to generate one or more types of predictive insights. In such an example, one or more of patterns and predictions may improve the understanding and management of hydraulic fracturing operations. For example, hydraulic fracturing equipment may be controlled based in part on one or more of uncovered patterns and predicted insights. Such control may relate to hydraulic fracturing fluid, proppant, additives, perforations, micro-seismology, intra-stage operations, inter-stage operations, environmental concerns, etc
As an example, a data-driven approach may improve optimization of one or more operational parameters. For example, consider a workflow that aims to optimize hydraulic fracturing parameters that may include, for example, one or more of fluid volumes, proppant volumes, pad volume, injection rates, maximum proppant concentration, and stage spacing; noting that one or more other hydraulic fracturing parameters may be included, additionally or alternatively. In such an example, an ML-driven approach may boost production rates from a reservoir and may also help to align field operations with one or more objectives. As an example, a framework may provide for data-driven decision making and optimization in hydraulic fracturing operations to improve operational efficiency, to reduce resource expenditures, and to enhance viability within an evolving energy landscape.
As an example, a framework may implement one or more ML models for characterizing, optimizing, controlling, etc., one or more hydraulic fracturing operations. As an example, an ML model-based approach can harness a plethora of data to streamline fracturing and/or re-fracturing candidate selection processes, predictions of hydraulic fracturing operations, and optimization of field operations. As an example, a framework may ingest data that can include, for example, one or more of well attribute data, well log data, production data, fracture design data, current state reservoir condition, and completion parameter data; noting that one or more other types of data may be ingested, additionally or alternatively. As an example, such a framework may employ one or more ML techniques for forecasting relatively short-term production rates of oil, gas, and/or water for one or more individual hydraulic fracturing stages (e.g., over a 1 year to 2 years period). Such predictive capabilities of a framework may improve decision making and improve an ability to tailor fracturing designs to maximize production efficiency.
As an example, a framework may include and/or be operatively coupled to one or more optimizers. For example, consider a framework that includes an optimizer that can provide for directing use of one or more ML models to generate an optimized solution and/or a framework that may link to an optimizer (e.g., via one or more application programming interfaces (APIs)) such that one or more ML models can be driven interactively to generate an optimized solution.
As an example, optimizer and framework integration (e.g., internal, external, etc.) may provide for adjusting one or more hydraulic fracturing parameters and/or one or more completion parameters to maximize or minimize a defined objective function. As an example, an objective function may be defined to be a single variable objective function or a multi-variable objective function. As an example, an objective function may encapsulate one or more of various goals, which may include, for example, maximizing production rates and/or one or more economic metrics (e.g., net-present value, water expended, energy expended, emissions generated, etc.). As an example, an optimization may be facilitated through one or more techniques that may include one or more techniques that can iteratively sample and/or otherwise generate different combinations of fracturing parameters and completion parameters, estimate an objective function (e.g., or objective functions), and hone in on an optimal set of parameters (e.g., an optimal set of values for one or more parameters, etc.). Such an iterative approach to optimization, grounded in real-world data, may supplant a manually-driven approach to optimization while offering a more nuanced and effective pathway to achieving desirable operational outcomes.
As an example, a framework may provide for implementation of a holistic workflow that may span from identifying hydraulic fracturing candidates to optimizing fracturing and completion parameters or beyond. As an example, a framework may provide for extensibility and adaptability to handle workflows for conventional reservoirs and unconventional reservoirs. As an example, a framework may provide for handling multi-stage fracturing scenarios, which may be at the forefront of modern reservoir management. As explained, a framework may operate without dependency on tedious numerical or analytical calculations. As an example, a framework may provide for real-time and/or near real-time adaptability based on incoming data. In such an example, the framework may generate output that can reduce operational bottlenecks, which may pave the way for more efficient, economically viable, and data-driven hydraulic fracturing operations.
As mentioned, model-based numerical simulation techniques may be computationally intensive and take a substantial amount of time to generate simulation results. For application of a numerical technique, equations may be discretized using a grid that includes nodes, cells, etc. To represent features in a geologic environment, a structural model may assist with properly locating nodes, cells, etc., of a grid for use in simulation using one or more numerical techniques.
As to numerical techniques, a numerical technique such as the finite difference method can include discretizing a 1D differential heat equation for temperature with respect to a spatial coordinate to approximate temperature derivatives (e.g., first order, second order, etc.). Where time is of interest, a derivative of temperature with respect to time may also be provided. As to the spatial coordinate, the numerical technique may rely on a spatial grid that includes various nodes where a temperature will be provided for each node upon solving the heat equation (e.g., subject to boundary conditions, generation terms, etc.). Such an example may apply to multiple dimensions in space (e.g., where discretization is applied to the multiple dimensions). Thus, a grid may discretize a volume of interest (VOI) into elementary elements (e.g., cells or grid blocks) that may be assigned or associated with properties (e.g., porosity, rock type, etc.), which may be germane to simulation of physical processes (e.g., fluid flow, reservoir compaction, etc.). While a differential heat equation is mentioned, additionally or alternatively, one or more other types of differential equations may be considered (e.g., consider differential equations for fluid flow, stress, strain, etc.).
As another example of a numerical technique, consider the finite element method where space may be represented by one dimensional or multi-dimensional elements. For one spatial dimension, an element may be represented by two nodes positioned along a spatial coordinate. For multiple spatial dimensions, an element may include any number of nodes. Further, some equations may be represented by certain nodes while others are represented by fewer nodes (e.g., consider an example for the Navier-Stokes equations where fewer nodes represent pressure and more nodes represent velocity). The finite element method may include providing nodes that can define triangular elements (e.g., tetrahedra in 3D, higher order simplexes in multidimensional spaces, etc.) or quadrilateral elements (e.g., hexahedra or pyramids in 3D, etc.), or polygonal elements (e.g., prisms in 3D, etc.). Such elements, as defined by corresponding nodes of a grid, may be referred to as grid cells.
Yet another example of a numerical technique is the finite volume method. For the finite volume method, values for model equation variables may be calculated at discrete places on a grid, for example, a node of the grid that includes a finite volume surrounding it. The finite volume method may apply the divergence theorem for evaluation of fluxes at surfaces of each finite volume such that flux entering a given finite volume equals that leaving to one or more adjacent finite volumes (e.g., to adhere to conservation laws). For the finite volume method, nodes of a grid may define grid cells.
As explained, a framework may implement one or more ML techniques where training (e.g., learning) depends on data, which can include actual field data. In various instances, data may be historic data and/or real-time data. For example, in a design phase of hydraulic fracturing operations, one or more ML models may be implemented that are trained on historic data, which may be acquired from field operations at one or more wells offset to a target well. As an example, in a control phase of hydraulic fracturing operations, real-time data may be utilized for one or more purposes, which may include additional training, input, etc. As an example, one or more ML models may be executable to generate output in an amount of time that is substantially less than that of a numerical model-based simulation. As explained, a reduction in time to generate output may be leveraged for optimization, particularly for iterative optimization.
Below, various types of environments, frameworks, workflows, data acquisition techniques, field equipment, field operations, etc., are described, which may involve use of a framework or frameworks, optionally during one or more field operations (e.g., hydraulic fracturing, etc.).
In the example of
In the example of
The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows, enabling improved quality drilling programs (e.g., digital drilling plans, etc.) to be produced quickly with assured coherency.
The DRILLOPS framework, which may be included in the system 100 of
The PETREL framework can be part of the DELFI cognitive exploration and production (E&P) environment (SLB, Houston, Texas, referred to as the DELFI environment) for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir.
The TECHLOG framework can handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale, etc.). The TECHLOG framework can structure wellbore data for analyses, planning, etc. As an example, the TECHLOG framework may be coupled to one or more ML models for purposes of generation of output, training, etc.
The PETROMOD framework provides petroleum systems modeling capabilities that can combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework can predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions.
The ECLIPSE framework provides a reservoir simulator with numerical solvers for prediction of dynamic behavior for various types of reservoirs and development schemes.
The INTERSECT framework provides a high-resolution reservoir simulator for simulation of geological features and quantification of uncertainties, for example, by creating production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework can produce results, which may be continuously updated by real-time data exchanges (e.g., from one or more types of data acquisition equipment in the field that can acquire data during one or more types of field operations, etc.). The INTERSECT framework can provide completion configurations for complex wells where such configurations can be built in the field, can provide detailed chemical-enhanced-oil-recovery (EOR) formulations where such formulations can be implemented in the field, can analyze application of steam injection and other thermal EOR techniques for implementation in the field, advanced production controls in terms of reservoir coupling and flexible field management, and flexibility to script customized solutions for improved modeling and field management control. The INTERSECT framework, as with the other example frameworks, may be utilized as part of the DELFI environment, for example, for rapid simulation of multiple concurrent cases.
The KINETIX framework provides for reservoir-centric stimulation-to-production analyses that can integrate geology, petrophysics, completion engineering, reservoir engineering, and geomechanics, for example, to provide for optimized completion and fracturing designs for a well, a pad, or a field. The KINETIX framework can be operatively coupled to and/or integrated with features of the PETREL framework (e.g., within the DELFI environment). As to the VISAGE framework it can be part of or otherwise operatively coupled to the KINETIX framework.
The VISAGE framework includes finite element numerical solvers that may provide simulation results such as, for example, results as to compaction and subsidence of a geologic environment, well and completion integrity in a geologic environment, cap-rock and fault-seal integrity in a geologic environment, fracture behavior in a geologic environment, thermal recovery in a geologic environment, CO2 disposal, etc.
As an example, the KINETIX framework can provide for analyses from 1D logs and simple geometric completions to 3D mechanical and petrophysical models coupled with the INTERSECT framework high-resolution reservoir simulator and VISAGE framework finite-element geomechanics simulator. The KINETIX framework can provide automated parallel processing using cloud platform resources and can provide for rapid assessment of well spacing, completion, and treatment design choices, enabling exploration of many scenarios in a relatively rapid manner (e.g., via provisioning of cloud platform resources). The KINETIX framework may be operatively coupled to the MANGROVE simulator (SLB, Houston, Texas), which can provide for optimization of stimulation design (e.g., stimulation treatment operations such as hydraulic fracturing) in a reservoir-centric environment.
The MANGROVE framework can combine scientific and experimental work to predict geomechanical propagation of hydraulic fractures, reactivation of natural fractures, etc., along with production forecasts within 3D reservoir models (e.g., production from a drainage area of a reservoir where fluid moves via one or more types of fractures to a well and/or from a well). The MANGROVE framework can provide results pertaining to heterogeneous interactions between hydraulic and natural fracture networks, which may assist with optimization of the number and location of fracture treatment stages (e.g., stimulation treatment(s)), for example, to increased perforation efficiency and recovery.
The PIPESIM simulator includes solvers that may provide simulation results such as, for example, multiphase flow results (e.g., from a reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc. The PIPESIM simulator may be integrated, for example, with the AVOCET production operations framework (SLB, Houston Texas). The PIPESIM simulator may be an optimizer that can optimize one or more operational scenarios at least in part via simulation of physical phenomena.
The aforementioned DELFI environment provides various features for workflows as to subsurface analysis, planning, construction and production, for example, as illustrated in the workspace framework 110. As shown in
In the example of
Visualization features may provide for visualization of various earth models, properties, etc., in one or more dimensions. As an example, visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations.
A workflow may utilize one or more frameworks to generate information that can be utilized to control one or more types of field equipment (e.g., drilling equipment, wireline equipment, fracturing equipment, etc.).
As shown, the system 200 can include a geological/geophysical data block 210, a surface models block 220 (e.g., for one or more structural models), a volume modules block 230, an applications block 240, a numerical processing block 250 and an operational decision block 260. As shown in the example of
As shown in the example of
As to the applications block 240, it may include applications such as a well prognosis application 242, a reserve calculation application 244 and a well stability assessment application 246. As to the numerical processing block 250, it may include a process for seismic velocity modeling 251 followed by seismic processing 252, a process for facies and petrophysical property interpolation 253 followed by flow simulation 254, and a process for geomechanical simulation 255 followed by geochemical simulation 256. As indicated, as an example, a workflow may proceed from the volume models block 230 to the numerical processing block 250 and then to the applications block 240 and/or to the operational decision block 260. As another example, a workflow may proceed from the surface models block 220 to the applications block 240 and then to the operational decisions block 260 (e.g., consider an application that operates using a structural model).
In the example of
Referring again to the data block 210, the well tops or drill hole data 212 may include spatial localization, and optionally surface dip, of an interface between two geological formations or of a subsurface discontinuity such as a geological fault; the seismic interpretation data 214 may include a set of points, lines or surface patches interpreted from seismic reflection data, and representing interfaces between media (e.g., geological formations in which seismic wave velocity differs) or subsurface discontinuities; the outcrop interpretation data 216 may include a set of lines or points, optionally associated with measured dip, representing boundaries between geological formations or geological faults, as interpreted on the earth surface; and the geological knowledge data 218 may include, for example knowledge of the paleo-tectonic and sedimentary evolution of a region.
As to a structural model, it may be, for example, a set of gridded or meshed surfaces representing one or more interfaces between geological formations (e.g., horizon surfaces) or mechanical discontinuities (fault surfaces) in the subsurface. As an example, a structural model may include some information about one or more topological relationships between surfaces (e.g., fault A truncates fault B, fault B intersects fault C, etc.).
As to the one or more boundary representations 232, they may include a numerical representation in which a subsurface model is partitioned into various closed units representing geological layers and fault blocks where an individual unit may be defined by its boundary and, optionally, by a set of internal boundaries such as fault surfaces.
As to the one or more structured grids 234, it may include a grid that partitions a volume of interest into different elementary volumes (cells), for example, that may be indexed according to a pre-defined, repeating pattern. As to the one or more unstructured meshes 236, it may include a mesh that partitions a volume of interest into different elementary volumes, for example, that may not be readily indexed following a pre-defined, repeating pattern (e.g., consider a Cartesian cube with indexes I, J, and K, along x, y, and z axes).
As to the seismic velocity modeling 251, it may include calculation of velocity of propagation of seismic waves (e.g., where seismic velocity depends on type of seismic wave and on direction of propagation of the wave). As to the seismic processing 252, it may include a set of processes allowing identification of localization of seismic reflectors in space, physical characteristics of the rocks in between these reflectors, etc.
As to the facies and petrophysical property interpolation 253, it may include an assessment of type of rocks and of their petrophysical properties (e.g., porosity, permeability), for example, optionally in areas not sampled by well logs or coring. As an example, such an interpolation may be constrained by interpretations from log and core data, and by prior geological knowledge.
As to the flow simulation 254, as an example, it may include simulation of flow of hydro-carbons in the subsurface, for example, through geological times (e.g., in the context of petroleum systems modeling, when trying to predict the presence and quality of oil in an un-drilled formation) or during the exploitation of a hydrocarbon reservoir (e.g., when some fluids are pumped from or into the reservoir).
As to geomechanical simulation 255, it may include simulation of the deformation of rocks under boundary conditions. Such a simulation may be used, for example, to assess compaction of a reservoir (e.g., associated with its depletion, when hydrocarbons are pumped from the porous and deformable rock that composes the reservoir). As an example, a geomechanical simulation may be used for a variety of purposes such as, for example, prediction of fracturing, reconstruction of the paleo-geometries of the reservoir as they were prior to tectonic deformations, etc.
As to geochemical simulation 256, such a simulation may simulate evolution of hydrocarbon formation and composition through geological history (e.g., to assess the likelihood of oil accumulation in a particular subterranean formation while exploring new prospects).
As to the various applications of the applications block 240, the well prognosis application 242 may include predicting type and characteristics of geological formations that may be encountered by a drill bit, and location where such rocks may be encountered (e.g., before a well is drilled); the reserve calculations application 244 may include assessing total amount of hydrocarbons or ore material present in a subsurface environment (e.g., and estimates of which proportion can be recovered, given a set of economic and technical constraints); and the well stability assessment application 246 may include estimating risk that a well, already drilled or to-be-drilled, will collapse or be damaged due underground stress.
As to the operational decision block 260, the seismic survey design process 261 may include deciding where to place seismic sources and receivers to optimize the coverage and quality of the collected seismic information while minimizing cost of acquisition; the well rate adjustment process 262 may include controlling injection and production well schedules and rates (e.g., to maximize recovery and production); the well trajectory planning process 263 may include designing a well trajectory to maximize potential recovery and production while minimizing drilling risks and costs; the well trajectory planning process 264 may include selecting proper well tubing, casing and completion (e.g., to meet expected production or injection targets in specified reservoir formations); and the prospect process 265 may include decision making, in an exploration context, to continue exploring, start producing or abandon prospects (e.g., based on an integrated assessment of technical and financial risks against expected benefits).
The system 200 can include and/or can be operatively coupled to a system such as the system 100 of
As an example, the system 200 may provide for monitoring data, which can include geo data per the geo data block 210. In various examples, geo data may be acquired during one or more operations. For example, consider acquiring geo data during drilling operations via downhole equipment and/or surface equipment. As an example, the operational decision block 260 can include capabilities for monitoring, analyzing, etc., such data for purposes of making one or more operational decisions, which may include controlling equipment, revising operations, revising a plan, etc. In such an example, data may be fed into the system 200 at one or more points where the quality of the data may be of particular interest. For example, data quality may be characterized by one or more metrics where data quality may provide indications as to trust, probabilities, etc., which may be germane to operational decision making and/or other decision making. As an example, the system 200 of
As explained with respect to
As to the example of the hydraulic fracturing system 340 of
As illustrated with respect to the block 402, the bore 430 may be at least partially cased with casing 440 into which a string or line 450 may be introduced that carries a perforator 460. As shown, the perforator 460 can include a distal end 462 and charge positions 465 associated with activatable charges that can perforate the casing 440 and form channels 415-1 in the layer 414. Next, per the block 403, fluid may be introduced into the bore 430 between the heel 434 and the toe 436 where the fluid passes through the perforations in the casing 440 and into the channels 415-1. Where such fluid is under pressure, the pressure may be sufficient to fracture the layer 414, for example, to form fractures 417-1. In the block 403, the fractures 417-1 may be first stage fractures, for example, of a multistage fracturing operation.
Per the block 404, additional operations are performed for further fracturing of the layer 414. For example, a plug 470 may be introduced into the bore 430 between the heel 434 and the toe 436 and positioned, for example, in a region between first stage perforations of the casing 440 and the heel 434. Per the block 405, the perforator 460 may be activated to form additional perforations in the casing 440 (e.g., second stage perforations) as well as channels 415-2 in the layer 414 (e.g., second stage channels). Per the block 406, fluid may be introduced while the plug 470 is disposed in the bore 430, for example, to isolate a portion of the bore 430 such that fluid pressure may build to a level sufficient to form fractures 417-2 in the layer 414 (e.g., second stage fractures).
In a method such as the method 400 of
As an example, a component may be degradable upon contact with a fluid such as an aqueous ionic fluid (e.g., saline fluid, etc.). As an example, a component may be degradable upon contact with well fluid that includes water (e.g., consider well fluid that includes oil and water, etc.). As an example, a component may be degradable upon contact with a fracturing fluid (e.g., a hydraulic fracturing fluid). As an example, a degradation time may depend on a component dimension or dimensions and can differ for various temperatures where a component is in contact with a fluid that is at least in part aqueous (e.g., include water as a medium, a solvent, a phase, etc.).
As explained with respect to
As an example, a framework may provide for end-to-end ML-based outputs via one or more workflows that may include identifying hydraulic fracturing candidates and/or re-fracturing candidates, predicting an initial short-term well production (e.g., oil, gas and/or water production rate and/or cumulative value(s)) for a given reservoir and hydraulic fracturing design, optimizing hydraulic fracturing and completion parameters in a conventional and/or an unconventional oil and gas reservoir, etc. Such a framework may include one or more training phases (e.g., learning phases) that ingest data that may be related to and/or include one or more of well attribute data, well log data, production data, fracture design data, hydraulic fracturing parameters, and completion parameters. As an example, an ML model may be trained to forecast short-term oil, gas and/or water production rates and/or cumulative values for one or more hydraulic fracturing stages over a period of time (e.g., consider a 1 year to 2 years period). As explained, a framework may be integrated with one or more optimizers such that, for example, a workflow can optimize various parameters, which may include, for example, hydraulic fracture placement parameters (e.g., hydraulic fracture and completion design parameters) towards achieving a maximum or a minimum objective function, for example, as to initial oil, gas and/or water production rates, one or more cumulative values, one or more economic, resource-based parameters (e.g., net-present value, etc.). For example, such a framework may provide output for a given well that may include and/or is to include multiple stages in a reservoir characterized by reservoir properties. As explained, a framework may provide for implementation of a robust prediction and optimization workflow that can streamline enhancement of hydraulic fracturing operations, which may be in contrast to relatively time-consuming, tedious numerical and analytical techniques that may involve relatively high uncertainties that may lead to unreliable results.
As explained, the framework 600 of
As an example, the input 610 may include one or more types of data, as may depend on availability. For example, consider data such as one or more of well data (e.g., well location, well trajectory, etc.), well log data (e.g., pore pressure, porosity, permeability, fluid saturation, net pay, Young's modulus, Poisson's ratio, minimum horizontal stress, production logs, etc.), production data (e.g., monthly and/or daily gas, oil and water production rates and/or cumulative production, production days, tubing head pressure, choke size, static pressure, downhole flowing pressure, etc.), etc. As an example, the parameters 620, which may include input parameters and/or computed parameters, may include fracture design parameters (e.g., fluid volume, fluid type, proppant volume, proppant type, proppant and/or mesh size, proppant concentration, average fluid rate, etc.), computed fracture design parameters (e.g., from the ML model 630) (e.g., fracture length, fracture height, fracture conductivity, etc.), and fracture completion parameters (e.g., perforation intervals, number of stages, stage duration, stage spacing, perforations per stage, perforation density, perforation hole size, etc.).
As an example, a framework may include a data pre-processing pipeline. For example, consider a data pre-processing pipeline that can include features for performing automated extraction and processing of various properties from different source of data (e.g., LAS, LIS, DLIS, xlsx, txt, pdf, etc.) that may be suitable for use in one or more ML training processes. For example, consider features for extraction and processing of location and trajectory data for each well; extraction and processing of statistical data (e.g., minimum, maximum, standard deviation, mean, median etc.) of well logs for each stage (e.g., perforation intervals) of each well; extraction and processing of historical and/or observed and calculated fracture design parameters for each stage of each well; extraction and processing of observed and historical completion design parameters for each stage of each well; extraction and processing of observed and/or historical choke size observation for each historical well; extraction and processing historical and/or observed production logs of each historical well; and allocation of production data (e.g., oil, gas and/or water production rates and/or cumulative values) per stage (e.g., perforation intervals).
As an example, a framework may provide for implementing a training, testing, validation, and inference process for one or more ML models. For example, pre-processed data from data pre-processing may be utilized in a supervised ML training process. As an example, such a training process may involve receiving model input, for example, as pre-processed well data, well logs, and fracture design parameters (e.g., including calculated fracture parameters from another ML model), and completion parameters from historical wells and/or stages; generating model output, for example, as short-term production profiles (e.g., oil, gas and/or water production rates and/or cumulative values) for a period of time (e.g., up to several years where, for example, if 1 year is utilized, output may include monthly points) for each stage of each historical well; training, testing, and validation of model output, for example, according to one or more of best practices of training, testing, and validation for one or more ML models; and inference where, upon provided well data, well logs, fracture design parameters, and completion parameters from a stage (e.g., any stage) of a well (e.g., any well), an ML model can predict one or more production profiles (e.g., oil, gas and/or water production rates and/or cumulative rates) for up to several years for each stage of each well. As an example, a workflow may include summing the contribution from each stage to account for overall well production.
As an example, the ML model 630 may provide for prediction of one or more fracture design parameters, which may be referred to as calculated fracture design parameters. As an example, the ML model 630 may provide for prediction of fracture design parameters such as, for example, one or more of fracture length, fracture height, and fracture conductivity. As an example, fracture conductivity may be defined as the product of fracture permeability and fracture width for a finite-conductivity fracture.
In the example of
As an example, the framework 700 may be implemented for execution of an optimization workflow to optimize hydraulic fracture placement (e.g., hydraulic fracture design parameters and completion design parameters) and initial production profiles (e.g., up to several years) for a given well data with multiple stages and reservoir properties.
As an example, an optimization process may involve some amount of post-training, once a reliable ML model is generated, where output can be used in an optimization loop for fracture design parameters and completion design parameters. For example, consider a workflow that includes defining an objective function (e.g., production rate(s) of oil, gas, and/or water, cumulative production of oil, gas, and/or water, one or more economic parameters, etc.); sampling through various fracture design parameter and completion design parameter ranges through one or more optimization techniques (e.g., differential evolution, particle swarm optimization, etc.) that can minimize or maximize a defined objective function; and outputting optimal fracture design and completion design parameters corresponding to the minimized or maximized defined objective function.
As shown in
In the example of
As an example, the framework 800 may provide for implementation of an automated end-to-end workflow for identifying one or more hydraulic fracturing and/or re-fracturing candidates where one or more optimized fracture design parameters (e.g., as one or more values) and/or one or more optimized completion design parameters (e.g., as one or more values) can be provided for each of the one or more identified candidates.
As an example, the framework 800 may be utilized for one or more scenarios. For example, consider a scenario involving selection and optimization of hydraulic fracturing and completion parameters of one or more existing wells; a scenario involving selection and optimization of hydraulic re-fracturing and completion parameters of one or more hydraulically fracted existing wells; and a scenario involving selection and optimization of hydraulic fracturing and completion parameters of one or more new wells.
As to application of the framework 800 for a scenario that involves hydraulic fracturing candidate selection and optimization for existing wells, consider the components in a region outlined by a dashed line of
As explained, the framework 800 may utilize various data and/or metrics to automatically determine whether a well is among a group of low productivity wells, which may be candidates for stimulation such as, for example, hydraulic fracturing as a stimulation technique. As an example, the framework 800 may generate a list of wells as selected candidates for hydraulic fracturing stimulation. As an example, the framework 800 may be implemented to generate optimized output for each candidate, as each candidate is selected and/or as taken from a list of candidates, which may, for example, be a ranked list (e.g., ranked according to productivity, types of recommended stimulation techniques, etc.).
As to hydraulic fracturing candidate optimization, the framework 800 may operate using a list of wells selected for hydraulic fracturing by a hydraulic fracturing candidate selection workflow. In such an example, sets of corresponding well related data, such as, for example, well data, well log data, and initial fracture design parameters and initial completion design parameters by be accessed for purposes of candidate optimization. For example, consider a workflow that uses input including input from a candidate selection process as input to an ML model that has been trained using historical data for a field or a reservoir of interest. In such an example, the workflow may commence with a base set of fracture design parameters and completion design parameters 818 and execute an optimization process based on a defined objective function, which may be provided via input received via a graphical user interface, via an execution file, via a default setting, etc. As an example, the framework 800 may include an objective function builder and/or selector component that can allow for building of and/or selection of a suitable objective function, which may be an objective function to be minimized or to be maximized. As an example, the framework 800 may operate to generate the optimized output 888. For example, for a ranked list of wells, the optimized output 888 can include associated objective function values and optimal fracture design parameter and completion design parameters for each stage of each well of the ranked list of wells.
In the example of
As to a hydraulic re-fracturing optimization part of the workflow, it can include receiving at least a portion of the input 910, at least a portion of the historical parameters 915, and at least a portion of the intermediate output 930. For example, consider the ML model 950 receiving sets of corresponding well related data, such as, for example, well data, well log data, and historical fracture design parameters and completion design parameters. As shown in the example of
As an example, the framework 1000 may be implemented for a new well workflow scenario where a new well has recently been drilled within a reservoir, with a number of well logs having been acquired. In such an example, an asset team may propose an initial fracture and completion design where, for example, one or more parameters may not be optimal and thereby subjected to optimization using the framework 1000.
As an example, a workflow for hydraulic fracture and completion parameters optimization for a new well may include receiving input data for the new well, which may include well data and well log data together with an initial set of fracture design parameters and completion design parameters. In such an example, the workflow can pass the input data to a trained ML model, which may be trained using historical data for a field or a reservoir of interest (e.g., as may be associated with the new well). As an example, such a workflow can commence with the initial set of fracture design parameters and completion design parameters and perform an optimization process that uses a defined objective function. As to output, the workflow may output an objective function value and optimal fracture design parameters and completion design parameters for each stage of the new well.
As an example, a framework may provide for accurate and rapid forecasting. For example, consider reliable post-fracture stimulation short-term production forecasts for oil, gas, and/or water that can be utilized for decision-making, operational planning, field control, etc. Such a framework may provide for improved accuracy, particularly for heterogeneous reservoir conditions.
As an example, a framework may provide for optimization of fracturing and completion parameters. For example, a framework may provide for achieving optimal fracturing and completion parameters for maximizing reservoir performance. Such a framework may provide for improved design, particularly where complex interactions and high uncertainties may exist.
As an example, a framework may provide for adaptability to reservoir types. For example, consider a framework that may be adapted conventional reservoirs and to unconventional reservoirs, which may thereby provide a comprehensive approach to hydraulic fracturing optimization.
As an example, a framework may provide for reduced dependency on tedious analytical techniques, for example, by implementation of one or more ML models in a data-driven manner that helps to minimize reliance on time-consuming and often unreliable numerical and analytical methods. Such a framework may provide for improving operational efficiency and reducing costs.
As an example, a framework may provide for data-driven decision-making in a manner that can transition tasks from numerical techniques and/or analytical techniques to data-driven techniques, which can help to reduce uncertainties and improve operational efficiency.
As an example, a framework may provide for efficient processing of relatively large datasets. For example, a framework may leverage one or more machine learning techniques, which may be available via one or more types of libraries, where training may be performed using cloud-based resources to generate one or more efficient ML models that can be executed in real-time or near real-time for purposes of generating relevant output, which may be utilized in an optimization loop (e.g., iterative optimization, etc.). An ability to efficiently process and analyze large datasets can result in an ability to derive actionable insights for modern reservoir management.
As explained, a framework may implement one or more ML models. Such an approach may provide for rapid and more accurate short-term production forecasts by learning from historical data and identifying complex patterns that other techniques might overlook (e.g., fail to capture or otherwise take into account). As explained, a framework may provide for optimization, which may be via one or more optimization techniques that can optimize hydraulic fracturing design parameters and completion design parameters via learning from a sufficient amount of historical data, enabling a more efficient and effective design process. As an example, design parameters may be or include operational parameters that may be implemented during field operations. For example, a framework may operate in real-time or near real-time to generate output that can be utilized during field operations, such as, for example, during execution of a fracturing stage, gearing up for a subsequent fracturing stage, etc.
As explained, a framework may be adaptability for use with different types of reservoirs (e.g., conventional and unconventional reservoirs) and provide for optimization of hydraulic fracturing across a variety of reservoir conditions.
As explained, a framework may provide for leveraging data analytics in a manner that facilitates data-driven decision-making, design, control, etc., which can help to reduce uncertainties and improve the accuracy of predictions and optimizations.
As explained, a framework may be tailored to a particular scenario or may be operable for multiple types of scenarios. As an example, a particular scenario may be for existing wells or may be for new wells or, for example, may be for a combination of existing wells and new wells. As an example, a particular scenario may be for a well that has been at least partially drilled and/or fractured where, for example, output of a framework may facilitate performance of one or more field operations (e.g., using optimized parameter values, etc.).
As an example, a framework may provide for extended forecasting. For example, consider extending one or more ML models to provide longer-term production forecasts, which may provide additional value, aiding in long-term planning, control, resource-based decisions, etc.
As explained, a framework may be operable using real-time data, which may be provided by a data acquisition framework (e.g., the TECHLOG framework). As an example, a framework may provide for merging an ML model with one or more real-time monitoring systems to enable dynamic optimization of hydraulic fracturing operations, which may be responsive to evolving reservoir conditions.
As an example, a framework may be operable as part of an automated control system. For example, a framework may be integrated with an automated control system (e.g., via an API, etc.) to enable real-time adjustments to fracturing parameters and/or completion parameters, which may help to enhance operational efficiency and responsiveness. As an example, consider the controller 360 of
As an example, a framework may provide for multi-objective optimization. For example, consider an optimization framework that is extensible to consider multiple objectives simultaneously (e.g., maximizing production while minimizing environmental impact), which may cater to a broader set of operational and sustainability goals. In such an example, one or more energy and/or emissions frameworks may be utilized that can, for example, determine energy efficiency, emissions based on energy source and consumption, etc.
As an example, a framework may be applicable for one or more types of workflows involving one or more field operations that involve fracturing. For example, consider subsurface operations that may involve one or more of water flooding, CO2 sequestration, and geothermal energy production. In such examples, one or more ML models may be trained using appropriate data to thereby provide for selection of sites, selection of wells, design of operations that may involve fracturing, design of completions, etc.
As explained, a framework may provide for rapid and enhanced forecasting accuracy by leveraging one or more ML models. Such a framework may provide more accurate and rapid short-term production forecasts by capturing complex relationships within the data that numerical or analytical techniques might miss.
As explained, a framework may result in reduced dependency on tedious calculations, which may be time-consuming and often error-prone numerical or analytical calculations. Such a framework can speed up an optimization process and reduce operational costs.
As explained, a framework may provide for implementation of an end-to-end workflow that may range from candidate identification to optimization in a holistic approach that streamlines operations and ensures cohesive alignment towards optimization goals, which can be in contrast to various fragmented approaches.
As explained, a framework can provide for improved accuracy and predictive insights. As an example, a framework may provide an ML-based solution that offers rapid and accurate forecasting and that provides predictive insights that can aide in proactive decision-making at one or more phases (e.g., site selection, design, control, etc.). As an example, a framework may be integrated with one or more other frameworks or within a framework environment. For example, consider integration with the PETREL framework and/or integration within the DELFI environment.
As explained, a framework may provide for efficient data processing and real-time adaptability. A framework may provide an ability to efficiently process datasets and adapt in near real-time to new data and/or changing conditions. Such a framework may facilitate quicker, data-driven decisions that lead to optimized operations and improved resource allocation. As an example, a framework may be part of a stimulation operations suite of tools that may improve reservoir design, performance, operations, etc., at least in part via optimization of hydraulic fracturing operations in the field in real-time or near real-time.
As explained, a framework can provide for implementation of an end-to-end workflow that ranges from candidate identification to optimization that streamlines operations, thereby ensuring a cohesive and systematic approach towards achieving optimization goals, thus enhancing operational efficiency.
As explained, a framework may provide for customized objective functions, whether for single variable or multi-variable optimizations. Such flexibility to define custom objective functions can allow for a tailored optimization approach, which may aim to align closely with specific operational and resource goals. Such an approach may improve outcomes due at least to objective function flexibility compared to more rigid, pre-defined optimization techniques.
As explained, a framework may be scalability and adaptability. For example, an ML-based approach can be scalable to accommodate large amounts of field data and/or to augment field data (e.g., via transformations, synthetic field data, etc.) and may be adaptable for conventional reservoirs and unconventional reservoirs. Such an approach can aim to meet diverse operational demands, making a framework a more versatile and future-proof product.
As an example, a framework may be implemented to enhance hydraulic fracturing operations, achieve better production rates, and reduce operational costs through data-driven decisions and optimized fracturing parameters. As explained, a framework may be integrated with one or more other frameworks, environments, systems, etc. For example, consider integration with one or more products for reservoir management and monitoring to create a more robust and comprehensive operational platform.
As explained, a framework may provide for enhanced real-time monitoring and/or control of field operations. For example, such a framework may be integrated with one or more real-time data acquisition technologies, which may thereby provide for real-time monitoring and optimization of hydraulic fracturing operations to enhance responsiveness of field operations to changing reservoir conditions.
As to types of machine learning (ML) models, consider one or more of a support vector machine (SVM) model, a k-nearest neighbors (KNN) model, an ensemble classifier model, a neural network (NN) model, etc. As an example, a machine learning model can be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back-propagation, Hopfield network, etc.), a regularization model (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression), a rule system model (e.g., cubist, one rule, zero rule, repeated incremental pruning to produce error reduction), a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, logistic regression, etc.), a Bayesian model (e.g., naïve Bayes, average on-dependence estimators, Bayesian belief network, Gaussian naïve Bayes, multinomial naïve Bayes, Bayesian network), a decision tree model (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, C5.0, chi-squared automatic interaction detection, decision stump, conditional decision tree, M5), a dimensionality reduction model (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.
As an example, a machine model may be built using a computational framework with a library, a toolbox, etc., such as, for example, those of the MATLAB framework (MathWorks, Inc., Natick, Massachusetts). The MATLAB framework includes a toolbox that provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor (KNN), k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Another MATLAB framework toolbox is the Deep Learning Toolbox (DLT), which provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. The DLT provides convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. The DLT includes features to build network architectures such as generative adversarial networks (GANs) and Siamese networks using custom training loops, shared weights, and automatic differentiation. The DLT provides for model exchange various other frameworks.
As an example, the TENSORFLOW framework (Google LLC, Mountain View, CA) may be implemented, which is an open-source software library for dataflow programming that includes a symbolic math library, which can be implemented for machine learning applications that can include neural networks. As an example, the CAFFE framework may be implemented, which is a DL framework developed by Berkeley AI Research (BAIR) (University of California, Berkeley, California). As another example, consider the SCIKIT platform (e.g., scikit-learn), which utilizes the PYTHON programming language. As an example, a framework such as the APOLLO AI framework may be utilized (APOLLO.AI GmbH, Germany). As an example, a framework such as the PYTORCH framework may be utilized (Facebook AI Research Lab (FAIR), Facebook, Inc., Menlo Park, California).
As an example, a training method can include various actions that can operate on a dataset to train a ML model. As an example, a dataset can be split into training data and test data where test data can provide for evaluation. A method can include cross-validation of parameters and best parameters, which can be provided for model training.
The TENSORFLOW framework can run on multiple CPUs and GPUs (with optional CUDA (NVIDIA Corp., Santa Clara, California) and SYCL (The Khronos Group Inc., Beaverton, Oregon) extensions for general-purpose computing on graphics processing units (GPUs)). TENSORFLOW is available on 64-bit LINUX, MACOS (Apple Inc., Cupertino, California), WINDOWS (Microsoft Corp., Redmond, Washington), and mobile computing platforms including ANDROID (Google LLC, Mountain View, California) and IOS (Apple Inc.) operating system-based platforms.
TENSORFLOW computations can be expressed as stateful dataflow graphs; noting that the name TENSORFLOW derives from the operations that such neural networks perform on multidimensional data arrays. Such arrays can be referred to as “tensors”.
As an example, one or more features of the KERAS library may be utilized. The KERAS library is an open-source library that provides a Python interface for artificial neural networks (ANNs). The KERAS library can act as an interface for the TENSORFLOW library.
As an example, a device may utilize TENSORFLOW LITE (TFL) or another type of lightweight framework. TFL is a set of tools that enables on-device machine learning where models may run on mobile, embedded, and IoT devices. TFL is optimized for on-device machine learning, by addressing latency (no round-trip to a server), privacy (no personal data leaves the device), connectivity (Internet connectivity is demanded), size (reduced model and binary size) and power consumption (e.g., efficient inference and a lack of network connections). TFL includes multiple platform support, covering ANDROID and iOS devices, embedded LINUX, and microcontrollers. TLF provides diverse language support, which includes JAVA, SWIFT, Objective-C, C++, and PYTHON. TFL provides high performance, with hardware acceleration and model optimization. As an example, one or more machine learning tasks may include, for example, classification, regression, object detection, pose estimation, question answering, text classification, etc., on one or more of multiple platforms.
The method 1100 is shown in
In the example of
As an example, a method can include receiving data for a well in a field and parameter values for hydraulic fracturing of the well in the field; predicting production data responsive to the hydraulic fracturing of the well using at least a portion of the data and at least a portion of the parameter values as input to a machine learning model, where the machine learning model is trained using historical data for the field; and outputting the predicted production data.
As an example, a method may include determining one or more parameter values using an additional machine learning model, for example, where the additional machine learning model receives initial parameter values as input and/or determines one or more values for one or more fracture design parameters. As an example, one or more fracture design parameters may include one or more of fracture length, fracture height, and fracture conductivity.
As an example, a method may include generating one or more optimized parameter values using output predicted production data (e.g., from one or more trained ML models). In such an example, the generating can include implementing an objective function in an iterative optimization loop. In such an example, the iterative optimization loop can include iteratively predicting production data responsive to updating one or more of the parameter values. In such an example, the updating to the one or more of the parameter values can include implementing an additional machine learning model, for example, where the additional machine learning model determines one or more values of one or more fracture design parameters.
As an example, a method can include selecting a well from a plurality of wells. In such an example, the selecting the well can include generating one or more stimulation recommendations for each of the plurality of wells. In such an example, the one or more stimulation recommendations can include one or more of a perforation recommendation, a matrix acidizing recommendation, and a hydraulic fracturing recommendation. As an example, a method can include generating that includes determining one or more of a heterogeneity index, a formation damage index, and a productivity index.
As an example, a method can include selecting a well in a manner that occurs responsive to generating a hydraulic fracturing recommendation for the well. As an example, a method can include selecting a well in a manner that includes determining that the well is an existing well that is underperforming as to fluid production. In such an example, the existing well may be a hydraulically fractured well where, for example, hydraulic fracturing may be recommended as to re-fracturing of the hydraulically fracture well. For example, re-fracturing may involve increasing fluid pressure in a well to stimulate existing fractures and/or to generate new fractures.
As an example, a field can include a well in fluid communication with a conventional reservoir or an unconventional reservoir. As an example, a field may be characterized as conventional or unconventional.
As an example, a system can include a processor; a memory operatively coupled to the processor; processor-executable instructions stored in the memory and executable to instruct the system to: receive data for a well in a field and parameter values for hydraulic fracturing of the well in the field; predict production data responsive to the hydraulic fracturing of the well using at least a portion of the data and at least a portion of the parameter values as input to a machine learning model, where the machine learning model is trained using historical data for the field; and output the predicted production data.
As an example, one or more computer-readable storage media can include processor-executable instructions executable by a system to instruct the system to: receive data for a well in a field and parameter values for hydraulic fracturing of the well in the field; predict production data responsive to the hydraulic fracturing of the well using at least a portion of the data and at least a portion of the parameter values as input to a machine learning model, where the machine learning model is trained using historical data for the field; and output the predicted production data.
As an example, a computer program product can include one or more computer-readable storage media that can include processor-executable instructions to instruct a computing system to perform one or more methods and/or one or more portions of a method.
In some embodiments, a method or methods may be executed by a computing system.
As an example, a system can include an individual computer system or an arrangement of distributed computer systems. In the example of
As an example, a module may be executed independently, or in coordination with, one or more processors 1204, which is (or are) operatively coupled to one or more storage media 1206 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 1204 can be operatively coupled to at least one of one or more network interfaces 1207; noting that one or more other components 1208 may also be included. In such an example, the computer system 1201-1 can transmit and/or receive information, for example, via the one or more networks 1209 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.).
As an example, the computer system 1201-1 may receive from and/or transmit information to one or more other devices, which may be or include, for example, one or more of the computer systems 1201-2, etc. A device may be located in a physical location that differs from that of the computer system 1201-1. As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.
As an example, a processor may be or include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.
As an example, the storage media 1206 may be implemented as one or more computer-readable or machine-readable storage media. As an example, storage may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems.
As an example, a storage medium or storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLUERAY disks, or other types of optical storage, or other types of storage devices.
As an example, a storage medium or media may be located in a machine running machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution. As an example, various components of a system such as, for example, a computer system, may be implemented in hardware, software, or a combination of both hardware and software (e.g., including firmware), including one or more signal processing and/or application specific integrated circuits.
As an example, a system may include a processing apparatus that may be or include a general-purpose processors or application specific chips (e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriate devices.
As an example, a device may be a mobile device that includes one or more network interfaces for communication of information. For example, a mobile device may include a wireless network interface (e.g., operable via IEEE 802.11, ETSI GSM, BLUETOOTH, satellite, etc.). As an example, a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g., optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery. As an example, a mobile device may be configured as a cell phone, a tablet, etc. As an example, a method may be implemented (e.g., wholly or in part) using a mobile device. As an example, a system may include one or more mobile devices.
As an example, a system may be a distributed environment, for example, a so-called “cloud” environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc. As an example, a device or a system may include one or more components for communication of information via one or more of the Internet (e.g., where communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based service).
As an example, information may be input from a display (e.g., consider a touchscreen), output to a display or both. As an example, information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed. As an example, information may be output stereographically or holographically. As to a printer, consider a 2D or a 3D printer. As an example, a 3D printer may include one or more substances that can be output to construct a 3D object. For example, data may be provided to a 3D printer to construct a 3D representation of a subterranean formation. As an example, layers may be constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example, holes, fractures, etc., may be constructed in 3D (e.g., as positive structures, as negative structures, etc.).
Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures.
This application claims the benefit of and priority to a U.S. Provisional Application having Ser. No. 63/613,146, filed 21 Dec. 2023, which is incorporated by reference herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63613146 | Dec 2023 | US |
