Distance analysis-based lithofacies identification method, apparatus and device, and storage medium

Abstract

The invention belongs to the technical field of earthquake comprehensive interpretation research, and discloses a lithofacies identification method based on difference analysis, which comprises the following steps: firstly, calculating to obtain difference data of a longitudinal wave velocity logging curve, difference data of a transverse wave velocity logging curve and difference data of a density logging curve; performing clustering analysis on the difference data of the longitudinal wave velocity logging curve, the difference data of the transverse wave velocity logging curve and the difference data of the density logging curve by using a Gaussian Mixture method according to the drilling lithofacies type to obtain logging lithofacies data; analyzing the lithofacies depth trend of the well logging according to the lithofacies data of the well logging, and predicting the lithofacies space distribution of the well logging by taking the lithofacies depth trend as a constraint and utilizing a Bayesian method; the invention further provides a lithofacies recognition device and equipment based on difference analysis and a storage medium. The lithofacies can be accurately identified, the identification working efficiency is improved, and the method has a good application effect and a good popularization prospect.

Description

Technical Field

[0001] The present invention belongs to the technical field of seismic comprehensive interpretation research, and relates to a lithofacies identification method, device, equipment and storage medium based on differential analysis. Background Art

[0002] The identification and classification of shale lithofacies are the basic work in the process of shale gas exploration and development. The rock mineral components of shale lithofacies are extremely complex, and the accurate identification and classification of lithofacies are the basis for evaluating reservoir quality and oiliness. At present, shale lithofacies can be accurately identified mainly through drilling core sampling and laboratory mineral component determination. This method is not only limited by drilling core sampling, but also due to reasons such as long testing time and high cost, generally only limited data of the target interval are tested, and the lithofacies information of the entire well section cannot be obtained. To achieve rapid and accurate identification of shale lithofacies, it is a good method to identify lithofacies types based on logging curves. Logging curves reflect complex lithology change information. Therefore, establishing the relationship between logging lithofacies and seismic facies is the key to realizing lithofacies identification. Summary of the Invention

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

[0004] The purpose of the present invention is to provide a lithofacies identification device, equipment and storage medium based on differential analysis.

[0005] To achieve the above object, the technical solution adopted by the present 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] Taking the logging depth value at the start of calculation as the initial reference point, set the number of samples of the first subsequence and the number of samples of the second subsequence in the logging curve.

[0009] S2. Calculate differential data

[0010] 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 in the logging curve, the average value and variance of the second subsequence, and calculate the differential data of the entire logging curve when the reference point is moved down each time.

[0011] S3. Lithofacies identification;

[0012] Bring the longitudinal wave velocity logging curve, the shear wave velocity logging curve, and the density logging curve into Steps S1 to S2 respectively, and calculate the difference data of the longitudinal wave velocity logging curve, the difference data of the shear wave velocity logging curve, and the difference data of the density logging curve;

[0013] According to the drilling lithofacies type, use the GaussianMixture method to perform cluster analysis on the difference data of the longitudinal wave velocity logging curve, the difference data of the shear wave velocity logging curve, and the difference data of the density logging curve to obtain the lithofacies data of the logging;

[0014] S4. Lithofacies depth analysis

[0015] Analyze the lithofacies depth trend of the logging based on the lithofacies data of the logging;

[0016] S5. Lithofacies space prediction

[0017] Using the lithofacies depth trend as a constraint, use the Bayesian method to predict the lithofacies spatial distribution of the logging.

[0018] As a limitation, in Step S2, the calculation of the difference data is as follows:

[0019]

[0020]

[0021] Among them, diff is the difference data of the logging curve, s is the mean square error of the first subsequence and the second subsequence, n 1 is the sample size of the first subsequence, n 2 is the sample size of the second subsequence, is the average value of the first subsequence; is the average value of the second subsequence, s 1 is the variance of the first subsequence, s 2 is the variance of the second subsequence.

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

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

[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 in the logging curve, the average value and variance of the second subsequence, and calculate the difference data of the entire logging curve;

[0025] The lithofacies identification module is used to substitute the longitudinal wave velocity logging curve, the shear wave velocity logging curve, and the density logging curve into steps S1 to S2 respectively, and calculate the difference data of the longitudinal wave velocity logging curve, the difference data of the shear wave velocity logging curve, and the difference data of the density logging curve;

[0026] Using the difference data of the longitudinal wave velocity logging curve, the difference data of the shear wave velocity logging curve, and the difference data of the density logging curve, and according to the drilling lithofacies type, the Gaussian Mixture method is used to perform clustering analysis on the difference data of the longitudinal wave velocity logging curve, the difference data of the shear wave velocity logging curve, and the difference data of the density logging curve to obtain the lithofacies data of the logging;

[0027] The lithofacies depth analysis module is used to analyze the lithofacies depth trend of the logging according to the lithofacies data of the logging;

[0028] The lithofacies spatial prediction module is used to use the lithofacies depth trend as a constraint and use the Bayesian method to predict the lithofacies spatial distribution of the logging.

[0029] The present invention also provides a computer device, which includes a processor and a memory. The memory is used to store at least one segment of computer program, and the at least one segment of computer program is loaded and executed by the processor to perform the lithofacies identification method based on difference analysis as described above.

[0030] The present invention also provides a storage medium, which is used to store at least one segment of computer program, and the at least one segment of computer program is used to execute the lithofacies identification method based on difference analysis.

[0031] Due to the adoption of the above technical solution, compared with the prior art, the technical progress achieved by the present invention is as follows:

[0032] (1) By using the difference data of the longitudinal wave velocity logging curve, the difference data of the shear wave velocity logging curve, and the difference data of the density logging curve, and according to the drilling lithofacies type, the present invention obtains the lithofacies data of the logging, and then conducts depth analysis on the lithofacies and predicts the lithofacies spatial distribution of the logging, providing a basis for seismic exploration and development;

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

[0034] (3) The present invention is easy to operate, is conducive to popularization, and provides a practical method for further tapping the potential of oil fields.

[0035] In summary, the present invention can accurately identify lithofacies, improve the identification work efficiency, and has good application effects and popularization prospects. Description of the Drawings

[0036] Figure 1 The following is the flowchart of the method according to Embodiment 1 of the present invention;

[0037] Figure 2 The following is a schematic diagram of the difference data of the logging curves according to Embodiment 1 of the present invention;

[0038] Figure 3 The following is the lithofacies identification result according to Embodiment 1 of the present invention;

[0039] Figure 4 The following is an analysis diagram of the depth trend of different lithofacies according to Embodiment 1 of the present invention;

[0040] Figure 5 The following is the lithofacies prediction result according to Embodiment 1 of the present invention;

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

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

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

[0044] For better explaining the present invention and facilitating understanding, the present invention will be described in detail below with reference to the accompanying drawings through specific implementation manners.

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

[0046] As Figure 1 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] Taking the logging depth value at the start of calculation as the initial reference point, set the number of samples of the first subsequence and the number of samples of the second subsequence in the logging curve;

[0049] S2. Calculate the difference data

[0050] Slide the reference point, and move the reference point down to the end of the first subsequence in turn. When the reference point is moved down each time, calculate the average value and variance of the first subsequence in the logging curve, and the average value and variance of the second subsequence, 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:

[0052]

[0053]

[0054] Among them, diff is the difference data of the logging curve, s is the mean square error of the first subsequence and the second subsequence, and n 1 is the sample size of the first subsequence, and n 2 is the sample size of the second subsequence, is the average value of the first subsequence; is the average value of the second subsequence, and s 1 is the variance of the first subsequence, and s 2 is the variance of the second subsequence;

[0055] S3. Lithofacies identification;

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

[0057] According to the drilling lithofacies type, use the GaussianMixture method to perform cluster analysis on the difference data diff-Vp of the compressional wave velocity logging curve, the difference data diff-Vs of the shear wave velocity logging curve, and the difference data diff-den of the density logging curve to obtain the lithofacies data of the logging;

[0058] As Figure 2 shown is the schematic diagram of the difference data of the logging curve obtained in this embodiment. From Figure 2 it can be seen that the difference data diff-Vp of the compressional wave velocity logging curve, the difference data diff-Vs of the shear wave velocity logging curve, and the difference data diff-den of the density logging curve are more likely to identify the lithofacies of the logging;

[0059] As Figure 3 shown is the lithofacies identification result obtained in this step. From Figure 3 it can be known that according to the difference data diff-Vp of the compressional wave velocity logging curve, the difference data diff-Vs of the shear wave velocity logging curve, and the difference data diff-den of the density logging curve, 3 lithofacies, sandstone facies, soft shale facies, and hard shale facies, are obtained by using cluster analysis;

[0060] S4. Lithofacies depth analysis

[0061] According to the lithofacies data of the logging, analyze the lithofacies depth trend of the logging;

[0062] As Figure 4 shown in the depth trend analysis chart of different lithofacies obtained in this step, it can be seen from Figure 4 this that the depth distribution trend of each lithofacies is different. Among them, the longitudinal wave velocity logging curve Vp, the transverse wave velocity logging curve Vs, and the density logging curve den of the sandstone facies are relatively small, those of the soft shale facies are in the middle, and those of the hard shale are relatively small;

[0063] S5. Lithofacies spatial prediction

[0064] Using the lithofacies depth trend as a constraint and the Bayesian method to predict the spatial distribution of logging lithofacies;

[0065] As Figure 5 shown in the lithofacies prediction result obtained in this step, it can be seen from Figure 5 this that the lithofacies distribution characteristics of the research work area are obtained, and the spatial distribution of lithofacies is consistent with the logging lithofacies result and is reasonably distributed. Therefore, in this embodiment, the differential analysis method can be used to process complex logging curves, obtain lithology change interfaces, identify lithofacies that can be recognized by seismic, and use well-seismic combination to perform spatial prediction of lithofacies, providing a basis for seismic exploration and development.

[0066] Embodiment 2 A lithofacies identification method based on differential analysis

[0067] As Figure 6 shown, this embodiment is a lithofacies identification device based on differential analysis, including:

[0068] A subsequence sample number determination module, which uses the logging depth value at the start of the calculation as the initial reference point, and sets the sample number of the first subsequence and the sample number of the second subsequence in the logging curve;

[0069] A differential data calculation module, which slides to set the reference point, moves the reference point down to the end of the first subsequence in turn, calculates the average value and variance of the first subsequence in the logging curve, the average value and variance of the second subsequence, and calculates the differential data of the entire logging curve when the reference point moves down each time;

[0070] A lithofacies identification module, which respectively brings the longitudinal wave velocity logging curve, the transverse wave velocity logging curve, and the density logging curve into steps S1 to S2 to calculate the differential data of the longitudinal wave velocity logging curve, the differential data of the transverse wave velocity logging curve, and the differential data of the density logging curve;

[0071] Using the difference data of the longitudinal wave velocity logging curve, the difference data of the shear wave velocity logging curve, and the difference data of the density logging curve, and according to the drilling lithofacies type, the Gaussian Mixture method is used to perform clustering analysis on the difference data of the longitudinal wave velocity logging curve, the difference data of the shear wave velocity logging curve, and the difference data of the density logging curve to obtain the lithofacies data of the logging;

[0072] The lithofacies depth analysis module is used to analyze the lithofacies depth trend of the logging according to the lithofacies data of the logging;

[0073] The lithofacies spatial prediction module is used to use the lithofacies depth trend as a constraint and use the Bayesian method to predict the lithofacies spatial distribution of the logging.

[0074] Among them, when the lithofacies identification device based on difference analysis provided in this embodiment performs data processing, only the above-mentioned division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules according to needs.

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

[0076] Based on the same inventive concept, as Figure 8 shown, this embodiment also provides a computer-readable storage medium, and the storage medium is used to store at least one segment of computer program, and at least one segment of computer program is used to execute the lithofacies identification method based on difference analysis in Embodiment 1.

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

[0078] In addition, it should be understood that the computer-readable storage medium herein (for example, the memory) can be a volatile memory or a non-volatile memory, or can include both a volatile memory and a non-volatile memory.

[0079] Those skilled in the art will also understand that the various exemplary logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, the functions of the various illustrative components, blocks, modules, circuits, and steps have been described generally. Whether this function is implemented as software or hardware depends on the particular application and the design constraints imposed on the overall system. The functions that can be implemented in various ways by those skilled in the art for each specific application, but such implementation decisions should not be construed as causing a departure from the scope of the disclosure of the embodiments of the present invention.

[0080] It should be noted that the above are only the preferred embodiments of the present invention and are not used 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 recorded in the above embodiments or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A lithofacies identification method based on differential analysis, characterized in that, it includes the following steps: S1. Determine the number of subsequence samples Taking the logging depth value at the start of calculation as the initial reference point, set the number of samples of the first subsequence and the number of samples of the second subsequence in the logging curve; S2. Calculate differential data Slide the reference point, move the reference point down to the end of the first subsequence in turn, calculate the mean and variance of the first subsequence in the logging curve, and the mean and variance of the second subsequence each time the reference point is moved down, and calculate the differential data of the entire logging curve; S3. Lithofacies identification; Bring the longitudinal wave velocity logging curve, transverse wave velocity logging curve, and density logging curve into steps S1 - S2 respectively, and calculate the differential data of the longitudinal wave velocity logging curve, the differential data of the transverse wave velocity logging curve, and the differential data of the density logging curve; According to the drilling lithofacies type, use the GaussianMixture method to perform clustering analysis on the differential data of the longitudinal wave velocity logging curve, the differential data of the transverse wave velocity logging curve, and the differential data of the density logging curve to obtain the lithofacies data of the logging; S4. Lithofacies depth analysis According to the lithofacies data of the logging, analyze the lithofacies depth trend of the logging; S5. Lithofacies spatial prediction Using the lithofacies depth trend as a constraint, use the Bayesian method to predict the lithofacies spatial distribution of the logging.

2. The lithofacies identification method based on differential analysis according to claim 1, characterized in that, In step S2, the differential data of the entire logging curve is calculated as: where diff is the difference data of the logging curves, s is the mean square error of the first subsequence and the second subsequence, n 1 is the sample size of the first subsequence, n 2 is the sample size of the second subsequence, is the mean value of the first subsequence; is the mean value of the second subsequence, s 1 is the variance of the first subsequence, s 2 is the variance of the second subsequence.

3. A lithofacies identification device based on differential analysis, characterized in that, it includes: A subsequence sample number determination module, which is used to take the logging depth value at the start of calculation as the 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 differential data calculation module, which is used to slide the reference point, move the reference point down to the end of the first subsequence in turn, calculate the mean and variance of the first subsequence in the logging curve, and the mean and variance of the second subsequence each time the reference point is moved down, and calculate the differential data of the entire logging curve; A lithofacies identification module, which is used to bring the longitudinal wave velocity logging curve, transverse wave velocity logging curve, and density logging curve into steps S1 - S2 respectively, and calculate the differential data of the longitudinal wave velocity logging curve, the differential data of the transverse wave velocity logging curve, and the differential data of the density logging curve; Using the differential data of the longitudinal wave velocity logging curve, the differential data of the transverse wave velocity logging curve, and the differential data of the density logging curve, and according to the drilling lithofacies type, use the GaussianMixture method to perform clustering analysis on the differential data of the longitudinal wave velocity logging curve, the differential data of the transverse wave velocity logging curve, and the differential data of the density logging curve to obtain the lithofacies data of the logging; A lithofacies depth analysis module, which is used to analyze the lithofacies depth trend of the logging according to the lithofacies data of the logging; A lithofacies spatial prediction module, which is used to use the lithofacies depth trend as a constraint and use the Bayesian method to predict the lithofacies spatial distribution of the logging.

4. A computer device, characterized in that, The computer device includes a processor and a memory, and the memory is used to store at least one segment of computer program, and the at least one segment of computer program is loaded and executed by the processor to perform the lithofacies identification method based on difference analysis according to any one of claims 1 or 2.

5. A storage medium, characterized in that the storage medium is used to store at least one segment of computer program, and the at least one segment of computer program is used to perform the lithofacies identification method based on difference analysis according to any one of claims 1 or 2.

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