Patent Application
20230359793
Basin and petroleum system modeling relates to simulating the geological evolution of sedimentary basin and its associated petroleum systems. Generally, a number of processes are considered, including pore pressure and compaction, rock stress and failure, temperature predictions as well as the geochemical processes inside organic rich source rocks and hydrocarbon migration and accumulation. One specific use case is pore pressure prediction on a basin scale, which can be used to assess drilling risks.
There generally exist uncertainties for various input parameters for this type of modeling. For example, in pore pressure prediction, input parameters that may include a degree of uncertainty may include rock permeabilities, compaction parameters, facies models, and paleo-erosion amounts. Data from existing wells can be used as validation points for petroleum system models and to control pressures and porosities at the well location.
Ensemble-based statistical approaches consider a number of different realizations of the input parameters, and thereby provide a mechanism to ensure that predicted pressures and porosities match the observed well parameter at the well location. Ensemble approaches may also enable predictions for pore pressure values into unknown areas, e.g., in areas in which a well may be planned to extend.
The number of realizations called for to accurately describe the uncertainty for the petroleum system model may be relatively high, often on the order of 100 to 10,000. As the costs associated with high-performance computing resources used to perform such simulations are also high, ensemble-based approaches may be economically impractical. Accordingly, basin and petroleum system modeling generally restricts the analysis to either the best case or a few selected manually created realizations, hence limiting the applicability of petroleum system modeling.
Embodiments of the disclosure include a method for simulating a subterranean volume that includes receiving one or more input parameters and one or more simulation realizations representing the subterranean volume, modeling the one or more simulation realizations as a target function of the one or more input parameters, training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations, predicting a value for the target function based on a first candidate simulation or a first candidate output parameter of a simulation, selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function, and simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both.
In an embodiment, the method includes predicting a second value for the target function based on at least one of a second candidate simulation or a second candidate output parameter, and determining not to simulate the subterranean volume using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function.
In an embodiment, selecting the first candidate simulation, the first candidate output parameter, or both is based on the first candidate simulation or the first candidate output parameter minimizing the first value of the target function.
In an embodiment, simulating the subterranean volume includes simulating the subterranean volume using an ensemble of different realizations including the selected first candidate simulation.
In an embodiment, the first candidate simulation, the first candidate output parameter, or both are selected for simulating prior to simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both.
In an embodiment, predicting the first candidate output parameter includes determining one or more statistical characteristics for values of the first candidate output parameter.
In an embodiment, the method further includes generating a visualization of the subterranean volume based on simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both.
In an embodiment, the method also includes adjusting a weight of a mud in a well based at least in part on the simulating, wherein the simulating is configured to predict a pore pressure, a fracture gradient, or both in a rock formation.
Embodiments of the disclosure also include a computing system including one or more processors, and a memory system including one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the computing system to perform operations. The operations include receiving one or more input parameters and one or more simulation realizations representing a subterranean volume, modeling the one or more simulation realizations as a target function of the one or more input parameters, training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations, predicting a value for the target function based on a first candidate simulation or a first candidate output parameter of a simulation, selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function, and simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both.
Embodiments of the disclosure also include a non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include receiving one or more input parameters and one or more simulation realizations representing a subterranean volume, modeling the one or more simulation realizations as a target function of the one or more input parameters, training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations, predicting a value for the target function based on a first candidate simulation or a first candidate output parameter of a simulation, selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function, and simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both.
It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and/or claimed below. Accordingly, this summary is not intended to be limiting.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.
It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.
The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.
Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and/or the order of some operations may be changed.
In the example of
In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.
In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.
In the example of
As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (Schlumberger Limited, Houston Texas), the INTERSECT′ reservoir simulator (Schlumberger Limited, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc.).
In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (Schlumberger Limited, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).
In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (Schlumberger Limited, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).
As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.
In the example of
As an example, the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).
In the example of
In the example of
As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).
In some embodiments of the present disclosure, an alternative to the computationally-intensive ensemble simulation technique is provided. In such embodiments, the outcome of a petroleum system model may be predicted by a machine-learning model instead of (or potentially in addition to in parallel with) a full simulation. This may reduce simulation costs, while improving the prediction quality of petroleum system modeling. As such, embodiments of the present disclosure may both improve efficiency and improve accuracy of the modeling process, which may in turn enhance exploration, drilling, production, and other oilfield activities.
In an embodiment, a simulation of a petroleum system model can be considered as an evaluation of a function ƒ. The input parameters of the function are given by the model input parameters considered to be uncertain: {x1, x2, . . . , xL}=x, where L denotes the total number of input parameters and xi the value of the i-th parameter (which could be e.g. a shift of a permeability of a specific lithology or a parameter used for compaction). Output parameters can be split into two different types: output where calibration data exists y1, y2, . . . , yM and output for which a prediction should be performed: yM+1, yM+2, . . . , yM+N. A simulation of a specific realization can now be considered as an evaluation of a function:
ƒ(x)=y={y1, . . . ,yM,yM+1, . . . ,yM+N}
One approach to model an ensemble of realizations is to define a target function:
where
Machine learning may be considered to model generic functions. A machine-learning model is trained with a set of known data points {x(k), y(k)} to obtain a function ƒML as an approximation, e.g.: ƒML(x(k))≈y(k). Note that it is possible to either build a model with only a single target quantity, e.g. which reproduces yi for a single i, or to build models with multiple target quantities. A number of different machine-learning algorithms exists, e.g. Random forests or gradient boosting trees.
The method 200 may then include modeling an ensemble of realizations of the petroleum system simulations as a target function χ2(x), as at 206, as described above.
The method 200 may then select one of two paths or may conduct these two paths in parallel or in series and combine the results thereof. The first path begins at 208, where a machine-learning model is trained to predict the target function based on the set of ensemble realizations, as at 208. In other words, the ensemble is used to train a machine-learning model to predict χ2 (x), without having to compute the entire simulation. The machine learning model may then be implemented to predict the target function.
The prediction is used to identify/select one or more new realizations, based on the value of the target function associated with the realizations, as at 210. For example, lower values for the target function may represent suitable candidates for new realizations to be added to the ensemble. These identified simulations may then be run (computed) and added to the ensemble, as at 212. As such, the impact and/or accuracy of the new simulations is first predicted prior to expending the resources to conduct the simulation. Accordingly, at least some candidate simulations may be included based on resulting in low values for the target function (e.g., minimizing the target function), while others may be disregarded without simulation, based on a prediction that they do not result in a low target function value.
In the other path, one or more machine-learning models may be trained to predict individual simulation output parameters, as at 216. These predictions may be employed to identify specific (output) parameters for inclusion in the ensemble simulations, as at 218. In other words, the ensemble may be used to train machine-learning models to predict ƒ(x) (targeting either one parameter per machine-learning model or a multiple parameter per machine-learning model). These predictions can then be used to reduce (e.g., minimize) χ2(x).
In this aspect of the method 200, the selection which “candidate” (one or a subset of possible choices) output parameter y may be included may be made prior to the simulations being performed. For example, a value of the pore pressure at 1000 m depth for a set of input parameters may be predicted. This may provide insight into statistical characteristics (e.g., distributions) of the output parameter (including quantities such as average, variance, P10/P50/P90, etc.). As such, additional simulations may not be called for.
The machine-learning model's prediction and/or the simulated output values may be used to assess the target parameter yM+1, yM+2, . . . , yM+N considering both expectation values as well as variances (other statistical quantities might also be considered), as at 222. Depending on this, the method 200 may be iteratively repeated or considered as completed.
As a result of the ensemble simulations, a visualization of the subterranean domain including, for example, simulated pore pressure, fluid flow regimes, geology, lithology, facies models, basin models, etc. may be produced, as at 224. Such visualization may be a digital model that is displayed on a computer display. Further, based on the simulation and/or the visualization, a drilling operation may be planned or modified, as at 226. For example, drilling parameters, trajectory, geometry, etc., may be modified based on pore pressure, e.g., as predicted using the method 200.
A variety of practical use-cases are contemplated, and others may be developed based on the present disclosure. For example, pre-pressure and rock stress predictions may be made, which may facilitate the drilling process. More particularly, an area may include a number of wells, each with measured pressure data generated based on mud weights, drill stem tests, leak-off tests, etc. A geological model may also be constructed to represent the area. The pressure and rock stress distribution for a to-be-drilled well may thus be predicted. Such prediction may proceed by using a basin model to predict pressures. The predictions may be validated/calibrated against existing pressure data (e.g., from existing wells). An embodiment of the present method may then be employed to predict pressure and rock stress for the target well, e.g., without running at least some of the model realizations (or using a subset of the parameters) that might otherwise be used with a full model simulation of an ensemble of realizations.
As can be seen in
Another use case may be hydrocarbon quality (e.g., composition) prediction in a reservoir. In this case, petroleum systems models (e.g., models of basin temperatures, pressures, geochemical processes such as hydrocarbon generation, migration, accumulation over geological times) may be used to predict hydrocarbon quality (e.g., compositions, densities (API gravity), gas-oil-ratio). Embodiments of the present disclosure may be used to calibrate against existing values of known/existing neighboring oil fields and/or to analyze the impact of uncertainties (e.g., thermal evolution of the basin, geochemical properties, etc.).
For example,
In some embodiments, the methods of the present disclosure may be executed by a computing system.
A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.
The storage media 506 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of
In some embodiments, computing system 500 contains one or more simulation modeling module(s) 508. In the example of computing system 500, computer system 501A includes the simulation modeling module 508. In some embodiments, a single simulation modeling module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of simulation modeling modules may be used to perform some aspects of methods herein.
It should be appreciated that computing system 500 is merely one example of a computing system, and that computing system 500 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of
Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of the present disclosure.
Computational interpretations, models, and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 500,
The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrate and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principals of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.
This application claims priority to U.S. Provisional Patent Application having Ser. No. 62/706,999, which was filed on Sep. 23, 2020 and is incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/071560 | 9/23/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 62706999 | Sep 2020 | US |
