A method, apparatus, equipment and medium for evaluating reservoir pore structure

Abstract

This application discloses a method, apparatus, equipment, and medium for evaluating reservoir porosity structure, relating to the field of oil and gas exploration and development. The method includes acquiring nuclear magnetic resonance (NMR) echo data; performing multifractal detrended fluctuation analysis on the NMR echo data to obtain multifractal detrended fluctuation analysis parameters; selecting target multifractal detrended fluctuation analysis parameters from the multifractal detrended fluctuation analysis parameters; and evaluating the reservoir porosity structure based on the target multifractal detrended fluctuation analysis parameters. Through the above technical solution of this application, NMR echo data can be directly processed, avoiding uncertainties in the inversion process, thereby improving the accuracy of reservoir porosity structure evaluation.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development, and in particular to a method, apparatus, equipment and medium for evaluating reservoir pore structure. Background Technology

[0002] In recent years, among existing technologies, those based on nuclear magnetic resonance (NMR) have been developing rapidly. Spectroscopic methods for evaluating reservoir pore structure have been widely developed, but nuclear magnetic resonance (NMR) remains a key method. The spectrum cannot be directly measured and needs to be obtained through inversion from nuclear magnetic resonance echo data. Under low signal-to-noise ratio conditions, the inversion process contains significant errors, affecting the underlying... A method for evaluating reservoir pore structure based on spectra.

[0003] As can be seen from the above, how to improve the accuracy of reservoir pore structure evaluation by directly processing nuclear magnetic resonance echo data and avoiding uncertainties in the inversion process is a problem to be solved in this field. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, device, and medium for evaluating reservoir porosity structure, which can improve the accuracy of reservoir porosity structure evaluation by directly processing nuclear magnetic resonance echo data and avoiding uncertainties in the inversion process. The specific solution is as follows:

[0005] In a first aspect, this application discloses a method for evaluating reservoir pore structure, including:

[0006] Acquire nuclear magnetic resonance echo data;

[0007] The nuclear magnetic resonance echo data is subjected to multifractal detrending fluctuation analysis to obtain multifractal detrending fluctuation analysis parameters.

[0008] Select the target multifractal detrending fluctuation analysis parameters from the multifractal detrending fluctuation analysis parameters;

[0009] The reservoir pore structure is evaluated based on the target multifractal detrended fluctuation analysis parameters.

[0010] Optionally, the step of performing multifractal detrending fluctuation analysis on the nuclear magnetic resonance echo data to obtain multifractal detrending fluctuation analysis parameters includes:

[0011] The nuclear magnetic resonance echo data were processed using a multifractal-detrended fluctuation analysis method to obtain multifractal detrended fluctuation analysis parameters.

[0012] Optionally, the multifractal detrending fluctuation analysis parameters include the generalized Hearst exponent at the maximum moment, the generalized Hearst exponent at the minimum moment, the difference between the generalized Hearst exponent at the maximum moment and the generalized Hearst exponent at the minimum moment, the singular value exponent at the maximum moment, the singular value exponent at the minimum moment, the difference between the singular value exponent at the maximum moment and the singular value exponent at the minimum moment, and the Hearst exponent.

[0013] Optionally, the step of selecting the target multifractal detrending fluctuation analysis parameters from the multifractal detrending fluctuation analysis parameters includes:

[0014] Based on the multifractal detrending fluctuation analysis parameters and the preset pore structure characteristic parameters, the target multifractal detrending fluctuation analysis parameters are selected from all the multifractal detrending fluctuation analysis parameters.

[0015] Optionally, the step of selecting target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the multifractal detrending fluctuation analysis parameters and preset pore structure characteristic parameters includes:

[0016] The correlation between the multifractal detrending fluctuation analysis parameters and the preset pore structure characteristic parameters is calculated;

[0017] Based on the correlation, the target multifractal detrending fluctuation analysis parameter is selected from all the multifractal detrending fluctuation analysis parameters.

[0018] Optionally, selecting target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the correlation includes:

[0019] Based on the correlation, all the multifractal detrending fluctuation analysis parameters are sorted to obtain the sorted multifractal detrending fluctuation analysis parameters;

[0020] The number of target parameters is determined based on business needs, and the target multifractal detrending fluctuation analysis parameters are selected from the sorted multifractal detrending fluctuation analysis parameters according to the number of parameters.

[0021] Optionally, after evaluating the reservoir pore structure based on the target multifractal detrending fluctuation analysis parameters, the method further includes:

[0022] Obtain evaluation results;

[0023] Based on the evaluation results and the target multifractal detrending fluctuation analysis parameters, the evaluation effect diagrams of the pore structure of each reservoir are drawn.

[0024] Secondly, this application discloses a reservoir pore structure evaluation device, comprising:

[0025] The data acquisition module is used to acquire nuclear magnetic resonance echo data;

[0026] The analysis and processing module is used to perform multifractal detrending fluctuation analysis on the nuclear magnetic resonance echo data to obtain multifractal detrending fluctuation analysis parameters.

[0027] The parameter filtering module is used to filter out target multifractal detrending fluctuation analysis parameters from the multifractal detrending fluctuation analysis parameters.

[0028] The reservoir pore structure evaluation module is used to evaluate the reservoir pore structure based on the target multifractal detrended fluctuation analysis parameters.

[0029] Thirdly, this application discloses an electronic device, including:

[0030] Memory, used to store computer programs;

[0031] A processor is used to execute the computer program to implement the aforementioned reservoir pore structure evaluation method.

[0032] Fourthly, this application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed reservoir pore structure evaluation method.

[0033] As can be seen, this application provides a method for evaluating reservoir porosity structure, including acquiring nuclear magnetic resonance (NMR) echo data; performing multifractal detrended fluctuation analysis (MF-DFA) on the NMR echo data to obtain MF-DFA parameters; selecting target MF-DFA parameters from the MF-DFA parameters; and evaluating the reservoir porosity structure based on the target MF-DFA parameters. This application utilizes the MF-DFA method for evaluating reservoir porosity structure by processing NMR echo data to obtain MF-DFA parameters, and evaluating the reservoir porosity structure based on these MF-DFA parameters. This method can directly process NMR echo data, avoid the uncertainties of the inversion process, and thus improve the accuracy of reservoir porosity structure evaluation. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0035] Figure 1 This is a flowchart of a reservoir pore structure evaluation method disclosed in this application;

[0036] Figure 2 This is a flowchart of a reservoir pore structure evaluation method disclosed in this application;

[0037] Figure 3 This is a diagram of the core experimental analysis results disclosed in this application;

[0038] Figure 4 This application discloses a generalized Hearst index and a singular spectrum.

[0039] Figure 5 This is an image illustrating the evaluation effect of reservoir pore structure as disclosed in this application;

[0040] Figure 6 This is a schematic diagram of a reservoir pore structure evaluation device disclosed in this application.

[0041] Figure 7 This application provides a structural diagram of an electronic device. Detailed Implementation

[0042] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] In recent years, among existing technologies, those based on nuclear magnetic resonance (NMR) have been developing rapidly. Spectroscopic methods for evaluating reservoir pore structure have been widely developed, but nuclear magnetic resonance (NMR) remains a key method. The spectrum cannot be directly measured and needs to be obtained through inversion from nuclear magnetic resonance echo data. Under low signal-to-noise ratio conditions, the inversion process contains significant errors, affecting the underlying... A method for evaluating reservoir porosity structure based on nuclear magnetic resonance (NMR) spectra. As can be seen above, how to improve the accuracy of reservoir porosity structure evaluation by directly processing NMR echo data and avoiding uncertainties in the inversion process is a problem that needs to be solved in this field.

[0044] See Figure 1As shown in the figure, an embodiment of the present invention discloses a method for evaluating reservoir pore structure, which may specifically include:

[0045] Step S11: Acquire nuclear magnetic resonance echo data.

[0046] Step S12: Perform multifractal detrending fluctuation analysis on the nuclear magnetic resonance echo data to obtain multifractal detrending fluctuation analysis parameters.

[0047] In this embodiment, the NMR echo data is processed using a multifractal-detrended fluctuation analysis method to obtain multifractal detrended fluctuation analysis parameters. These parameters include the generalized Hearst exponent at maximum moment, the generalized Hearst exponent at minimum moment, the difference between the generalized Hearst exponent at maximum moment and the generalized Hearst exponent at minimum moment, the singular value exponent at maximum moment, the singular value exponent at minimum moment, the difference between the singular value exponent at maximum moment and the singular value exponent at minimum moment, and the Hearst exponent.

[0048] Furthermore, the MF-DFA parameters (i.e., multifractal detrending volatility analysis parameters) in this application include, but are not limited to, the generalized Hurst exponent when the moment q is at its maximum. The generalized Hurst exponent that minimizes the first moment q , and difference Singular value exponent when the first moment q is maximum The singular value exponent when the moment q is minimized , and difference The Hurst exponent H, specifically.

[0049] Step S13: Select the target multifractal detrending fluctuation analysis parameters from the multifractal detrending fluctuation analysis parameters.

[0050] Step S14: Evaluate the reservoir pore structure based on the target multifractal detrending fluctuation analysis parameters.

[0051] In this embodiment, nuclear magnetic resonance (NMR) echo data is acquired; multifractal detrended fluctuation analysis (MF-DFA) is performed on the NMR echo data to obtain MF-DFA parameters; target MF-DFA parameters are selected from the MF-DFA parameters; and the reservoir porosity structure is evaluated based on the target MF-DFA parameters. This application utilizes the MF-DFA method for reservoir porosity structure evaluation to process NMR echo data, obtain MF-DFA parameters, and evaluate reservoir porosity structure based on MF-DFA parameters. This method directly processes NMR echo data, avoids uncertainties in the inversion process, and thus improves the accuracy of reservoir porosity structure evaluation.

[0052] See Figure 2 As shown in the figure, an embodiment of the present invention discloses a method for evaluating reservoir pore structure, which may specifically include:

[0053] Step S21: Acquire nuclear magnetic resonance echo data.

[0054] Step S22: Perform multifractal detrending fluctuation analysis on the nuclear magnetic resonance echo data to obtain multifractal detrending fluctuation analysis parameters.

[0055] Step S23: Based on the multifractal detrending fluctuation analysis parameters and the preset pore structure characteristic parameters, select the target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters.

[0056] Specifically, the correlation between the multifractal detrending fluctuation analysis parameters and the preset pore structure characteristic parameters is calculated; based on the correlation, target multifractal detrending fluctuation analysis parameters are selected from all the multifractal detrending fluctuation analysis parameters. The specific selection process is as follows: all the multifractal detrending fluctuation analysis parameters are sorted based on the correlation to obtain sorted multifractal detrending fluctuation analysis parameters; the number of target parameters is determined according to business needs, and target multifractal detrending fluctuation analysis parameters are selected from the sorted multifractal detrending fluctuation analysis parameters according to the number of parameters.

[0057] Taking Table 1 as an example, Table 1 shows the correlation between MF-DFA parameters and pore structure characteristic parameters. Here, K represents permeability, r_ave represents average pore radius, Pd represents displacement pressure, Rs represents relative sorting coefficient, and Pc50 represents median pressure. The numbers in the figure represent the correlation coefficient R between the corresponding two sets of parameters. It can be seen that the three optimal MF-DFA parameters for evaluating reservoir pore structure are... , H. Table 1

[0058]

[0059] In existing technologies, nuclear magnetic resonance is used. Spectroscopy is used to evaluate reservoir pore structure, in order to Figure 3 For example, Figure 3 (1) in the text represents the nuclear magnetic resonance obtained through core experiment analysis. Spectrum Figure 1 (2) in the text represents the corresponding echo data. There are a total of 19 sets. These cores come from the Changqing Oil and Gas Field and are composed of tight sandstone. Figure 4 In the figure, (1) represents the generalized Hurst exponent of nuclear magnetic resonance echo data. Figure 4 In the text, (2) represents the singular spectrum, based on Figure 3 (2) can be obtained Figure 4 The generalized Hurst index and singular spectrum in [the text].

[0060] according to Figure 3 (1) It is possible to obtain results based on nuclear magnetic resonance. The correlation between spectral multifractal parameters and pore structure characteristic parameters is shown in Table 2. The numbers in Table 2 represent the correlation coefficients R between the corresponding two sets of parameters. The fractal dimension representing the moment q when it is at its maximum. The fractal dimension representing the minimum value of the moment q. represent and difference, The singular value exponent representing the maximum moment q, The singular value exponent representing the minimum value of moment q, represent and difference, This represents the dimension of information. It can be seen that the three multifractal parameters for evaluating the optimal reservoir pore structure are: , , The evaluation results are worse than those based on MF-DFA parameters.

[0061] Table 2

[0062]

[0063] Step S24: Evaluate the reservoir pore structure based on the target multifractal detrending fluctuation analysis parameters.

[0064] In this embodiment, after evaluating the reservoir porosity structure based on the target multifractal detrended fluctuation analysis parameters, the evaluation results are obtained. Based on the evaluation results and the target multifractal detrended fluctuation analysis parameters, the evaluation effect of each reservoir porosity structure is plotted. Specific effect diagrams are shown below. Figure 5 As shown, it can intuitively demonstrate , The correlation between H and K, r_ave, Pd, Rs, and Pc50.

[0065] In this embodiment, nuclear magnetic resonance (NMR) echo data is acquired; multifractal detrended fluctuation analysis (MF-DFA) is performed on the NMR echo data to obtain MF-DFA parameters; target MF-DFA parameters are selected from the MF-DFA parameters; and the reservoir porosity structure is evaluated based on the target MF-DFA parameters. This application utilizes the MF-DFA method for reservoir porosity structure evaluation to process NMR echo data, obtain MF-DFA parameters, and evaluate reservoir porosity structure based on MF-DFA parameters. This method directly processes NMR echo data, avoids uncertainties in the inversion process, and thus improves the accuracy of reservoir porosity structure evaluation.

[0066] See Figure 6 As shown in the figure, an embodiment of the present invention discloses a reservoir pore structure evaluation device, which may specifically include:

[0067] Data acquisition module 11 is used to acquire nuclear magnetic resonance echo data;

[0068] The analysis and processing module 12 is used to perform multifractal detrending fluctuation analysis on the nuclear magnetic resonance echo data to obtain multifractal detrending fluctuation analysis parameters.

[0069] The parameter filtering module 13 is used to filter out target multifractal detrending fluctuation analysis parameters from the multifractal detrending fluctuation analysis parameters.

[0070] The reservoir pore structure evaluation module 14 is used to evaluate the reservoir pore structure based on the target multifractal detrended fluctuation analysis parameters.

[0071] In this embodiment, nuclear magnetic resonance (NMR) echo data is acquired; multifractal detrended fluctuation analysis (MF-DFA) is performed on the NMR echo data to obtain MF-DFA parameters; target MF-DFA parameters are selected from the MF-DFA parameters; and the reservoir porosity structure is evaluated based on the target MF-DFA parameters. This application utilizes the MF-DFA method for reservoir porosity structure evaluation to process NMR echo data, obtain MF-DFA parameters, and evaluate reservoir porosity structure based on MF-DFA parameters. This method directly processes NMR echo data, avoids uncertainties in the inversion process, and thus improves the accuracy of reservoir porosity structure evaluation.

[0072] In some specific embodiments, the analysis and processing module 12 may specifically include:

[0073] The data processing module is used to process the nuclear magnetic resonance echo data using a multifractal-detrended fluctuation analysis method to obtain multifractal detrended fluctuation analysis parameters.

[0074] In some specific embodiments, the analysis and processing module 12 may specifically include:

[0075] The multifractal detrended fluctuation analysis parameters include the generalized Hearst exponent at the maximum moment, the generalized Hearst exponent at the minimum moment, the difference between the generalized Hearst exponent at the maximum moment and the generalized Hearst exponent at the minimum moment, the singular value exponent at the maximum moment, the singular value exponent at the minimum moment, the difference between the singular value exponent at the maximum moment and the singular value exponent at the minimum moment, and the Hearst exponent.

[0076] In some specific embodiments, the analysis parameter filtering module 13 may specifically include:

[0077] The analysis parameter filtering module is used to filter out target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the multifractal detrending fluctuation analysis parameters and preset pore structure characteristic parameters.

[0078] In some specific embodiments, the analysis parameter filtering module 13 may specifically include:

[0079] The correlation calculation module is used to calculate the correlation between the multifractal detrending fluctuation analysis parameters and the preset pore structure characteristic parameters;

[0080] The parameter filtering module is used to filter out target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the correlation.

[0081] In some specific embodiments, the analysis parameter filtering module 13 may specifically include:

[0082] The parameter sorting module is used to sort all the multifractal detrending fluctuation analysis parameters based on the correlation to obtain the sorted multifractal detrending fluctuation analysis parameters.

[0083] The parameter filtering module is used to determine the number of target parameters according to business needs, and to filter the target multifractal detrending fluctuation analysis parameters from the sorted multifractal detrending fluctuation analysis parameters according to the number of parameters.

[0084] In some specific embodiments, the reservoir pore structure evaluation module 14 may specifically include:

[0085] The results acquisition module is used to acquire evaluation results;

[0086] The effect drawing module is used to draw the evaluation effect diagram of each reservoir pore structure based on the evaluation results and the target multifractal detrending fluctuation analysis parameters.

[0087] Figure 7 This is a schematic diagram of an electronic device provided in an embodiment of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the reservoir porosity structure evaluation method performed by the electronic device disclosed in any of the foregoing embodiments.

[0088] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0089] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored on it include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.

[0090] The operating system 221 manages and controls the various hardware devices on the electronic device 20 and the computer program 222 to enable the processor 21 to perform calculations and processing on the data 223 in the memory 22. The operating system 221 can be Windows, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the reservoir porosity structure evaluation method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the reservoir porosity structure evaluation device from external devices, as well as data collected by its own input / output interface 25.

[0091] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0092] Furthermore, embodiments of this application also disclose a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, it implements the reservoir pore structure evaluation method steps disclosed in any of the foregoing embodiments.

[0093] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0094] The present invention provides a detailed description of a reservoir pore structure evaluation method, apparatus, device, and storage medium. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for evaluating reservoir pore structure, characterized in that, include: Acquire nuclear magnetic resonance echo data; The nuclear magnetic resonance echo data is subjected to multifractal detrending fluctuation analysis to obtain multifractal detrending fluctuation analysis parameters. Select the target multifractal detrending fluctuation analysis parameters from the multifractal detrending fluctuation analysis parameters; The reservoir pore structure is evaluated based on the target multifractal detrended fluctuation analysis parameters. Selecting target multifractal detrending fluctuation analysis parameters from the multifractal detrending fluctuation analysis parameters includes: selecting target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the multifractal detrending fluctuation analysis parameters and preset pore structure characteristic parameters; Based on the multifractal detrending fluctuation analysis parameters and the preset pore structure characteristic parameters, the target multifractal detrending fluctuation analysis parameters are selected from all the multifractal detrending fluctuation analysis parameters, including: calculating the correlation between the multifractal detrending fluctuation analysis parameters and the preset pore structure characteristic parameters; and selecting the target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the correlation. Selecting target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the correlation includes: sorting all the multifractal detrending fluctuation analysis parameters based on the correlation to obtain sorted multifractal detrending fluctuation analysis parameters; determining the number of target parameters according to business needs; and selecting target multifractal detrending fluctuation analysis parameters from the sorted multifractal detrending fluctuation analysis parameters according to the number.

2. The reservoir pore structure evaluation method according to claim 1, characterized in that, The nuclear magnetic resonance echo data is subjected to multifractal detrended fluctuation analysis to obtain multifractal detrended fluctuation analysis parameters, including: The nuclear magnetic resonance echo data were processed using a multifractal-detrended fluctuation analysis method to obtain multifractal detrended fluctuation analysis parameters.

3. The reservoir pore structure evaluation method according to claim 2, characterized in that, The multifractal detrended fluctuation analysis parameters include the generalized Hearst exponent at the maximum moment, the generalized Hearst exponent at the minimum moment, the difference between the generalized Hearst exponent at the maximum moment and the generalized Hearst exponent at the minimum moment, the singular value exponent at the maximum moment, the singular value exponent at the minimum moment, the difference between the singular value exponent at the maximum moment and the singular value exponent at the minimum moment, and the Hearst exponent.

4. The reservoir pore structure evaluation method according to any one of claims 1 to 3, characterized in that, After evaluating the reservoir porosity structure based on the target multifractal detrending fluctuation analysis parameters, the evaluation also includes: Obtain evaluation results; Based on the evaluation results and the target multifractal detrending fluctuation analysis parameters, the evaluation effect diagrams of the pore structure of each reservoir are drawn.

5. A reservoir pore structure evaluation device, characterized in that, include: The data acquisition module is used to acquire nuclear magnetic resonance echo data; The analysis and processing module is used to perform multifractal detrending fluctuation analysis on the nuclear magnetic resonance echo data to obtain multifractal detrending fluctuation analysis parameters. The parameter filtering module is used to filter out target multifractal detrending fluctuation analysis parameters from the multifractal detrending fluctuation analysis parameters. The reservoir pore structure evaluation module is used to evaluate the reservoir pore structure based on the target multifractal detrended fluctuation analysis parameters. Selecting target multifractal detrending fluctuation analysis parameters from the multifractal detrending fluctuation analysis parameters includes: selecting target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the multifractal detrending fluctuation analysis parameters and preset pore structure characteristic parameters; Based on the multifractal detrending fluctuation analysis parameters and the preset pore structure characteristic parameters, the target multifractal detrending fluctuation analysis parameters are selected from all the multifractal detrending fluctuation analysis parameters, including: calculating the correlation between the multifractal detrending fluctuation analysis parameters and the preset pore structure characteristic parameters; and selecting the target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the correlation. Selecting target multifractal detrending fluctuation analysis parameters from all the multifractal detrending fluctuation analysis parameters based on the correlation includes: sorting all the multifractal detrending fluctuation analysis parameters based on the correlation to obtain sorted multifractal detrending fluctuation analysis parameters; determining the number of target parameters according to business needs; and selecting target multifractal detrending fluctuation analysis parameters from the sorted multifractal detrending fluctuation analysis parameters according to the number.

6. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the reservoir pore structure evaluation method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the reservoir porosity structure evaluation method as described in any one of claims 1 to 4.

Images