Methods, devices, equipment, and storage media for lithofacies identification based on difference analysis

Abstract

This invention belongs to the field of seismic comprehensive interpretation research technology and discloses a lithofacies identification method based on difference analysis. First, it calculates the difference data of P-wave velocity logging curves, S-wave velocity logging curves, and density logging curves. Using the drilling lithofacies type, the Gaussian Mixture method is used to perform cluster analysis on the difference data of P-wave velocity logging curves, S-wave velocity logging curves, and density logging curves to obtain the well logging lithofacies data. Based on the well logging lithofacies data, the lithofacies depth trend is analyzed. Using the lithofacies depth trend as a constraint, a Bayesian method is used to predict the spatial distribution of well logging lithofacies. This invention also provides a lithofacies identification device, equipment, and storage medium based on difference analysis. This invention can accurately identify lithofacies, improve the efficiency of identification work, and has good application effects and promotion prospects.

Description

Technical Field

[0001] This invention belongs to the field of earthquake comprehensive interpretation research technology, and relates to a lithofacies identification method, device, equipment and storage medium based on difference analysis. Background Technology

[0002] Identification and classification of shale lithofacies are fundamental tasks in shale gas exploration and development. The rock and mineral composition of shale lithofacies is extremely complex, and accurate identification and classification are essential for evaluating reservoir quality and oil-bearing potential. Currently, accurate identification of shale lithofacies mainly relies on well core sampling and laboratory mineral composition analysis. This method is limited by well core sampling and, due to its long testing time and high cost, generally only tests a limited number of data points in the target section, failing to obtain lithofacies information for the entire well section. To achieve rapid and accurate identification of shale lithofacies, identifying lithofacies types based on well logging curves is a good approach. Well logging curves reflect complex lithological variations; therefore, establishing the relationship between well logging lithofacies and seismic facies is crucial for lithofacies identification. Summary of the Invention

[0003] The purpose of this invention is to provide a lithofacies identification method based on differential logging, which obtains lithofacies data from well logging through differential logging data, and then performs in-depth analysis of lithofacies and predicts the spatial distribution of lithofacies in well logging.

[0004] The purpose of this invention is to provide a petrographic identification device, equipment, and storage medium based on difference analysis.

[0005] To achieve the above objectives, the technical solution adopted by this invention is as follows:

[0006] A lithofacies identification method based on differential analysis includes the following steps:

[0007] S1. Determine the number of subsequence samples.

[0008] Using the initial logging depth value as the initial reference point, set the number of samples for the first subsequence and the number of samples for the second subsequence in the logging curve;

[0009] S2, Calculate the difference data

[0010] Slide the reference point and move it down to the end of the first subsequence. Calculate the average and variance of the first subsequence and the average and variance of the second subsequence in the logging curve each time the reference point is moved down, and calculate the difference data of the entire logging curve.

[0011] S3, Lithofacies identification;

[0012] Substitute the P-wave velocity logging curve, S-wave velocity logging curve, and density logging curve into steps S1 to S2 respectively to calculate the difference data of the P-wave velocity logging curve, the difference data of the S-wave velocity logging curve, and the difference data of the density logging curve.

[0013] Based on the drilling lithofacies type, the Gaussian Mixture method was used to perform cluster analysis on the difference data of P-wave velocity logging curves, S-wave velocity logging curves, and density logging curves to obtain the logging lithofacies data.

[0014] S4, Lithofacies depth analysis

[0015] Based on the lithofacies data from well logging, analyze the lithofacies depth trend;

[0016] S5, Lithofacies spatial prediction

[0017] Using the lithofacies depth trend as a constraint, the spatial distribution of lithofacies in well logging is predicted using the Bayesian method.

[0018] As a limitation, in step S2, the difference data is calculated as follows:

[0019]

[0020]

[0021] Where, diff represents the difference data of the logging curves, s represents the root mean square error of the first and second subsequences, n1 represents the sample size of the first subsequence, and n2 represents the sample size of the second subsequence. The average value of the first subsequence; s1 is the average of the second subsequence, s2 is the variance of the first subsequence, and s3 is the variance of the second subsequence.

[0022] The present invention also provides a lithofacies identification device based on difference analysis, comprising:

[0023] The subsequence sample number determination module is used to set the sample number of the first subsequence and the sample number of the second subsequence in the logging curve, using the logging depth value at the beginning of the calculation as the initial reference point.

[0024] The difference data calculation module is used to slide and set the reference point, move the reference point down to the end of the first subsequence in turn, calculate the average value and variance of the first subsequence and the average value and variance of the second subsequence in the logging curve each time the reference point is moved down, and calculate the difference data of the entire logging curve.

[0025] The lithofacies identification module is used to input the P-wave velocity logging curve, the S-wave velocity logging curve, and the density logging curve into steps S1 to S2 respectively, and calculate the difference data of the P-wave velocity logging curve, the difference data of the S-wave velocity logging curve, and the difference data of the density logging curve.

[0026] By utilizing the difference data of P-wave velocity logging curves, S-wave velocity logging curves, and density logging curves, and based on the drilling lithofacies type, the Gaussian Mixture method was used to perform cluster analysis on the difference data of P-wave velocity logging curves, S-wave velocity logging curves, and density logging curves to obtain the lithofacies data of the well logs.

[0027] The lithofacies depth analysis module is used to analyze the lithofacies depth trend of well logging based on the lithofacies data.

[0028] The lithofacies spatial prediction module is used to predict the lithofacies spatial distribution of well logging using Bayesian methods, with lithofacies depth trends as constraints.

[0029] The present invention also provides a computer device, the computer device including a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded by the processor and executed by the processor to perform the lithofacies identification method based on difference analysis.

[0030] The present invention also provides a storage medium for storing at least one computer program for executing the aforementioned petrographic identification method based on difference analysis.

[0031] The present invention, by adopting the above-described technical solution, achieves the following technical advancements compared to existing technologies:

[0032] (1) This invention obtains well-logging lithofacies data based on the drilling lithofacies type by using the difference data of the P-wave velocity logging curve, the difference data of the S-wave velocity logging curve and the difference data of the density logging curve, and then performs depth analysis of the lithofacies and predicts the spatial distribution of the lithofacies in the well log, providing a basis for seismic exploration and development.

[0033] (2) The method of the present invention is simple and intuitive, with high prediction accuracy, and can accurately identify lithofacies, which is convenient for guiding the actual shale gas extraction.

[0034] (3) This invention is easy to operate and easy to promote, providing a practical and feasible method for further tapping the potential of oil fields.

[0035] In summary, this invention can accurately identify lithofacies, improve the efficiency of identification work, and has good application effects and prospects for promotion. Attached Figure Description

[0036] Figure 1 The diagram shown is a flowchart of the method in Embodiment 1 of the present invention;

[0037] Figure 2 The figure shown is a schematic diagram of the difference data of the logging curves in Embodiment 1 of the present invention;

[0038] Figure 3 The image shows the petrographic identification results of Example 1 of the present invention;

[0039] Figure 4 The figure shown is a trend analysis diagram of different lithofacies depths in Embodiment 1 of the present invention;

[0040] Figure 5 The figure shows the lithofacies prediction results of Example 1 of the present invention;

[0041] Figure 6 The diagram shown is a block diagram of the device according to Embodiment 2 of the present invention;

[0042] Figure 7 The diagram shown is a structural schematic of the computer device according to Embodiment 2 of the present invention;

[0043] Figure 8 The diagram shown is a structural schematic of the computer storage medium according to Embodiment 2 of the present invention. Detailed Implementation

[0044] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] Example 1: A lithofacies identification method based on difference analysis

[0046] like Figure 1 As shown, this embodiment is a lithofacies identification method based on difference analysis, including the following steps:

[0047] S1. Determine the number of subsequence samples.

[0048] Using the initial logging depth value as the initial reference point, set the number of samples for the first subsequence and the number of samples for the second subsequence in the logging curve;

[0049] S2, Calculate the difference data

[0050] Slide the reference point and move it down to the end of the first subsequence. Calculate the average and variance of the first subsequence and the average and variance of the second subsequence in the logging curve each time the reference point is moved down, and calculate the difference data of the entire logging curve.

[0051] In this step, the difference data of the entire logging curve is calculated as follows:

[0052]

[0053]

[0054] Where, diff represents the difference data of the logging curves, s represents the root mean square error of the first and second subsequences, n1 represents the sample size of the first subsequence, and n2 represents the sample size of the second subsequence. The average value of the first subsequence; s1 is the average of the second subsequence, s2 is the variance of the first subsequence, and s3 is the variance of the second subsequence.

[0055] S3, Lithofacies identification;

[0056] Substitute the P-wave velocity logging curve Vp, the S-wave velocity logging curve Vs, and the density logging curve den into steps S1 to S2 respectively to calculate the difference data diff-Vp of the P-wave velocity logging curve, the difference data diff-Vs of the S-wave velocity logging curve, and the difference data diff-den of the density logging curve.

[0057] Based on the drilling lithofacies type, the Gaussian Mixture method was used to perform cluster analysis on the difference data of P-wave velocity logging curves (diff-Vp), S-wave velocity logging curves (diff-Vs), and density logging curves (diff-den) to obtain the logging lithofacies data.

[0058] like Figure 2 The diagram shown illustrates the differences in logging curves obtained in this embodiment. Figure 2 It can be seen that the difference data of P-wave velocity logging curves (diff-Vp), the difference data of S-wave velocity logging curves (diff-Vs), and the difference data of density logging curves (diff-den) are more effective in identifying the lithofacies of well logging.

[0059] like Figure 3 The image shows the lithofacies identification results obtained in this step. Figure 3 It can be seen that, based on the difference data of P-wave velocity logging curves (diff-Vp), the difference data of S-wave velocity logging curves (diff-Vs), and the difference data of density logging curves (diff-den), cluster analysis was used to obtain three lithofacies: sandstone facies, soft shale facies, and hard shale facies.

[0060] S4, Lithofacies depth analysis

[0061] Based on the lithofacies data from well logging, analyze the lithofacies depth trend;

[0062] like Figure 4 The image shown is a trend analysis diagram of different lithofacies depths obtained in this step. Figure 4 It can be seen that the depth distribution trends of each lithofacies are different. Among them, the P-wave velocity logging curve Vp, S-wave velocity logging curve Vs, and density logging curve den of sandstone facies are relatively small. The P-wave velocity logging curve Vp, S-wave velocity logging curve Vs, and density logging curve den of soft shale facies are in the middle. The P-wave velocity logging curve Vp, S-wave velocity logging curve Vs, and density logging curve den of hard shale are relatively small.

[0063] S5, Lithofacies spatial prediction

[0064] Using the lithofacies depth trend as a constraint, the spatial distribution of lithofacies in well logging is predicted using the Bayesian method;

[0065] like Figure 5 The image shows the lithofacies prediction results obtained in this step. Figure 5 It can be seen that the obtained lithofacies distribution characteristics of the study area are consistent with the well logging lithofacies results, and the distribution is reasonable. Therefore, this embodiment uses the difference analysis method to process complex well logging curves, obtain lithological change interfaces, identify lithofacies that can be identified by seismic logging, and use well-seismic joint analysis to make spatial predictions of lithofacies, providing a basis for seismic exploration and development.

[0066] Example 2: A lithofacies identification method based on difference analysis

[0067] like Figure 6 As shown, this embodiment is a petrographic identification device based on difference analysis, comprising:

[0068] The subsequence sample number determination module is used to set the sample number of the first subsequence and the sample number of the second subsequence in the logging curve, using the logging depth value at the beginning of the calculation as the initial reference point.

[0069] The difference data calculation module is used to slide and set the reference point, move the reference point down to the end of the first subsequence in turn, calculate the average value and variance of the first subsequence and the average value and variance of the second subsequence in the logging curve each time the reference point is moved down, and calculate the difference data of the entire logging curve.

[0070] The lithofacies identification module is used to input the P-wave velocity logging curve, the S-wave velocity logging curve, and the density logging curve into steps S1 to S2 respectively, and calculate the difference data of the P-wave velocity logging curve, the difference data of the S-wave velocity logging curve, and the difference data of the density logging curve.

[0071] By utilizing the difference data of P-wave velocity logging curves, S-wave velocity logging curves, and density logging curves, and based on the drilling lithofacies type, the Gaussian Mixture method was used to perform cluster analysis on the difference data of P-wave velocity logging curves, S-wave velocity logging curves, and density logging curves to obtain the lithofacies data of the well logs.

[0072] The lithofacies depth analysis module is used to analyze the lithofacies depth trend of well logging based on the lithofacies data.

[0073] The lithofacies spatial prediction module is used to predict the lithofacies spatial distribution of well logging using Bayesian methods, with lithofacies depth trends as constraints.

[0074] In this embodiment, the petrographic identification device based on difference analysis is only illustrated by the above-mentioned division of functional modules when processing data. In actual applications, the above functions can be assigned to different functional modules as needed.

[0075] Based on the same inventive concept, such as Figure 7 As shown, this embodiment also provides a computer device, including: at least a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded by the processor and executed by the processor to perform the lithofacies identification method based on difference analysis of Embodiment 1.

[0076] Based on the same inventive concept, such as Figure 8 As shown, this embodiment also provides a computer-readable storage medium for storing at least one computer program for executing the lithofacies identification method based on difference analysis of Embodiment 1.

[0077] It should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods.

[0078] Furthermore, it should be understood that the computer-readable storage medium (e.g., memory) described herein may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory.

[0079] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the functionality of various illustrative components, blocks, modules, circuits, and steps has been generally described. Whether this functionality is implemented as software or as hardware depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the embodiments disclosed herein.

[0080] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for lithofacies identification based on difference analysis, characterized in that, The method comprises the following steps: S1, determining the number of subsequence samples Taking the logging depth value at which the calculation starts as an initial reference point, setting the number of samples of the first subsequence and the number of samples of the second subsequence in the logging curve; S2, calculating difference data Sliding the reference point, moving the reference point to the end of the first subsequence in sequence, calculating the average value and variance of the first subsequence and the average value and variance of the second subsequence in the logging curve when the reference point moves each time, and calculating the difference data of the entire logging curve; In step S2, the difference data of the entire logging curve is calculated as follows: Wherein, diff is the difference data of the well logging curve, s is the mean square deviation of the first subsequence and the second subsequence, n1 is the sample size of the first subsequence, n2 is the sample size of the second subsequence, is the average value of the first subsequence; is the average value of the second subsequence, s1 is the variance of the first subsequence, and s2 is the variance of the second subsequence. S3, lithofacies identification The P-wave velocity logging curve, the S-wave velocity logging curve and the density logging curve are respectively brought into steps S1-S2, and the difference data of the P-wave velocity logging curve, the difference data of the S-wave velocity logging curve and the difference data of the density logging curve are calculated; According to the drilling lithofacies type, the Gaussian Mixture method is used to perform cluster analysis on the difference data of the P-wave velocity logging curve, the difference data of the S-wave velocity logging curve and the difference data of the density logging curve, and lithofacies data of the logging is obtained; S4, lithofacies depth analysis According to the lithofacies data of the logging, the lithofacies depth trend of the logging is analyzed; S5, lithofacies space prediction Taking the lithofacies depth trend as a constraint, the lithofacies space distribution of the logging is predicted by using the Bayesian method.

2. A lithofacies identification device based on difference analysis, characterized by, The method comprises: A subsequence sample number determination module is configured to take the logging depth value at which the calculation starts as an initial reference point, and set the number of samples of the first subsequence and the number of samples of the second subsequence in the logging curve; A difference data calculation module is configured to slide the reference point, move the reference point to the end of the first subsequence in sequence, calculate the average value and variance of the first subsequence and the average value and variance of the second subsequence in the logging curve when the reference point moves each time, and calculate the difference data of the entire logging curve; The difference data of the entire logging curve is calculated as follows: Wherein, diff is the difference data of the well logging curve, s is the mean square deviation of the first subsequence and the second subsequence, n1 is the sample size of the first subsequence, n2 is the sample size of the second subsequence, is the average value of the first subsequence; is the average value of the second subsequence, s1 is the variance of the first subsequence, and s2 is the variance of the second subsequence. A lithofacies identification module is configured to bring the P-wave velocity logging curve, the S-wave velocity logging curve and the density logging curve into the subsequence sample number determination module and the difference data calculation module respectively, and calculate the difference data of the P-wave velocity logging curve, the difference data of the S-wave velocity logging curve and the difference data of the density logging curve. The P-wave velocity logging curve, the S-wave velocity logging curve and the density logging curve are respectively brought into the subsequence sample number determination module and the difference data calculation module, and the difference data of the P-wave velocity logging curve, the difference data of the S-wave velocity logging curve and the difference data of the density logging curve are calculated; According to the drilling lithofacies type, the Gaussian Mixture method is used to perform cluster analysis on the difference data of the P-wave velocity logging curve, the difference data of the S-wave velocity logging curve and the difference data of the density logging curve, and lithofacies data of the logging is obtained; A lithofacies depth analysis module is configured to analyze the lithofacies depth trend of the logging according to the lithofacies data of the logging; 3. A computer device, comprising: A lithofacies space prediction module is configured to take the lithofacies depth trend as a constraint, and predict the lithofacies space distribution of the logging by using the Bayesian method. The computer device comprises a processor and a memory, the memory is used to store at least one piece of computer program, the at least one piece of computer program is loaded and executed by the processor, and the lithofacies identification method based on difference analysis in claim 1 is executed.

4. A storage medium, characterized by The storage medium is configured to store at least one piece of computer program, and the at least one piece of computer program is configured to execute the lithofacies identification method based on difference analysis. The storage medium is configured to store at least one piece of computer program, and the at least one piece of computer program is configured to execute the lithofacies identification method based on difference analysis.

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