Porous sandstone fault zone closure evaluation method and related equipment

Abstract

The invention discloses a porosity sandstone fault zone closure evaluation method and related equipment, belongs to the technical field of geology, and realizes digitization and refinement of an evaluation process through detailed geological exploration and sampling data acquisition in combination with classified arrangement of physical parameter data and multi-parameter model construction. The method not only improves the accuracy of evaluation, but also enhances the comprehensiveness of evaluation by comprehensively considering a plurality of influence factors. A three-dimensional digital model is used for physical parameter simulation calculation, and visual display and efficient analysis of evaluation results are achieved. The method not only improves the evaluation efficiency, but also enables the evaluation result to have a more scientific basis, and provides powerful support for engineering practices such as oil-gas exploration and underground water development. Through rapid iteration and optimization, the method can flexibly meet the requirements of closure evaluation under different geological conditions, reduces the engineering risk, and guarantees the practicality and reliability of the evaluation result.

Description

Technical Field

[0001] This invention belongs to the field of geological technology, specifically a method and related equipment for evaluating the sealing of porous sandstone fault zones. Background Technology

[0002] Faults are three-dimensional geological bodies with complex internal structures, playing a crucial role in the migration, accumulation, and formation of oil and gas reservoirs. This role is primarily manifested in two aspects: conduit function and sealing function. The internal structure of the fault zone is key to determining its conduction and sealing capabilities. The sealing evaluation of porous sandstone fault zones refers to assessing the sealing performance of fault zones formed in oil-bearing porous sandstone under tectonic movements to determine their effectiveness as reservoirs. Currently used methods for evaluating the sealing performance of porous sandstone fault zones have the following drawbacks: 1. Difficulty in data acquisition: Evaluating the sealing performance requires obtaining a large amount of physical property data, including porosity, permeability, and geostress. However, these data are costly to obtain and are often difficult to acquire in actual sites, thus affecting the accuracy of the evaluation.

[0003] 2. Limited by single physical property parameters; most current sealing evaluation methods only consider a single physical property parameter, such as porosity or permeability. However, these parameters are actually affected by a variety of factors, and there is no comprehensive index that can fully reflect the sealing of porous sandstone fault zones.

[0004] 3. Lack of reliable model support; most existing closure evaluation methods are based on empirical formulas or simplified models, which have limited applicability and accuracy under different conditions, and require more reliable model support.

[0005] 4. Lack of specificity: Different types of porous sandstone fault zones have different characteristics and sealing properties, but current evaluation methods lack specificity and cannot effectively evaluate different types of fault zones. Summary of the Invention

[0006] This invention provides a method and related equipment for evaluating the sealing of porous sandstone fault zones, which solves the problems of existing methods for evaluating the sealing of porous sandstone fault zones being limited by a single physical property parameter and lacking reliable model support.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for evaluating the sealing properties of porous sandstone fault zones includes: Geological exploration and sampling data of the study area were acquired and preprocessed, and physical parameter data were obtained based on the geological exploration and sampling data; Classify and organize physical parameter data and establish classification standards; Establish a multi-parameter model based on physical parameter data; Optimize and adjust the multi-parameter model to generate a three-dimensional digital model; The calculation results are obtained based on the three-dimensional digital model and physical parameter simulation calculation. The calculation results are analyzed based on classification criteria, and the sealing properties of porous sandstone fault zones are evaluated based on the analysis results.

[0008] Preferably, the multi-parameter model includes a three-dimensional geological model and an inversion physical model.

[0009] Preferably, the steps for establishing a three-dimensional geological model are as follows: The physical parameter data is retrieved, imported, cleaned and processed in one go, a stratigraphic model is built and a fault model is added, and finally a three-dimensional geological model is built.

[0010] Preferably, the inversion physical model is obtained by combining a three-dimensional geological model and an actual physical model, and the inversion physical model includes a seepage model and a geostress model.

[0011] Preferably, the seepage model and the geostress model are obtained using the finite element method and numerical simulation techniques.

[0012] Preferably, the preprocessing of geological exploration and sampling data includes denoising, smoothing, alignment, and registration.

[0013] Preferably, the specific steps for performing data analysis on the calculation results based on classification criteria and evaluating the sealing properties of porous sandstone fault zones based on the analysis results are as follows: Use the LinearRegression class from the Scikit-learn library to build a linear regression model, and use the fit() function to train the model. After the model is trained, use the predict() function to predict new data to be predicted and print the prediction results.

[0014] A system for evaluating the sealing properties of porous sandstone fault zones includes: Data acquisition module: used to acquire and preprocess geological exploration and sampling data of the study area, and to obtain physical parameter data based on the geological exploration and sampling data; Classification module: Used to classify and organize physical parameter data and establish classification criteria; Modeling module: Used to build multi-parameter models based on physical parameter data; Model generation module: used to optimize and adjust multi-parameter models to generate 3D digital models; Calculation module: used to perform physical parameter simulation calculations based on a 3D digital model and obtain calculation results; Evaluation module: Used to perform data analysis on the calculation results based on classification criteria, and to evaluate the sealing of porous sandstone fault zones based on the analysis results.

[0015] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of a method for evaluating the sealing of porous sandstone fault zones.

[0016] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for evaluating the sealing of porous sandstone fault zones.

[0017] Compared with existing technologies, this invention offers the following advantages: It provides a method for evaluating the sealing of porous sandstone fault zones. Through detailed geological exploration and sampling data acquisition, combined with the classification and organization of physical parameter data and the construction of multi-parameter models, the evaluation process is digitized and refined. This method not only improves the accuracy of the evaluation but also enhances its comprehensiveness by considering multiple influencing factors. The use of a three-dimensional digital model for physical parameter simulation calculation enables intuitive display and efficient analysis of the evaluation results. This method not only improves evaluation efficiency but also makes the evaluation results more scientifically grounded, providing strong support for engineering practices such as oil and gas exploration and groundwater development. Through rapid iteration and optimization, this method can flexibly address the sealing evaluation needs under different geological conditions, reduce engineering risks, ensure the practicality and reliability of the evaluation results, and provide strong technical support for geological research and engineering practice. Attached Figure Description

[0018] Figure 1 This is a flowchart of a method for evaluating the sealing of porous sandstone fracture zones according to the present invention; Figure 2 This is a flowchart illustrating a method for evaluating the sealing properties of porous sandstone fault zones according to an embodiment of the present invention. Figure 3 This is a block diagram of a porous sandstone fault zone sealing evaluation system according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0023] like Figure 1 As shown, this invention provides a method for evaluating the sealing properties of porous sandstone fault zones, comprising: S101 acquires and preprocesses geological exploration and sampling data of the study area, and obtains physical parameter data based on the geological exploration and sampling data; S102 classifies and organizes physical parameter data and establishes classification standards; S103 establishes a multi-parameter model based on physical parameter data; S104 optimizes and adjusts the multi-parameter model to generate a three-dimensional digital model; S105 obtains calculation results based on a three-dimensional digital model and physical parameter simulation calculations. S106 performs data analysis on the calculation results based on the classification criteria, and evaluates the sealing of the porous sandstone fault zone based on the analysis results.

[0024] Multi-parameter models include three-dimensional geological models and inversion physical models. This design can more accurately describe geological structures and physical properties, thereby improving the accuracy and practicality of the evaluation.

[0025] The specific steps for establishing a three-dimensional geological model are as follows: The physical parameter data was retrieved, imported, cleaned and processed in one go, stratigraphic models were built and fault models were added, and finally a three-dimensional geological model was built. These steps ensured the accuracy and reliability of the model and provided a solid foundation for subsequent evaluation work.

[0026] The inversion physical model is obtained by combining a three-dimensional geological model and an actual physical model. The inversion physical model includes a seepage model and a geostress model, which helps to understand the influence of geological structure on physical properties more deeply, thereby improving the accuracy of the evaluation.

[0027] The seepage model and geostress model are obtained through the finite element method and numerical simulation techniques. These methods can efficiently handle complex geological and physical problems and improve the efficiency and accuracy of the evaluation.

[0028] Preprocessing of geological exploration and sampling data includes denoising, smoothing, alignment, and registration. These processes can reduce data errors, improve data accuracy and reliability, and provide high-quality input for subsequent evaluation work.

[0029] The specific steps for evaluating the sealing properties of porous sandstone fault zones based on the analysis results, using classification criteria and calculation results as a basis, are as follows: Use the LinearRegression class from the Scikit-learn library to build a linear regression model, and use the fit() function to train the model. After the model is trained, use the predict() function to predict new data to be predicted and print the prediction results.

[0030] This method can process large amounts of data quickly and accurately, and evaluate the sealing properties of porous sandstone fault zones based on the analysis results, thus improving the efficiency and accuracy of the evaluation. At the same time, its predictive capabilities provide strong support for future geological research and engineering practices.

[0031] like Figure 2 Another embodiment of the present invention provides a method for evaluating the sealing of porous sandstone fault zones, comprising: Step S1, data acquisition, involves collecting geological exploration and sampling data from the study area, including well logging data, core data, and outcrop profile data. Geophysical exploration, seismic imaging, field observation, and sampling are also required. Field observation includes visual information such as geological structure, rock type, fault zone distribution, and fracture characteristics. Sampling refers to collecting rock samples for laboratory testing to obtain physical parameters such as porosity, permeability, and geostress. This process acquires physical parameter data under actual conditions through laboratory and field testing, obtaining multiple physical property parameters such as porosity, permeability, and geostress, and establishing a corresponding three-dimensional database. Step S2, Feature Classification: Based on the characteristics of various porous sandstone fault zones in the study area collected in Step S1, as well as geophysical exploration, field observation and sampling, classify and organize the physical parameter data under actual conditions, and establish corresponding classification standards so that targeted analysis and evaluation can be carried out when evaluating the closure. Step S3: Establish a parametric model. Establish a multi-parameter model by comprehensively calculating multiple physical property parameters such as porosity, permeability, and geostress to obtain an index that fully reflects the sealing of the fault zone. The establishment of the multi-parameter model includes the following three-dimensional geological model and inversion physical model. By establishing a comprehensive multi-parameter model to fully assess the sealing of porous sandstone fault zones, this method surpasses the limitations of traditional single physical property parameters.

[0032] First, using geological modeling software, the collected geological exploration data, sampling data, and data obtained from geophysical exploration, field observation, and sampling were processed to construct a detailed three-dimensional geological model. This model not only covers the distribution and thickness of strata but also includes detailed information about faults. Next, based on this three-dimensional geological model, an inversion physical model was further established, including a seepage model and a geostress model. These two models respectively consider the distribution and variation of porosity, permeability, and geostress. Using the finite element method, the flow behavior of fluids in the fault zone and the influence of geostress on the fault zone were simulated. Finally, the above models were combined for comprehensive calculations to derive a series of indicators that comprehensively reflect the sealing of the fault zone, including permeability distribution, fluid flow paths, and stress concentration areas.

[0033] Step S4, optimization and adjustment, combines laboratory testing with field testing. Based on the basic data obtained from laboratory testing (physical parameters such as porosity, permeability, and geostress), the multi-parameter model is corrected and improved through field testing, thereby optimizing and adjusting the multi-parameter model and generating a complete three-dimensional digital model. Step S5, simulation calculation: On the established three-dimensional geological model, perform simulation calculations of relevant parameters, including the solution of permeability, porosity, and seepage velocity indices. Common calculation software includes Petrel and ECLIPSE. Step S6, data analysis: Using artificial intelligence technologies, such as machine learning and artificial neural networks, to perform deep learning and analysis on a large amount of data, it is possible to better predict the sealing performance of porous sandstone fault zones and improve the accuracy of the evaluation. A linear regression model implemented in Python: import pandas as pd from sklearn.linear_model import LinearRegression # Read data data = pd.read_csv('data.csv') X = data.iloc[:, :-1] # Physical property parameters y = data.iloc[:, -1] # Closure index # Establish a linear regression model model = LinearRegression() model.fit(X, y) # Predicting Closure Indicators new_X = [[0.2, 100, 30], [0.3, 150, 40]] # Physical property parameters to be predicted new_y = model.predict(new_X) # Prediction result print(new_y); The formula for the linear regression model is: y = β0 + β1x1 + β2x2 + … + βn*xn + ε Where y represents the dependent variable, x1, x2, …, xn are the independent variables, β0, β1, β2, …, βn are the model parameters, and ε is the error term.

[0034] In the sample data, we obtained n sets of data through observation and measurement of x and y, namely (x1, y1), (x2, y2), …, (xn, yn). Then we can use the least squares method to estimate the parameter values ​​in the model, that is, minimize the sum of squared residuals. Specifically, we need to find a set of parameters β0, β1, β2, …, βn such that the sum of the squares of the differences between the predicted and true values ​​for each sample point is minimized. This can be achieved by fitting an optimal straight line; When there is only one independent variable, we call it a simple linear regression model. In this case, the formula of the model can be simplified to: y = β0 + β1*x + ε Where y represents the dependent variable, x represents the independent variable, β0 and β1 are the intercept and slope, respectively, and ε is the error term.

[0035] The geological exploration data, sampling data, and data obtained through geophysical exploration, field observation and sampling collected in the study area were uniformly preprocessed, such as denoising, smoothing, alignment and registration. In step S3, the three-dimensional geological model is created by converting the collected data into a three-dimensional geological model using CAD software or professional geological modeling software. The specific operations include data retrieval, data import, data cleaning and processing, establishing a stratigraphic model, adding fault models, and establishing a three-dimensional geological model. In step S3, the physical model inversion is to combine the established geological model with the physical model to invert the physical model, including the seepage model and the geostress model. This process requires the use of the finite element method and numerical simulation technology for solving. In step S6, a linear regression model is built using the LinearRegression class in the Scikit-learn library, and the fit() function is used to train the model. Then, after the model training is completed, the predict() function is used to predict the new data to be predicted and the prediction results are printed out. The general form of the fit() function is: model.fit(x, y, batch_size=32, epochs=10, validation_data=(val_x,val_y)) Where x and y represent the training set data and the corresponding labels, respectively; batch_size represents the number of samples used in each iteration, which defaults to 32; epochs represents the number of iterations, which defaults to 10; and validation_data represents the validation set data and the corresponding labels. The general form of the predict() function is: model.predict(x, batch_size=None, verbose=0) Where x is the input data, which can be a single sample or a batch of samples; batch_size represents the number of samples used in each prediction (optional parameter); verbose indicates whether to display detailed information, 0 means no display, 1 means display progress bar information; When the predict() function is executed, the model uses the current weights and biases to infer the input data and output the prediction results. It should be noted that the predict() function can only be used for a model that has already been trained. If you want to retrain the model, you need to call the fit() function again. During the execution of the `fit()` function, the model iteratively adjusts its weights and biases based on the given training set data and calculates the error loss value. Simultaneously, it can also use validation set data to test the model's performance on unseen data and avoid overfitting.

[0036] like Figure 3 As shown, another embodiment of the present invention provides a system for evaluating the sealing of porous sandstone fault zones, comprising: Data acquisition module: used to acquire and preprocess geological exploration and sampling data of the study area, and to obtain physical parameter data based on the geological exploration and sampling data; Classification module: Used to classify and organize physical parameter data and establish classification criteria; Modeling module: Used to build multi-parameter models based on physical parameter data; Model generation module: used to optimize and adjust multi-parameter models to generate 3D digital models; Calculation module: used to perform physical parameter simulation calculations based on a 3D digital model and obtain calculation results; Evaluation module: Used to perform data analysis on the calculation results based on classification criteria, and to evaluate the sealing of porous sandstone fault zones based on the analysis results.

[0037] The multi-parameter model includes a three-dimensional geological model and an inversion physical model.

[0038] The specific steps for establishing a three-dimensional geological model are as follows: The physical parameter data is retrieved, imported, cleaned and processed in one go, a stratigraphic model is built and a fault model is added, and finally a three-dimensional geological model is built.

[0039] The inversion physical model is obtained by combining a three-dimensional geological model and an actual physical model. The inversion physical model includes a seepage model and a geostress model.

[0040] The seepage model and geostress model were obtained using the finite element method and numerical simulation techniques.

[0041] Preprocessing of geological exploration and sampling data includes denoising, smoothing, alignment, and registration.

[0042] The specific steps for evaluating the sealing properties of porous sandstone fault zones based on the analysis results, using classification criteria and calculation results as a basis, are as follows: Use the LinearRegression class from the Scikit-learn library to build a linear regression model, and use the fit() function to train the model. After the model is trained, use the predict() function to predict new data to be predicted and print the prediction results.

[0043] An embodiment of the present invention provides a terminal device. This terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0044] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0045] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0046] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0047] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0048] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0049] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art, guided by the specification, can make many other modifications without departing from the scope of the claims of the present invention, and all of these modifications are within the scope of protection of the present invention.

Claims

1. A method for evaluating the sealing properties of porous sandstone fault zones, characterized in that, include: Geological exploration and sampling data of the study area were acquired and preprocessed to obtain preprocessed geological exploration and sampling data; Physical parameter data are obtained from preprocessed geological exploration and sampling data; Classify and organize physical parameter data and establish classification standards; Establish a multi-parameter model based on physical parameter data; Optimize and adjust the multi-parameter model to generate a three-dimensional digital model; The calculation results are obtained based on the simulation calculation using a three-dimensional digital model and physical parameters. The calculation results are analyzed based on classification criteria, and the sealing properties of porous sandstone fault zones are evaluated based on the analysis results.

2. The method for evaluating the sealing of porous sandstone fault zones according to claim 1, characterized in that, The multi-parameter model includes a three-dimensional geological model and an inversion physical model.

3. The method for evaluating the sealing of porous sandstone fault zones according to claim 2, characterized in that, The specific steps for establishing a three-dimensional geological model are as follows: The physical parameter data is retrieved, imported, cleaned and processed in sequence, stratigraphic models are established and fault models are added, and finally a three-dimensional geological model is established.

4. The method for evaluating the sealing of porous sandstone fault zones according to claim 3, characterized in that, The inversion physical model is obtained by combining a three-dimensional geological model and an actual physical model. The inversion physical model includes a seepage model and a geostress model.

5. The method for evaluating the sealing of porous sandstone fault zones according to claim 4, characterized in that, The seepage model and geostress model were obtained using the finite element method and numerical simulation techniques.

6. The method for evaluating the sealing of porous sandstone fault zones according to claim 1, characterized in that, Preprocessing of geological exploration and sampling data includes denoising, smoothing, alignment, and registration.

7. The method for evaluating the sealing of porous sandstone fault zones according to claim 1, characterized in that, The specific steps for evaluating the sealing properties of porous sandstone fault zones based on the analysis results, using classification criteria and calculation results as a basis, are as follows: Use the LinearRegression class from the Scikit-learn library to build a linear regression model, and use the fit() function to train the model. After the model is trained, use the predict() function to predict new data to be predicted and print the prediction results.

8. A system for evaluating the sealing of porous sandstone fault zones, characterized in that, include: Data acquisition module: used to acquire and preprocess geological exploration and sampling data of the study area, and to obtain physical parameter data based on the geological exploration and sampling data; Classification module: Used to classify and organize physical parameter data and establish classification criteria; Modeling module: Used to build multi-parameter models based on physical parameter data; Model generation module: used to optimize and adjust multi-parameter models to generate 3D digital models; Calculation module: used to perform physical parameter simulation calculations based on a 3D digital model and obtain calculation results; Evaluation module: Used to perform data analysis on the calculation results based on classification criteria, and to evaluate the sealing of porous sandstone fault zones based on the analysis results.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for evaluating the sealing of porous sandstone fault zones as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for evaluating the sealing of porous sandstone fault zones as described in any one of claims 1 to 7.

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