A method and system for logging quantitative characterization of shale reservoir bedding structure

Abstract

The application discloses a kind of shale reservoir bedding structure's logging quantitative characterization method and system, can accurately, effectively identify shale reservoir bedding structure, wide application range, analysis measurement cost is low, improve the exploration and development efficiency of shale oil and gas simultaneously.The method comprises the following steps: collecting the logging curve data of target area;According to the logging curve data, calculate and obtain natural gamma heteromorphic curve, acoustic heteromorphic curve and deep lateral resistivity heteromorphic curve;According to natural gamma heteromorphic curve, acoustic heteromorphic curve and deep lateral resistivity heteromorphic curve, calculate and obtain bedding coefficient curve;Filtering to bedding coefficient curve, construct bedding structure identification standard according to the bedding coefficient curve after filtering;Determine the bedding structure of target area based on bedding structure identification standard, realize the logging quantitative characterization of reservoir bedding structure.

Description

Technical Field

[0001] This invention belongs to the field of shale gas geological exploration and evaluation design technology, specifically relating to a well logging quantitative characterization method and system for shale reservoir bedding structure. Background Technology

[0002] With the development of exploration and development technologies, shale oil has become an important area of ​​exploration for alternative resources. In recent years, most studies on shale and mudstone have focused on pore structure, reservoir characteristics, mechanical properties, and adsorption characteristics, while research on quantitative logging identification methods for shale reservoir bedding structures is relatively limited. Bedding structure is one of the key factors determining whether shale and mudstone can become effective reservoirs, and it is also an important facies marker in shale sedimentology research. Accurate identification of bedding structure is crucial for shale oil and gas exploration and development.

[0003] Currently, there are three common methods for identifying bedding structures:

[0004] One method is to identify reservoir bedding structures through lithological description and thin section microscopic observation. This method is the most accurate in identifying bedding structures, but it is only applicable to the core section of the core well, and its scope of application is narrow. It cannot be applied to the overall reservoir structure.

[0005] The second method is to classify reservoir bedding structures based on FMI imaging logging data. This method is intuitive and effective, but its disadvantage is that FMI imaging logging and array sonic logging are expensive, which is not conducive to the promotion and application of this method in exploration and development blocks.

[0006] Third, the P-wave velocity ratio method is used to identify the bedding structure of shale reservoirs. The bedding structure characteristics are judged by the magnitude of the P-wave velocity ratio. However, this method is only applicable to porous reservoirs and not to the bedding structure characteristics of fractured reservoirs. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a well logging quantitative characterization method and system for shale reservoir bedding structures, which can accurately and effectively identify shale reservoir bedding structures, has a wide range of applications, low analysis and measurement costs, and improves the exploration and development efficiency of shale oil and gas.

[0008] This invention is achieved through the following technical solution:

[0009] A well logging quantitative characterization method for shale reservoir bedding structures includes the following steps:

[0010] Collect well logging data for the target area;

[0011] The natural gamma ray profile, sonic profile, and deep lateral resistivity profile were calculated and obtained based on well logging data.

[0012] The stratification coefficient curves were obtained by calculating the natural gamma curve, acoustic curve, and deep lateral resistivity curve.

[0013] Filter the bedding coefficient curves and construct bedding structure identification criteria based on the filtered bedding coefficient curves.

[0014] Based on the bedding structure identification criteria, the bedding structure of the target area is determined, and the quantitative characterization of reservoir bedding structure is achieved through well logging.

[0015] Preferably, the logging data includes natural gamma logging data, sonic logging data, and resistivity logging data.

[0016] Preferably, the calculation expression for the natural gamma-ray curve is:

[0017] GR1 i =(GR-AVGR) Ni )*(GR-AVGR Ni )

[0018] In the formula, GR1 i AVGR represents the aberration value of the natural gamma curve with sampling interval i; GR represents the natural gamma curve value with sampling interval i; Ni Let N represent the average value of the natural gamma curve with a window length of N and a sampling interval of i, where N ≥ i.

[0019] Preferably, the calculation expression for the acoustic wave irregular curve is:

[0020] AC1 i =(AC-AVAC) Ni )*(AC-AVAC Ni )

[0021] In the formula, AC1 i AVAC represents the irregular value of the acoustic waveform at a sampling interval of i; AC represents the acoustic waveform value at a sampling interval of i; AVAC Ni This represents the average value of the acoustic waveform with a window length of N and a sampling interval of i, where N ≥ i.

[0022] Preferably, the calculation expression for the deep lateral resistivity profile curve is:

[0023] HLLD1 i =(lgHLLD-lgAVHLLD) Ni )*(lgHLLD-lgAVHLLD Ni )

[0024] In the formula, HLLD1 iHLLD represents the deep lateral resistivity curve irregularity value with a sampling interval of i; AVHLLD represents the deep lateral resistivity curve value with a sampling interval of i. Ni Let N represent the average value of the deep lateral resistivity curve with a window length of N and a sampling interval of i, where N ≥ i.

[0025] Preferably, the calculation expression for the bedding coefficient curve is:

[0026]

[0027] In the formula, STR represents the stratification coefficient;

[0028] GR1 imax GR1 represents the maximum value of the natural gamma-ray curve. imin This represents the minimum value of the natural gamma-ray curve.

[0029] AC1 imax AC1 represents the maximum value of the acoustic wave curve. imin This represents the minimum value of the irregular acoustic wave curve;

[0030] HLLD1 imax HLLD1 represents the maximum value of the deep lateral resistivity profile curve. imin This represents the minimum value of the deep lateral resistivity profile curve.

[0031] Preferably, the filtering of the bedding coefficient curve specifically involves:

[0032] Gaussian filtering is applied to the calculated layering coefficient curves.

[0033] Preferably, the step of constructing a layering structure identification standard based on the filtered layering coefficient curve specifically involves:

[0034] After core description and thin section calibration, the range of bedding coefficient values ​​for different bedding structures is obtained from the filtered bedding coefficient curves.

[0035] Based on the range of bedding coefficient values, a bedding structure identification standard is set.

[0036] Preferably, the criterion for identifying the bedding structure is:

[0037] When the bedding coefficient is >0.25, it is judged as striations;

[0038] When the bedding coefficient is 0.25 or higher and 0.1 or higher, it is determined to be bedding-like.

[0039] When the bedding coefficient is less than 0.1, it is determined to be massive bedding.

[0040] A well logging quantitative characterization system for shale reservoir bedding structures includes:

[0041] The data acquisition module is used to acquire well logging curve data for the target area;

[0042] The well logging curve acquisition module is used to calculate and acquire natural gamma ray curves, sonic curves, and deep lateral resistivity curves based on well logging curve data.

[0043] The bedding coefficient curve acquisition module is used to calculate and obtain the bedding coefficient curve based on the natural gamma curve, acoustic curve, and deep lateral resistivity curve.

[0044] The standard construction module is used to filter the layering coefficient curve and construct a layering structure identification standard based on the filtered layering coefficient curve.

[0045] The bedding structure identification module is used to determine the bedding structure of the target area based on the bedding structure identification criteria, so as to realize the quantitative logging characterization of reservoir bedding structure.

[0046] Compared with the prior art, the present invention has the following beneficial technical effects:

[0047] This invention provides a quantitative logging characterization method for shale reservoir bedding structures. By collecting logging curve data from the target area, including natural gamma ray logging, sonic logging, and resistivity logging data, the corresponding natural gamma ray anomaly curves, sonic anomaly curves, and deep lateral resistivity anomaly curves are calculated. The morphological changes and frequencies of these three types of curves can accurately reflect the bedding structure characteristics of the formation to a certain extent. Furthermore, based on the obtained curves, quantitative characterization parameters for analyzing the bedding structure of shale gas reservoirs are calculated: bedding coefficients. After core description and thin section calibration, the range of bedding coefficient values ​​for different bedding structures is obtained. Since core description and thin section identification can determine the mineral composition, pore structure characteristics, and bedding of the core, this method is particularly useful. Therefore, by using a defined bedding structure, the range of bedding coefficient values ​​can be calibrated, thereby quantitatively constructing a reservoir bedding structure identification standard. Based on the constructed identification standard, further well logging interpretation of the reservoir bedding structure can be carried out, realizing the quantitative characterization of the reservoir bedding structure and judging the quality of the reservoir. The quantitative characterization method described in this invention can accurately and effectively identify the bedding structure of shale reservoirs, not only solving the problem of difficult identification of shale reservoir bedding structures, but also saving the experimental costs of special analysis of shale reservoir bedding structures. The method of identifying shale reservoir bedding structures is low-cost, easy to operate, and easy to promote and apply. The characterization method described in this invention can accurately identify the bedding structure of shale reservoirs, effectively improve the exploration and development efficiency of shale oil and gas, and provide technical reference for the identification of shale reservoir bedding structures. Attached Figure Description

[0048] Figure 1This is a flowchart illustrating the steps of the well logging quantitative characterization method for shale reservoir bedding structure according to the present invention.

[0049] Figure 2 This is a schematic diagram showing the range of values ​​for the stratification coefficient STR1 for different stratification structures in this invention;

[0050] Figure 3 This is a diagram showing the results of the bedding structure identification of well XX in an embodiment of the present invention. Detailed Implementation

[0051] The principles and features of the present invention will be further described in detail below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention. It should be noted that the accompanying drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clearly illustrate the purpose of the embodiments of the present invention.

[0052] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or it can be in a centered component. When a component is said to be "connected to" another component, it can be directly connected to the other component or it may also be in a centered component. When a component is said to be "set to" another component, it can be directly set on the other component or it may also be in a centered component.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0054] This invention provides a well logging quantitative characterization method for the bedding structure of shale reservoirs, such as... Figure 1 As shown, it includes the following steps:

[0055] Collect well logging data for the target area;

[0056] The natural gamma ray profile, sonic profile, and deep lateral resistivity profile were calculated and obtained based on well logging data.

[0057] The stratification coefficient curves were obtained by calculating the natural gamma curve, acoustic curve, and deep lateral resistivity curve.

[0058] Filter the bedding coefficient curves and construct bedding structure identification criteria based on the filtered bedding coefficient curves.

[0059] Based on the bedding structure identification criteria, the bedding structure of the target area is determined, and the quantitative characterization of reservoir bedding structure is achieved through well logging.

[0060] This invention designs a quantitative logging characterization method for shale reservoir bedding structures. By collecting logging curve data from the target area, including natural gamma ray logging, sonic logging, and resistivity logging data, the corresponding natural gamma ray anomaly curves, sonic anomaly curves, and deep lateral resistivity anomaly curves are calculated. The morphological changes and frequencies of these three types of curves can accurately reflect the bedding structure characteristics of the formation to a certain extent. Furthermore, based on the obtained curves, quantitative characterization parameters for analyzing shale gas reservoir bedding structures are calculated: bedding coefficients. After core description and thin-section calibration, the range of bedding coefficient values ​​for different bedding structures is obtained. Since core description and thin-section identification can determine the mineral composition, pore structure characteristics, and bedding of the core, this method is particularly effective. Therefore, by using a defined bedding structure, the range of bedding coefficient values ​​can be calibrated, thereby quantitatively constructing a reservoir bedding structure identification standard. Based on the constructed identification standard, further well logging interpretation of the reservoir bedding structure can be carried out, realizing the quantitative characterization of the reservoir bedding structure and judging the quality of the reservoir. The quantitative characterization method described in this invention can accurately and effectively identify the bedding structure of shale reservoirs, not only solving the problem of difficult identification of shale reservoir bedding structures, but also saving the experimental costs of special analysis of shale reservoir bedding structures. The method of identifying shale reservoir bedding structures is low-cost, easy to operate, and easy to promote and apply. The characterization method described in this invention can accurately identify the bedding structure of shale reservoirs, effectively improve the exploration and development efficiency of shale oil and gas, and provide technical reference for the identification of shale reservoir bedding structures.

[0061] Specifically, the well logging quantitative characterization method for shale reservoir bedding structure described in this invention is implemented as follows:

[0062] (1) Collect conventional logging curve data for the target area;

[0063] Step (1) includes: collecting natural gamma logging curve data, sonic logging curve data, and resistivity logging curve data of the target area.

[0064] The morphological changes and frequencies of natural gamma ray logging data, sonic logging data, and resistivity logging data can, to some extent, reflect the stratigraphic characteristics of the formation.

[0065] The acquisition of natural gamma ray logging data, sonic logging data, and deep lateral resistivity logging data is well known in the art. Those skilled in the art can acquire natural gamma ray logging data, sonic logging data, and resistivity logging data by conventional means in the art, based on the description in this application.

[0066] (2) Using the natural gamma logging curve obtained in step (1), the natural gamma anomaly curve GR1 is obtained using the natural gamma anomaly curve formula. i ;

[0067] The implementation process of step (2) is as follows:

[0068] Using the natural gamma logging curve data obtained in step (1), and applying the natural gamma curve anomaly value formula, the natural gamma anomaly curve GR1 is obtained. i ;

[0069] GR1 i =(GR-AVGR) Ni )*(GR-AVGR Ni )

[0070] In the formula:

[0071] GR1 i AVGR represents the aberration value of the natural gamma curve at a sampling interval of i, where i is typically taken as 0.125m; GR represents the natural gamma curve value at a sampling interval of i; AVGR Ni This represents the average value of the natural gamma curve with a window length of N and a sampling interval of i, where N ≥ i, and N is typically taken as 1m-5m; the natural gamma anisotropy curve GR1 i The above formula can be entered into the Sinolog software and calculated iteratively.

[0072] (3) Using the acoustic logging curve obtained in step (1), the acoustic curve anomaly value formula is used to obtain the acoustic anomaly curve AC1. i ;

[0073] The implementation process of step (3) is as follows:

[0074] Using the acoustic logging curve data obtained in step (1), the acoustic curve anomaly value formula is used to obtain the acoustic anomaly curve AC1. i ;

[0075] AC1 i =(AC-AVAC) Ni )*(AC-AVAC Ni )

[0076] AC1 i The value of the irregular acoustic waveform at a sampling interval of i is represented by _i_, which is typically taken as 0.125m; AC represents the acoustic waveform value at a sampling interval of i; AVAC Ni This represents the average value of the acoustic waveform with a window length of N and a sampling interval of i, where N ≥ i, and N is typically taken as 1m-5m; the acoustic anisotropy curve AC1 i The above formula can be entered into the Sinolog software and calculated iteratively.

[0077] (4) Using the deep lateral resistivity curve obtained in step (1), and applying the formula for the irregular value of the deep lateral resistivity curve, the HLLD irregular deep lateral resistivity curve is obtained. i ;

[0078] The implementation process of step (4) is as follows:

[0079] Using the deep lateral resistivity curve data obtained in step (1), and applying the formula for the irregular value of deep lateral resistivity curves, the deep lateral resistivity curve HLLD is obtained. i ;

[0080] HLLD1 i =(lgHLLD-lgAVHLLD) Ni )*(lgHLLD-lgAVHLLD Ni )

[0081] HLLD1 i HLLD represents the lateral resistivity curve value at a sampling interval of i, where i is typically taken as 0.125 m; HLLD represents the lateral resistivity curve value at a sampling interval of i; AVHLLD Ni This represents the average value of the deep lateral resistivity curve with a window length of N and a sampling interval of i, where N ≥ i, and N is typically taken as 1m-5m; the deep lateral resistivity anisotropy curve HLLD1 i The above formula can be entered into the Sinolog software and calculated iteratively.

[0082] (5) The natural gamma-ray aberration curve GR1 obtained by using steps (2), (3), and (4) i Acoustic wave irregular curve AC1 i and deep lateral resistivity profile HLLD i The stratification coefficient STR is calculated using the stratification coefficient formula.

[0083] The implementation process of step (5) is as follows:

[0084] The natural gamma-ray aberration curve GR1 obtained in step (2) i The acoustic waveform AC1 obtained in step (3) i The deep lateral resistivity profile HLLD1 obtained in step (4) i The stratification coefficient STR is calculated using the following formula:

[0085]

[0086] In the formula:

[0087] STR stands for bedding coefficient, which is a quantitative characterization parameter for the bedding structure of shale gas reservoirs;

[0088] GR1 imax GR1 represents the maximum value of the natural gamma-ray curve. imin This represents the minimum value of the natural gamma-ray curve.

[0089] AC1 imax AC1 represents the maximum value of the acoustic wave curve. imin This represents the minimum value of the irregular acoustic wave curve;

[0090] HLLD1 imax HLLD1 represents the maximum value of the deep lateral resistivity profile curve. imin This represents the minimum value of the deep lateral resistivity profile curve.

[0091] (6) Filter the stratification coefficient curve STR obtained in step (5) to obtain the filtered stratification coefficient curve STR1;

[0092] The implementation process of step (6) is as follows:

[0093] Gaussian filtering is applied to the stratification coefficient curve STR obtained in step (5) to obtain the filtered stratification coefficient curve STR1.

[0094] Gaussian filtering of the stratification coefficient curve STR1 is well known in the field and can be calculated using sinolog software.

[0095] (7) Using the filtered bedding coefficient curve STR1 obtained in step (6), determine the reservoir bedding structure identification criteria;

[0096] The implementation process of step (7) is as follows:

[0097] The filtered bedding coefficient STR1 calculated in step (6) is used to obtain the range of STR1 values ​​for different bedding structures after core description and thin section calibration, and the reservoir bedding structure identification criteria are determined.

[0098] Core description and thin section identification can determine the mineral composition, pore structure characteristics, and bedding structure of the core. The determined bedding structure can be used to define the range of STR1 values ​​and determine the identification criteria for reservoir bedding structure.

[0099] like Figure 2 As shown, when STR1 > 0.25, it is a fibrous layered structure;

[0100] When STR1 is between 0.1 and 0.25, it indicates layered bedding.

[0101] When STR1 < 0.1, it is a massive bedding.

[0102] (8) Using the bedding structure identification criteria obtained in step (7), well logging interpretation of reservoir bedding structures is carried out to achieve quantitative well logging characterization of reservoir bedding structures.

[0103] The implementation process of step (8) is as follows:

[0104] Using the reservoir bedding structure identification criteria obtained in step (7), well logging interpretation of reservoir bedding structures is carried out to achieve quantitative well logging characterization of reservoir bedding structures, thereby judging the quality of the reservoir.

[0105] Example

[0106] This embodiment provides a quantitative characterization method for well logging of shale reservoir bedding structures, such as... Figure 1 As shown, the method includes the following steps:

[0107] Step (1) includes: collecting natural gamma logging curve data, sonic logging curve data, and resistivity logging curve data of the target area.

[0108] The acquisition of natural gamma ray logging data, sonic logging data, and deep lateral resistivity logging data is well known in the art. Those skilled in the art can acquire natural gamma ray logging data, sonic logging data, and resistivity logging data by conventional means in the art, based on the description in this application.

[0109] Step (2): Using the natural gamma logging curve data obtained in step (1), the invented formula is used to obtain the natural gamma anomaly curve GR1. i ;

[0110] GR1 i =(GR-AVGR) Ni )*(GR-AVGR Ni )

[0111] In the formula:

[0112] GR1 i The natural gamma curve aberration value with sampling interval i, where i is 0.125m in this region; GR is the natural gamma curve value with sampling interval 0.125m; AVGR Ni The average value of the natural gamma curve with a window length of 1m and a sampling interval of 0.125m; the natural gamma anisotropy curve GR1 i The formula can be used to calculate the result repeatedly in Sinolog software.

[0113] Step (3): Using the sonic logging curve data obtained in step (1), the invented formula is used to obtain the sonic irregular curve AC1. i ;

[0114] AC1i =(AC-AVAC) Ni )*(AC-AVAC Ni )

[0115] AC1 i The acoustic waveform curve with a sampling interval of i is an irregular value, where i is 0.125m in this region; the acoustic waveform curve value with an AC sampling interval of 0.125m; AVAC Ni The average value of the acoustic waveform with a window length of 1m and a sampling interval of 0.125m; acoustic anisotropy curve AC1 i The formula can be used to calculate the result repeatedly in Sinolog software.

[0116] Step (4): Using the inventive formula, the deep lateral resistivity curve data obtained in step (1) is used to obtain the deep lateral resistivity curve HLLD. i ;

[0117] HLLD1 i =(HLLD-AVHLLD) Ni )*(HLLD-AVHLLD Ni )

[0118] HLLD1 i The sampling interval is i, representing the irregular value of the deep lateral resistivity curve, where i is 0.125 m in this region; HLLD represents the deep lateral resistivity curve value with a sampling interval of 0.125 m; AVHLLD Ni The average value of the deep lateral resistivity curves with a window length of 1m and a sampling interval of 0.125m; the deep lateral resistivity anisotropy curve HLLD1. i The formula can be used to calculate the result repeatedly in Sinolog software.

[0119] Step (5): The natural gamma-ray aberration curve GR1 obtained in steps (2), (3), and (4) is... i Acoustic wave irregular curve AC1 i HLLD1 deep lateral resistivity profile i The stratification coefficient STR is calculated using the following formula:

[0120]

[0121] In the formula:

[0122] STR stands for bedding coefficient, a quantitative characterization parameter for the bedding structure of shale gas reservoirs.

[0123] GR1 imax GR1 is the maximum value of the natural gamma-ray irregular curve. imin This represents the minimum value of the natural gamma-ray curve.

[0124] AC1 imax AC1 represents the maximum value of the acoustic wave curve. imin This represents the minimum value of the irregular acoustic wave curve.

[0125] HLLD1 imax HLLD1 represents the maximum value of the deep lateral resistivity profile curve. imin This represents the minimum value of the deep lateral resistivity profile curve;

[0126] Step (6): Perform Gaussian filtering on the stratification coefficient curve STR obtained in step (5) to obtain the filtered stratification coefficient curve STR1;

[0127] Gaussian filtering of the stratification coefficient curve STR1 is well known in the field and can be calculated using sinolog software.

[0128] Step (7): Using the bedding coefficient STR1 calculated in step (6), the range of STR1 values ​​for different bedding structures is obtained after core description and thin section calibration.

[0129] like Figure 2 The figure shows the range of values ​​for the bedding coefficient STR1 for different bedding structures. From the figure, we can see that:

[0130] When STR1 > 0.25, it is a lamellae pattern;

[0131] When STR1 is between 0.1 and 0.25, it indicates layered bedding.

[0132] When STR1 < 0.1, it is a massive bedding.

[0133] Step (8): Using the bedding structure identification criteria determined in step (7), conduct well logging interpretation of reservoir bedding structures to achieve quantitative well logging characterization of reservoir bedding structures.

[0134] like Figure 3 As shown, the fifth STR1 is the bedding structure identification result of well XX in the study block, which is... Figure 3 As can be seen, the accuracy rate of the data compared with the measured core data in the depth range of 2230m-2254m is as high as 90%. Therefore, the well logging quantitative characterization method for reservoir bedding structure described in this invention can accurately identify the bedding structure of shale reservoirs, effectively improve the exploration and development efficiency of shale oil and gas, and can be promoted and applied to shale gas geological exploration and evaluation in this field, which has guiding and reference significance.

[0135] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any equivalent changes, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A well logging quantitative characterization method for shale reservoir bedding structures, characterized in that, Includes the following steps: Collect well logging data for the target area; The natural gamma ray profile, sonic profile, and deep lateral resistivity profile were calculated and obtained based on well logging data. The stratification coefficient curves were obtained by calculating the natural gamma curve, acoustic curve, and deep lateral resistivity curve. Filter the bedding coefficient curves and construct bedding structure identification criteria based on the filtered bedding coefficient curves. Based on the bedding structure identification criteria, the bedding structure of the target area is determined, and the reservoir bedding structure is quantitatively characterized by well logging. The calculation expression for the natural gamma-ray irregular curve is as follows: In the formula, This represents the aberration value of the natural gamma curve with a sampling interval of i; This represents the natural gamma curve value with a sampling interval of i; Let N represent the average value of the natural gamma curve with a window length of N and a sampling interval of i, where N ≥ i; The calculation expression for the acoustic wave irregular curve is as follows: In the formula, This represents the irregular value of the acoustic waveform with a sampling interval of i; This represents the acoustic waveform value with a sampling interval of i; This represents the average value of the acoustic waveform with a window length of N and a sampling interval of i, where N ≥ i; The calculation expression for the deep lateral resistivity irregular curve is as follows: In the formula, This represents the irregular value of the deep lateral resistivity curve with a sampling interval of i; This represents the deep lateral resistivity curve value with a sampling interval of i; This represents the average value of the deep lateral resistivity curve with a window length of N and a sampling interval of i, where N ≥ i; The calculation expression for the layering coefficient curve is as follows: In the formula, Indicates the stratification coefficient; This represents the maximum value of the natural gamma-ray curve. This represents the minimum value of the natural gamma-ray curve. This represents the maximum value of the irregular acoustic wave curve. This represents the minimum value of the irregular acoustic wave curve; This represents the maximum value of the deep lateral resistivity profile curve. This represents the minimum value of the deep lateral resistivity profile curve; The criteria for identifying the layering structure are as follows: When the bedding coefficient is >0.25, it is judged as striations; When the bedding coefficient is 0.25 or higher and 0.1 or higher, it is determined to be bedding-like. When the bedding coefficient is less than 0.1, it is determined to be massive bedding.

2. The well logging quantitative characterization method for shale reservoir bedding structure according to claim 1, characterized in that, The logging data includes natural gamma logging data, sonic logging data, and resistivity logging data.

3. The well logging quantitative characterization method for shale reservoir bedding structure according to claim 1, characterized in that, The filtering of the layering coefficient curve specifically involves: Gaussian filtering is applied to the calculated layering coefficient curves.

4. The well logging quantitative characterization method for shale reservoir bedding structure according to claim 1, characterized in that, The specific steps for constructing a layering structure identification standard based on the filtered layering coefficient curve are as follows: After core description and thin section calibration, the range of bedding coefficient values ​​for different bedding structures is obtained from the filtered bedding coefficient curves. Based on the range of bedding coefficient values, a bedding structure identification standard is set.

5. A well logging quantitative characterization system for shale reservoir bedding structures, characterized in that, include: The data acquisition module is used to acquire well logging curve data for the target area; The well logging curve acquisition module is used to calculate and acquire natural gamma ray curves, sonic curves, and deep lateral resistivity curves based on well logging curve data. The bedding coefficient curve acquisition module is used to calculate and obtain the bedding coefficient curve based on the natural gamma curve, acoustic curve, and deep lateral resistivity curve. The standard construction module is used to filter the layering coefficient curve and construct a layering structure identification standard based on the filtered layering coefficient curve. The bedding structure identification module is used to determine the bedding structure of the target area based on the bedding structure identification criteria, so as to realize the quantitative characterization of reservoir bedding structure through well logging. The calculation expression for the natural gamma-ray irregular curve is as follows: In the formula, This represents the aberration value of the natural gamma curve with a sampling interval of i; This represents the natural gamma curve value with a sampling interval of i; Let N represent the average value of the natural gamma curve with a window length of N and a sampling interval of i, where N ≥ i; The calculation expression for the acoustic wave irregular curve is as follows: In the formula, This represents the irregular value of the acoustic waveform with a sampling interval of i; This represents the acoustic waveform value with a sampling interval of i; This represents the average value of the acoustic waveform with a window length of N and a sampling interval of i, where N ≥ i; The calculation expression for the deep lateral resistivity irregular curve is as follows: In the formula, This represents the irregular value of the deep lateral resistivity curve with a sampling interval of i; This represents the deep lateral resistivity curve value with a sampling interval of i; This represents the average value of the deep lateral resistivity curve with a window length of N and a sampling interval of i, where N ≥ i; The calculation expression for the layering coefficient curve is as follows: In the formula, Indicates the stratification coefficient; This represents the maximum value of the natural gamma-ray curve. This represents the minimum value of the natural gamma-ray curve. This represents the maximum value of the irregular acoustic wave curve. This represents the minimum value of the irregular acoustic wave curve; This represents the maximum value of the deep lateral resistivity profile curve. This represents the minimum value of the deep lateral resistivity profile curve; The criteria for identifying the layering structure are as follows: When the bedding coefficient is >0.25, it is judged as striations; When the bedding coefficient is 0.25 or higher and 0.1 or higher, it is determined to be bedding-like. When the bedding coefficient is less than 0.1, it is determined to be massive bedding.

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