A hydraulic fracturing problem finite element modeling system and method based on a large language model

Abstract

The application discloses a hydraulic fracturing problem finite element modeling system and method based on a large language model. The hydraulic fracturing problem finite element modeling system based on the large language model comprises a client, a cloud service platform and a computing server. The client is composed of an interactive interface and a display interface. The cloud service platform comprises a storage space, a first input end, a second input end, a third input end, a transmission end, a large language model that has been trained, a grid tool and a parameter grid coupling tool. The computing server comprises a storage unit, a running unit and a visualization unit. The application proposes a hydraulic fracturing problem finite element modeling system based on a large language model, and trains a large language model. The large language model can generate a key parameter file according to user input. The parameter grid coupling tool can generate a finite element model file according to the key parameter file and a grid file.

Description

Technical Field

[0001] The present invention relates to the field of numerical simulation, and in particular to a finite element modeling system and method for hydraulic fracturing problems based on a large language model. Background Art

[0002] Finite element analysis plays a vital role in hydraulic fracturing, primarily due to its ability to accurately simulate complex physical phenomena. This technology can meticulously depict the dynamic interactions between rock and fluid during the fracturing process, including fracture formation, expansion, and stress distribution. By creating high-resolution two- and three-dimensional models, finite element analysis helps engineers understand the evolution of fracture networks and their impact on production, thereby optimizing fracturing designs and increasing oil and gas recoveries. Furthermore, it can integrate field measurement data to enhance the reliability and accuracy of models, providing a scientific basis for decision-making. Furthermore, the visualization capabilities of the finite element method make complex processes more intuitive, helping to quickly identify potential problems. Finite element analysis is not only a fundamental tool for hydraulic fracturing research but also a crucial means of improving engineering efficiency and safety.

[0003] Existing technologies still have shortcomings in the automated generation of finite element models for hydraulic fracturing, mainly due to their limited intelligence level, which makes it difficult to effectively replace manual professional judgment. Finite element modeling involves complex geometric processing, meshing, material property settings, and boundary condition definitions, requiring in-depth professional knowledge and rich practical experience. Existing technologies are difficult to complete automatically through intelligent means. In addition, the quality and computational stability of automatically generated models often fail to meet the actual accuracy requirements of the project, and manual adjustment and optimization by professionals are still required. Therefore, the automated generation of finite element models still faces major challenges in terms of intelligence and accuracy assurance.

[0004] Large language models are capable of incremental learning, continuously improving modeling results by gradually updating data and knowledge, adapting to dynamically changing needs. Furthermore, large language models demonstrate strong data understanding and generation capabilities in automated modeling, automating complex modeling processes and significantly reducing manual intervention. Furthermore, large language models offer high computational efficiency and, with appropriate resource allocation, can rapidly generate the required models, significantly reducing time costs. Therefore, the use of large language model technology for automated modeling holds enormous potential and is expected to play a significant role in improving modeling efficiency and accuracy in the future.

[0005] The current technology has insufficient application scope in the automated generation of finite element models. For example, patent US10451520B2 (“Method and system for automated finite element model generation”) proposes a method for automatically generating finite element models through software tools. Although this method can achieve certain results in the modeling of certain standard geometric shapes and simple structures, its intelligence level is still limited when dealing with complex geometries, nonlinear material properties, and multi-physics field coupling problems. Specifically, the automated modeling process in this patent has high requirements for input data, and usually requires a lot of manual intervention to ensure the accuracy and stability of the model. Therefore, although the patent demonstrates certain automation potential, in actual engineering applications, it is still difficult to completely replace the experience and judgment of professionals, resulting in its adaptability and application scope being limited in a wider range of complex engineering scenarios. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the present invention provides a finite element modeling system and method for hydraulic fracturing problems based on a large language model.

[0007] The finite element modeling system for hydraulic fracturing problems based on a large language model in the present invention includes: a client, a cloud service platform and a computing server;

[0008] The client is composed of an interactive interface and a display interface; wherein,

[0009] The interactive interface includes a user input unit and a system output unit;

[0010] The display interface is a window for the system to display the visual finite element model and operation results;

[0011] The cloud service platform includes a storage space, a first input terminal, a second input terminal, a third input terminal, a transmission terminal, a trained large language model, a grid tool, and a parameter grid coupling tool;

[0012] The first input end is an input port for receiving external data input and user input and inputting into the large language model;

[0013] The second input terminal is an input port for receiving a key parameter file and inputting it into the grid tool;

[0014] The third input terminal is an input port for receiving key parameter files and grid files and inputting them into the parameter grid coupling tool;

[0015] The transmission end is a transmission port for transmitting the finite element model file in the storage space of the cloud service platform to the storage unit of the computing server;

[0016] The trained large language model is mainly a large language model that generates key parameter files based on user input content;

[0017] The grid tool is a tool that is called by the cloud service platform to generate a grid file based on the key parameter file;

[0018] The parameter grid coupling tool is a tool that is called by the cloud service platform to generate a finite element model file based on the key parameter file and the grid file;

[0019] The computing server includes a storage unit, an operation unit and a visualization unit.

[0020] The finite element modeling method for hydraulic fracturing problems based on a large language model in the present invention includes:

[0021] The user enters the key description of the finite element model they wish to generate in the user input unit. The API interface is consistent with the API interface of the trained large language model.

[0022] The cloud service platform's large language model processes the user's input description and generates a key parameter file. The mesh tool reads the key parameter file and generates a mesh file. The parameter mesh coupling tool processes the key parameter file and mesh file and generates a finite element model file.

[0023] The computing server runs the finite element model file, and the finite element model and the running results are presented using a visualization unit. The system output unit prompts the user that the modeling and running have been completed, and the visual finite element model and the running results are displayed on the display interface.

[0024] The user determines whether it meets expectations; if it does not meet expectations, the user needs to re-enter the relevant description; if it meets expectations, the user can choose to output the finite element model and running results.

[0025] Beneficial effects of the present invention:

[0026] This application proposes a finite element modeling system for hydraulic fracturing problems based on a large language model, and trains a large language model. The large language model can generate key parameter files based on user input, and the parameter grid coupling tool can generate finite element model files based on the key parameter files and grid files.

[0027] The computing server of the present application is capable of running the finite element modeling file generated by the large language model, allowing the user and the system to continuously interact until the ideal finite element model is obtained, thereby realizing efficient acquisition of the finite element model. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 System and file architecture diagrams;

[0029] Figure 2 Training large language model process diagram;

[0030] Figure 3 Training parameter grid coupling tool process diagram;

[0031] Figure 4 From user input content to show finite element model and running result process diagram;

[0032] Figure 5 Hydraulic fracturing problem sub-problem classification diagram;

[0033] Figure 6 Large language model training flowchart;

[0034] Figure 7 Key parameter file example diagram;

[0035] Figure 8 User modeling flowchart using system. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings.

[0037] The present application provides a hydraulic fracturing problem finite element modeling system based on a large language model, as shown in Figure 1 The system is composed of a client, a cloud service platform and a computing server;

[0038] The client is composed of an interactive interface and a display interface; the interactive interface includes a user input unit and a system output unit; the user input unit is a window for user to input content in the form of text, picture and vector diagram; the system output unit is a window for the system to prompt the user; the display interface is a window for the system to display visual finite element model and running result;

[0039] The cloud service platform includes a storage space, an input end 1, an input end 2, an input end 3, a transmission end, a trained large language model, a grid tool and a parameter grid coupling tool.

[0040] The storage space is a space for storing database generated by the trained large language model, key parameter file, grid file generated by the grid tool and finite element model file of the parameter grid coupling tool.

[0041] Further, the database stores all data generated in the training process of the large language model, in the form of "keyword-data value", which is convenient for the trained large language model to process user input content to generate key parameter file.

[0042] Furthermore, the key parameter file is the result generated by the trained large language model processing the user input content, and the parameter file format in the file is "keyword-data value".

[0043] Furthermore, the grid file is generated by the cloud service platform by calling the grid tool based on the key parameter file.

[0044] Furthermore, the finite element model file is generated by coupling the key parameter file and the mesh file through the parameter mesh coupling tool, and the finite element model file can be directly run by the running unit to generate the result.

[0045] The input terminal 1 is an input port for receiving external data input and user input and inputting into the large language model; the input terminal 2 is an input port for receiving key parameter files and inputting into the grid tool; the input terminal 3 is an input port for receiving key parameter files and grid files and inputting into the parameter grid coupling tool.

[0046] The transmission end is a transmission port for transmitting the finite element model file in the storage space of the cloud service platform to the storage unit of the computing server; the trained large language model is mainly a large language model that generates a key parameter file based on the user input content; the grid tool is a tool called by the cloud service platform to generate a grid file based on the key parameter file; the parameter grid coupling tool is a tool called by the cloud service platform to generate a finite element model file based on the key parameter file and the grid file.

[0047] The computing service platform includes a storage unit, an operation unit and a visualization unit; the storage unit is mainly a unit for storing finite element model files, and the finite element model files here are transmitted from the transmission end of the cloud service platform to the storage space; the operation unit is a unit for running the finite element model files; the visualization unit is a unit for visualizing the finite element model and the operation results.

[0048] like Figure 1 As shown, the present application provides a finite element modeling system for hydraulic fracturing problems based on a large language model, which mainly includes a two-step implementation route, numbered ① and ② respectively. The blue part is the system component, the yellow part is the file, and the purple part is the large language model that has completed training.

[0049] In one embodiment, the first step is to implement the route and train the large language model built into the system. The training process is mainly carried out on the cloud service platform: Figure 2 As shown in , a large amount of external data and human input content are first used as input to train the large language model. During the training process, the main goal is to generate a database and key parameter files in the form of "keyword-data value"; Figure 3As shown, the grid tool is called by the cloud service platform to generate a grid file based on the key parameter file generated by the trained large language model, and the parameter grid coupling tool is called by the cloud service platform to generate a finite element model file that can be run in the running unit based on the key parameter file and the grid file;

[0050] In an embodiment, the second step implements the route from user input to the computing server to the display interface to display the finite element model and the running result: as shown in Figure 4 As shown, input end 1 receives the content input by the user in the user input unit, and the trained large language model generates a key parameter file; input end 2 receives the key parameter file, and the grid tool generates a grid file; input end 3 receives the key parameter file and the grid file, and the parameter grid coupling tool generates a finite element model file; the finite element model file is transmitted by the transmission end to the storage unit of the computing server; the running unit runs the finite element model file, the visualization unit processes the finite element model and the running result, and the display interface displays them to the user.

[0051] As shown in Figure 5 As shown, the water conservancy fracturing problem in the present application includes many sub-problems, including crack propagation mechanism, proppant selection, induced earthquake, wellbore damage, fracturing fluid optimization, pressure control, three-dimensional fracturing network evolution, microseismic interpretation, fracturing effect evaluation, and backflow, etc. The subsequent sub-problem database will be mainly constructed according to the sub-problem classification.

[0052] As shown in Figure 6 As shown, the large language model training process of the hydraulic fracturing problem includes the following steps, and this example takes a type of sub-problem "crack propagation mechanism" in the hydraulic fracturing problem as an example for illustration:

[0053] A, data preparation:

[0054] A-1, data collection: select diversified data sources as external data, such as web crawlers, public data sets, digital books, etc., to ensure that the data volume is large enough and as much as possible is related to "crack propagation mechanism";

[0055] Further, it also includes that the relevant content input by humans also needs to be input as input content, so as to train a large language model capable of generating a key parameter file;

[0056] A-2, data cleaning: remove irrelevant information of "crack propagation mechanism" and remove duplicate content.

[0057] B, data preprocessing:

[0058] B-1, formatting: convert documents and text forms into a unified format readable by the large language model;

[0059] B-2. Construct a vocabulary: The vocabulary should include as many keywords as possible in the "crack propagation mechanism" problem, such as geometry keywords, material property keywords, initial condition keywords, boundary condition keywords, etc.

[0060] It further includes: B-3, word frequency filtering: some words expressing the same meaning are classified as the same keyword to reduce the vocabulary size; for example: "intersection angle" and "approach angle" are both classified as "intersection angle".

[0061] C. Training model and configuration settings:

[0062] C-1. Model Architecture: Choose an appropriate training model. You can use existing pre-trained models as a basis to reduce training time and resources.

[0063] C-2. Set hyperparameters: determine the learning rate and batch size, and set the effective number of training rounds.

[0064] D. Model training:

[0065] D-1. Training Objective: The goal is to generate a database and key parameter file in the form of "keyword-data value" for the "crack propagation mechanism" problem. The database contains all the keywords and related data values ​​for the "crack propagation mechanism" problem. The key parameter file contains the subproblem name, geometry keywords, attribute keywords, grid keywords, etc.

[0066] D-2. Evaluation and Adjustment: Manually evaluate the training results and adjust incorrect training results until the correct key parameter file is generated.

[0067] E. Model deployment:

[0068] E-1. Platform selection: The deployment platform is the cloud service platform;

[0069] E-2. API interface: Select the appropriate API interface to support real-time reasoning.

[0070] The key parameter file in this example includes the key parameters that describe a physical model. This article uses the "crack propagation mechanism" problem as an example to illustrate:

[0071] Human input: A linear elastic fracture mechanics two-dimensional model (LEFM) is established with a length of 100 m and a width of 50 m. The initial crack shape is rectangular and located on the left side. The material elastic modulus is 1.5 GPa, the Poisson's ratio is 0.18, the formation pressure is 9.81 MPa, and the fracture criterion based on the stress intensity factor is adopted.

[0072] Key parameter file: can be improved in combination with the database, including sub-problem name, geometry keywords, attribute keywords, grid keywords, etc. Figure 7 As shown:

[0073] Sub-problem name: “Crack propagation mechanism-1”;

[0074] Geometric keywords: "2D-1", "Length-100m", "Width-50m", "Rectangle-1", "Left-1";

[0075] Attribute keywords: "formation pressure - 9.81 MPa", "elastic modulus - 1.5 GPa", "Poisson's ratio - 0.18", "stress intensity factor - 1";

[0076] Mesh keywords: "triangular element-1" etc.

[0077] After the above steps, a database for the sub-problem "crack propagation mechanism" is obtained, which assists in generating the key parameter file next time; the large language model has been trained, and the content is input in the user input unit, and the result is the key parameter file; the database of other sub-problems of the hydraulic fracturing problem can also be created using this process, and the large language model of other sub-problems can also be trained using this process.

[0078] Furthermore, the parameter mesh coupling tool generates a finite element model file based on the coupling of the key parameter file and the mesh file. The finite element model file includes multiple parts: control statements, nodes, element definitions, initial values, boundary conditions, material definitions, working conditions, time steps, etc.

[0079] like Figure 8 As shown in the figure, users can use this system to perform finite element modeling of hydraulic fracturing problems, including the following steps:

[0080] Step 1: The user enters the key description of the finite element model that he wants to generate in the user input unit. The API interface is consistent with the API interface of the trained large language model.

[0081] Step 2: The cloud service platform's large language model processes the user's input description and generates a key parameter file. The mesh tool reads the key parameter file and generates a mesh file. The parameter mesh coupling tool processes the key parameter file and the mesh file and generates a finite element model file.

[0082] The computing server runs the finite element model file, and the finite element model and the running results are presented using a visualization unit. The system output unit prompts the user that the modeling and running have been completed, and the visual finite element model and the running results are displayed on the display interface.

[0083] Step 3: The user determines whether the result meets expectations. If not, the user needs to re-enter the relevant description. If it meets expectations, the user can choose to output the finite element model and running results.

[0084] The contents described in the embodiments of this specification are merely an enumeration of the implementation forms of the inventive concept. The scope of protection of the present invention should not be regarded as limited to the specific forms described in the embodiments. The scope of protection of the present invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A finite element modeling system for hydraulic fracturing problems based on a large language model, comprising a client, a cloud service platform, and a computing server, characterized in that: The client is composed of an interactive interface and a display interface; wherein, The interactive interface includes a user input unit and a system output unit; The display interface is a window for the system to display the visual finite element model and operation results; The cloud service platform includes a storage space, a first input terminal, a second input terminal, a third input terminal, a transmission terminal, a trained large language model, a grid tool, and a parameter grid coupling tool; The first input end is an input port for receiving external data input and user input and inputting into the large language model; The second input terminal is an input port for receiving a key parameter file and inputting it into the grid tool; The third input terminal is an input port for receiving key parameter files and grid files and inputting them into the parameter grid coupling tool; The transmission end is a transmission port for transmitting the finite element model file in the storage space of the cloud service platform to the storage unit of the computing server; The trained large language model generates a large language model of a key parameter file according to user input content; The grid tool is a tool that is called by the cloud service platform to generate a grid file based on the key parameter file; The parameter grid coupling tool is a tool that is called by the cloud service platform to generate a finite element model file based on the key parameter file and the grid file; The computing server includes a storage unit, an operation unit and a visualization unit; The hydraulic fracturing problem includes fracture propagation mechanism, proppant selection, induced seismicity, wellbore damage, fracturing fluid optimization, pressure control, three-dimensional fracturing network evolution, microseismic interpretation, fracturing effect evaluation, and backflow. X is used to represent one of the problems. Problem X is trained by the large language model. The large language model training goal is to generate a database and key parameter file in the form of "keyword-data value" in problem X.

2. The finite element modeling system for hydraulic fracturing problems based on a large language model according to claim 1, characterized in that: The user input unit is a window for users to input content in a composite form of text, pictures, and vector graphics; The system output unit is a window for the system to prompt the user.

3. The finite element modeling system for hydraulic fracturing problems based on a large language model according to claim 1, characterized in that: The storage space is a space for storing the database and key parameter files generated by the large language model that has completed training, the grid files generated by the grid tool, and the finite element model files of the parameter grid coupling tool.

4. The finite element modeling system for hydraulic fracturing problems based on a large language model according to claim 3, characterized in that: The database stores all data that appears during the training of the large language model in the form of "keyword-data value"; The key parameter file is the result of a trained large language model processing user input content. The parameter file format in the file is "keyword-data value"; The grid file is generated by the cloud service platform calling the grid tool according to the key parameter file; The finite element model file is generated by coupling the key parameter file and the mesh file with the parameter mesh coupling tool. The finite element model file can be directly run by the running unit to generate the result.

5. The finite element modeling system for hydraulic fracturing problems based on a large language model according to claim 1, characterized in that: The storage unit stores a unit of the finite element model file; The running unit is a unit for running the finite element model file; The visualization unit is a unit for visualizing the finite element model and the operation results.

6. A finite element modeling method for hydraulic fracturing problems based on a large language model, using the system according to any one of claims 1 to 5, characterized in that The method comprises the following steps: The user enters the key description of the finite element model they wish to generate in the user input unit. The API interface is consistent with the API interface of the trained large language model. The cloud service platform's large language model processes the user's input description and generates a key parameter file. The grid tool reads the key parameter file and generates a grid file. The parameter mesh coupling tool processes the key parameter file and mesh file to generate the finite element model file; The computing server runs the finite element model file, and the finite element model and the running results are presented using a visualization unit. The system output unit prompts the user that the modeling and running have been completed, and the visual finite element model and the running results are displayed on the display interface. User judgment meets expectations; If it does not meet the user's expectations, the user needs to re-enter the relevant description; if it meets the expectations, the user can choose to output the finite element model and running results.

7. The finite element modeling method for hydraulic fracturing problems based on a large language model according to claim 6, characterized in that: The hydraulic fracturing problem includes fracture propagation mechanism, proppant selection, induced seismicity, wellbore damage, fracturing fluid optimization, pressure control, three-dimensional fracturing network evolution, microseismic interpretation, fracturing effect evaluation and backflow. X is used to represent one of the problems. The large language model training process of problem X is: A. Data preparation: A-1. Data collection: Select diverse data sources as external data and ensure that the data volume is large enough; A-2. Data cleaning: remove information irrelevant to question X and remove duplicate content; B. Data preprocessing: B-1. Formatting: Convert documents and text into a unified format readable by large language models; B-2. Build a vocabulary: The vocabulary should include as many keywords as possible from Question X. C. Training model and configuration settings: C-1. Model Architecture: Select a training model. You can use an existing pre-trained model as a basis to reduce training time and resources. C-2. Set hyperparameters: determine the learning rate and batch size, and set the effective number of training rounds; D. Model training: D-1. Training Objective: The goal is to generate a database and key parameter file in the form of "keyword-data value" for problem X. The database contains all the keywords and related data values ​​for problem X. The key parameter file contains the problem name, geometry keywords, attribute keywords, and mesh keywords. D-2. Evaluation and Adjustment: Evaluate the training results and adjust incorrect training results until the correct key parameter file is generated; E. Model deployment: E-1. Platform selection: The deployment platform is the cloud service platform; E-2, API interface: Select the API interface to support real-time reasoning.

8. The finite element modeling method for hydraulic fracturing problems based on a large language model according to claim 7, characterized in that: It also includes B-3, word frequency filtering: some words expressing the same meaning are grouped as the same keyword to reduce the size of the vocabulary.

9. The finite element modeling method for hydraulic fracturing problems based on a large language model according to claim 7, characterized in that: The parameter grid coupling tool generates a finite element model file according to the coupling of the key parameter file and the grid file. The finite element model file includes control statements, nodes, element definitions, initial values, boundary conditions, material definitions, working conditions and time steps.

Images