Terrestrial heat development scene multi-scale intelligent simulation method based on layered coupling

Abstract

The invention provides a geothermal development scene multi-scale intelligent simulation method based on layered coupling, and belongs to the field of intelligent simulation, and the method comprises the steps: constructing a plurality of scale models corresponding to geothermal resource data according to a geothermal development scene; constructing a multi-scale simulation model, and performing deep feature learning on the multi-scale simulation model by using a deep neural network to obtain deep feature representation; according to deep feature representation of the multi-scale simulation model, obtaining a common subspace through feature fusion, and carrying out feature balance and feature alignment to obtain a layered coupling type multi-scale simulation model; and carrying out simulation experiment verification on the layered coupling type multi-scale simulation model, and building a simulation platform oriented to a geothermal development scene. According to the method, the problem that cross-scale feature fusion of a multi-scale framework is difficult to realize due to the fact that an existing simulation method cannot fully analyze and utilize deep features of simulation models with different scales, especially a heterogeneous gap possibly caused by the dimension difference of a feature space is solved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent simulation, and in particular relates to a multi-scale intelligent simulation method for geothermal development scenarios based on hierarchical coupling. Background Art

[0002] Geothermal resources are widely found in the earth's crust and are a clean, stable and renewable energy source. Geothermal development mainly includes exploration, drilling, resource extraction, utilization and management of geothermal resources. Geothermal modeling refers to the mathematical simulation of the formation, distribution, flow, extraction and utilization of geothermal resources to optimize development strategies and predict development effects. Traditional modeling methods use numerical methods such as finite element method and finite difference to solve key values ​​such as temperature and pressure distribution in geothermal fields. Physical knowledge-driven deep learning is an effective tool for computer simulation, but most existing methods are limited to single-scale scenarios characterized by simple models. In the process of deep geothermal exploitation, high-pressure fluids are injected into the ground through deep boreholes, forcing the micro-nano pores in the target rock formation to expand to several millimeters. If the giant fracture network formed in the end is considered, the research scope will be expanded to several kilometers. However, observations and experimental results show that the influence of microscopic damage accumulation in rocks on fracture behavior is often not accurately described by macroscopic continuous models. Therefore, how to develop a universal cross-scale feature fusion method for efficient communication of multi-scale information is the key to achieving multi-scale simulation through deep learning.

[0003] With the rapid development of artificial intelligence technology, the use of deep learning methods to achieve intelligent simulation of physical problems is attracting widespread attention from academia and industry. Currently, among the many attempts to apply deep learning methods to multi-scale simulation, there are mainly studies on combining neural networks with traditional numerical methods, multi-scale model coupling methods based on data-driven or dynamic importance sampling, and the application of transfer learning to simulation.

[0004] However, the above simulation methods simply rely on the knowledge-data integrated driven characteristics of deep learning methods, and do not fully analyze and utilize the deep features of simulation models of different scales. The dimensional differences in feature space may cause heterogeneous gaps, which makes it difficult for multi-scale frameworks to achieve cross-scale feature fusion; at the same time, the field of deep learning simulation has not yet seen a method that takes into account the deep feature learning of simulation models and does not effectively complete the hierarchical coupling of multi-scale models. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a multi-scale intelligent simulation method for geothermal development scenarios, which solves the problem that the existing simulation methods are unable to fully analyze and utilize the deep characteristics of simulation models of different scales themselves, and the dimensional differences in the feature space cause a heterogeneous gap, making it difficult for the multi-scale framework to achieve cross-scale feature fusion.

[0006] In order to achieve the above objectives, the technical solution adopted by the present invention is: a multi-scale intelligent simulation method for geothermal development scenarios, comprising the following steps: S1. According to the geothermal development scenario, obtain geothermal resource data and construct multiple scale models corresponding to the geothermal resource data; S2. Construct a multi-scale simulation model based on multiple scale models, use a deep neural network to perform deep feature learning on the multi-scale simulation model, and obtain a deep feature representation of the multi-scale simulation model; S3. According to the deep feature representation of the multi-scale simulation model, feature fusion is performed on the cross-scale feature space of the multi-scale simulation model to obtain a common subspace, and feature balancing is performed on the common subspace to obtain a hierarchical coupled multi-scale simulation model; S4. Based on the physical process, the hierarchical coupled multi-scale simulation model is verified by simulation experiments, and the verified hierarchical coupled multi-scale simulation model is used to build a simulation platform for geothermal development scenarios to realize multi-scale hierarchical coupled intelligent simulation.

[0007] The beneficial effects of the present invention are as follows: the present invention utilizes physical information neural operators to fully analyze and utilize the deep features of simulation models of different scales themselves, thereby improving the pertinence and feasibility of the simulation of the present invention, and realizing a multi-scale framework that takes into account both deep feature learning of simulation models and cross-scale feature fusion, thereby enriching the existing multi-scale simulation theory; by cross-scale fusion of multi-scale features to obtain a common subspace, the micro-scale model provides missing information to the overall macro-scale model, thereby realizing hierarchical coupling modeling, improving the integrity of feature information, and reducing the dimensionality difference of feature space.

[0008] Further, the S2 comprises the following steps: S201, using the prior knowledge of the same physical process in multiple scale models and geothermal resource data, pre-training the physical information neural operator to construct an initial multi-scale simulation model; S202, embedding features into the initial multi-scale simulation model to obtain a multi-scale simulation model with embedded features; S203, designing a targeted representation learning model based on the multi-scale simulation model with embedded features, and constructing a deep neural network for learning and representing deep features of the multi-scale simulation model based on the targeted representation learning model; S204. Using a deep neural network to learn the multi-scale simulation model with embedded features, the feature space of the multi-scale simulation model is obtained, and a deep feature representation of the multi-scale simulation model related to the geothermal reservoir is obtained.

[0009] Furthermore, the expression for embedding features of the multi-scale simulation model is as follows: ; in, Represented by a neural network The function represented by y The predicted value of Represents the input function The feature embedding of Indicates input coordinates y The feature embedding of q represents the number of basis functions selected in the target space, Representing a neural network Parameters of ; among them, , , express q Euclidean real number space.

[0010] The beneficial effects of the above further scheme are as follows: the present invention eliminates the semantic gap by designing a targeted representation learning model, and constructs a deep neural network to learn and represent the deep features of the pre-trained physical information neural operator simulation model, thereby improving the correlation between the deep features and the geothermal reservoir. The established simulation model feature space lays a solid foundation for the hierarchical coupling of multi-scale features.

[0011] Furthermore, the S3 comprises the following steps: S301, according to the deep feature representation of the multi-scale simulation model, the cross-scale features of the multi-scale simulation model are fused to obtain a common subspace; S302, using the topological manifold structure of the model data, balancing the feature representations under different scale models to obtain a common subspace after balancing the features; S303, constructing a hierarchical coupled multi-scale simulation model through the transformation matrix between the original high-dimensional features and the low-dimensional common subspace.

[0012] Furthermore, the expression for constructing the hierarchical coupled multi-scale simulation model is as follows: ; in, represents the transformation path, represents the transformation factor, and or 1, Represents the transformation matrix from the original high-dimensional features to the low-dimensional common subspace, Represents the transformation matrix from the low-dimensional common subspace to the original high-dimensional features.

[0013] The beneficial effects of the above further scheme are as follows: the present invention realizes feature fusion from feature spaces of models of different scales to common subspaces by using a physical information operator method for developing multi-view feature fusion, and introduces the idea of ​​topological manifolds, thereby balancing the consistency and complementarity between different view representations, improving the robustness of the present invention, and bridging the heterogeneous gap; and through the transformation matrix between the original high-dimensional features and the low-dimensional common subspace, a hierarchical coupling mechanism for simulation models of different scales is established, thereby realizing the multi-scale simulation of complex physical systems in the hierarchical coupling form of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The figure is a flow chart of the method of the present invention.

[0015] Figure 2 This is the overall structural diagram of the multi-scale intelligent simulation method for geothermal development scenarios based on hierarchical coupling in this embodiment. DETAILED DESCRIPTION

[0016] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0017] Before describing this embodiment, the following terms are explained: PINO: Physical Information Neural Operator; K-means algorithm: K-means algorithm.

[0018] Example like Figure 1 As shown, the present invention provides a multi-scale intelligent simulation method for geothermal development scenarios, and its implementation method is as follows: S1. According to the geothermal development scenario, geothermal resource data is obtained and multiple scale models corresponding to the geothermal resource data are constructed.

[0019] In this embodiment, the deep characteristics of the multi-scale simulation model are related to the long-term evolution of the geothermal reservoir, the cross-scale correlation behavior, and the coupling relationship between different physical fields; like Figure 2As shown in the figure, the physical models in the form of differential equations at different scales of geothermal development scenarios can be divided into: macroscale models (continuous models based on the conservation laws of quantities such as mass, momentum and energy), mesoscale models (particle dynamics models that simulate the mechanical interactions between rock units) and microscale models (particle models based on first-principles molecular dynamics, quantum mechanics and Newtonian mechanics).

[0020] S2. Based on multiple scale models, a multi-scale simulation model is constructed, and a deep neural network is used to perform deep feature learning on the multi-scale simulation model to obtain a deep feature representation of the multi-scale simulation model. The specific steps are as follows: S201, using the prior knowledge of the same physical process in multiple scale models and geothermal resource data, pre-training the physical information neural operator to construct an initial multi-scale simulation model; S202, embedding features into the initial multi-scale simulation model to obtain a multi-scale simulation model with embedded features; S203, designing a targeted representation learning model based on the multi-scale simulation model with embedded features, and constructing a deep neural network for learning and representing deep features of the multi-scale simulation model based on the targeted representation learning model; S204. Using a deep neural network to learn the multi-scale simulation model with embedded features, the feature space of the multi-scale simulation model is obtained, and a deep feature representation of the multi-scale simulation model related to the geothermal reservoir is obtained.

[0021] In this embodiment, in order to realize the learning and representation of the deep features of the multi-scale simulation model, the prior knowledge of the physical model in the form of differential equations of the same physical process at multiple scales is used to pre-train the physical information neural operator, and a simulation model of the corresponding scale, that is, a multi-scale simulation model, is established; according to the process of learning the parameterized partial differential equation solving operator based on the physical information neural operator model, in the process of constructing the physical information neural operator, feature embedding based on Fourier features (RFF) is used to improve the performance of the method of the present invention, and the calculation formula is as follows: ; in, Represented by a neural network The function represented by y The predicted value of Represents the input function The feature embedding of Indicates input coordinates y The feature embedding of q represents the number of basis functions selected in the target space, Representing a neural network Parameters of ; among them, , , express q dimensional Euclidean real space; based on automatic differentiation, construct differential operators and convert the loss function based on partial differential equations , loss function based on initial conditions And the loss function based on boundary conditions Minimize and get the optimal parameters , complete the physical information neural operator optimization.

[0022] In this embodiment, the multi-scale simulation model of the present invention is constructed based on a physical information neural operator network; a targeted representation learning model for the PINO simulation model is designed, and a deep neural network for learning and representing deep features of the model is constructed on the basis of eliminating the semantic gap, thereby learning the feature space of the PINO simulation model at multiple scales, and obtaining the deep feature representation of the multi-scale simulation model related to the geothermal reservoir.

[0023] S3. According to the deep feature representation of the multi-scale simulation model, the cross-scale feature space of the multi-scale simulation model is feature-fused to obtain a common subspace, and the common subspace is feature-balanced to obtain a hierarchical coupled multi-scale simulation model. The specific steps are as follows: S301, according to the deep feature representation of the multi-scale simulation model, the cross-scale features of the multi-scale simulation model are fused to obtain a common subspace; S302, using the topological manifold structure of the model data, balancing the feature representations under different scale models to obtain a common subspace after balancing the features; S303, constructing a hierarchical coupled multi-scale simulation model through the transformation matrix between the original high-dimensional features and the low-dimensional common subspace.

[0024] In this embodiment, according to the tensor-based canonical correlation analysis method (TCCA), the study constructs the fusion from the feature space of the multi-scale model to the same common subspace, and realizes the preliminary fusion of the multi-scale simulation model features; the overall method spans multiple scales of micro, meso and macro, extracts the key features of each scale, and then integrates the extracted key features through the fusion mechanism to construct a simulation model with global consistency; Specifically, Figure 2 As shown, based on the deep feature representation of the multi-scale simulation model, the feature representation of the learned multi-scale simulation model is used ( p =1,2,…,n; n represents the total number of views, which is the total number of models of different scales in this embodiment), calculate the covariance tensor of all views , which is used to discover the relevant information between all views. By simplifying the calculation, it is transformed into solving the equivalent problem of maximizing the typical correlation of multiple views, that is, finding a set of optimal rank tensors ( k =1,2,…,r; r represents the feature dimension after dimensionality reduction, which should be less than the minimum value of the feature dimensions of different views), so that the covariance tensor can be represented as a rank tensor The weighted sum of ; the calculation expression is as follows: ; in, represents the tensor product, Represents; and ranks a tensor Integrate into a transformation matrix , the transformation matrix Acting on feature representation , initially realize the mapping from original high-dimensional features to low-dimensional common subspace; Furthermore, considering that simulation models of different scales may have large differences in feature representation due to the different physical models they use, constructing a common subspace based solely on tensor-based canonical correlation maximization cannot completely bridge the heterogeneous gap; after combining the research work on multi-view clustering, the idea of ​​topological manifold is introduced, and the topological manifold structure of the model data is used to balance the consistent structure and complementary information between the feature representations of simulation models of different scales, thereby improving the robustness of the PINO method for multi-view feature fusion and bridging the heterogeneous gap between feature spaces of different views.

[0025] In this embodiment, according to the common subspace after balancing the characteristics, such as Figure 2 As shown in the figure, the intelligent simulation model based on the neural operator network at different scales transforms the feature representation through the transformation matrix between the original high-dimensional features and the low-dimensional common subspace to obtain a hierarchical coupled multi-scale simulation model. The transformation expression is as follows: ; in, Represents the transformation path, which is used to define the transformation direction between the original high-dimensional features and the low-dimensional subspace. represents the transformation factor, and or 1, Represents the transformation matrix from the original high-dimensional features to the low-dimensional common subspace. hour, , the multi-scale simulation model will realize the transformation from the original high-dimensional features to the low-dimensional common subspace, Represents the transformation matrix from the low-dimensional common subspace to the original high-dimensional features. hour, , the multi-scale simulation model will realize the transformation from low-dimensional common subspace to original high-dimensional features.

[0026] S4. Based on the physical process, the hierarchical coupled multi-scale simulation model is verified by simulation experiments, and the verified hierarchical coupled multi-scale simulation model is used to build a simulation platform for geothermal development scenarios to realize hierarchical coupled multi-scale intelligent simulation.

[0027] In this embodiment, a hierarchical coupled multi-scale simulation model is used to conduct simulation experiments based on typical physical processes, and deep learning of the hierarchical coupled multi-scale simulation model is used as a multi-scale simulation tool. The multi-scale simulation tool with verified performance is used to build a simulation platform for geothermal development scenarios to achieve hierarchical coupled multi-scale simulation.

[0028] In this embodiment, the deep feature representation of the multi-scale simulation model and the hierarchical coupled multi-scale simulation model are used to develop a PINO method for hierarchical coupled multi-scale simulation.

[0029] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0030] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0031] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A multi-scale intelligent simulation method for geothermal development scenarios based on hierarchical coupling, characterized in that: The following steps are involved: S1. According to the geothermal development scenario, obtain geothermal resource data and construct multiple scale models corresponding to the geothermal resource data; S2. Construct a multi-scale simulation model based on multiple scale models, use a deep neural network to perform deep feature learning on the multi-scale simulation model, and obtain a deep feature representation of the multi-scale simulation model; S3. According to the deep feature representation of the multi-scale simulation model, feature fusion is performed on the cross-scale feature space of the multi-scale simulation model to obtain a common subspace, and feature balancing is performed on the common subspace to obtain a hierarchical coupled multi-scale simulation model; S4. Based on the physical process, the hierarchical coupled multi-scale simulation model is verified by simulation experiments, and the verified hierarchical coupled multi-scale simulation model is used to build a simulation platform for geothermal development scenarios to realize multi-scale hierarchical coupled intelligent simulation.

2. The multi-scale intelligent simulation method for geothermal development scenarios based on hierarchical coupling according to claim 1 is characterized in that: The S2 comprises the following steps: S201, using the prior knowledge of the same physical process in multiple scale models and geothermal resource data, pre-training the physical information neural operator to construct an initial multi-scale simulation model; S202, embedding features into the initial multi-scale simulation model to obtain a multi-scale simulation model with embedded features; S203, designing a targeted representation learning model based on the multi-scale simulation model with embedded features, and constructing a deep neural network for learning and representing deep features of the multi-scale simulation model based on the targeted representation learning model; S204. Using a deep neural network to learn the multi-scale simulation model with embedded features, the feature space of the multi-scale simulation model is obtained, and a deep feature representation of the multi-scale simulation model related to the geothermal reservoir is obtained.

3. The multi-scale intelligent simulation method for geothermal development scenarios based on hierarchical coupling according to claim 2 is characterized in that: The expression for embedding features of the multi-scale simulation model is as follows: in, Represented by a neural network The function represented by y The predicted value of Represents the input function The feature embedding of Indicates input coordinates y The feature embedding of q represents the number of basis functions selected in the target space, Representing a neural network Parameters of ; among them, , , express q Euclidean real number space.

4. The multi-scale intelligent simulation method for geothermal development scenarios based on hierarchical coupling according to claim 1 is characterized in that: The S3 comprises the following steps: S301, according to the deep feature representation of the multi-scale simulation model, the cross-scale features of the multi-scale simulation model are fused to obtain a common subspace; S302, using the topological manifold structure of the model data, balancing the feature representations under different scale models to obtain a common subspace after balancing the features; S303, constructing a hierarchical coupled multi-scale simulation model through the transformation matrix between the original high-dimensional features and the low-dimensional common subspace.

5. The multi-scale intelligent simulation method for geothermal development scenarios based on hierarchical coupling according to claim 4 is characterized in that: The expression for constructing the hierarchical coupled multi-scale simulation model is as follows: in, represents the transformation path, represents the transformation factor, and or 1, Represents the transformation matrix from the original high-dimensional features to the low-dimensional common subspace, Represents the transformation matrix from the low-dimensional common subspace to the original high-dimensional features.

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