A method, medium and device for predicting horizontal thread density of a marine shale core

Abstract

The present application provides a kind of marine shale core horizontal fracture line density prediction method, medium and device, the method comprises the following steps: S100: core horizontal fracture description, obtains the whole well section core horizontal fracture line density data;S200: the core depth of whole well section core horizontal fracture line density data is corrected to logging depth, obtains corrected core horizontal fracture line density data;S300: corrected core horizontal fracture line density data and shale gas conventional logging curve are analyzed, obtains several logging curves;S400: several logging curves obtained in step S300 are used to establish horizontal fracture line density prediction model;S500: marine shale core horizontal fracture line density prediction is carried out using horizontal fracture line density prediction model.The present application can effectively realize the rapid prediction of the horizontal fracture line density of the evaluation well in the model block or the well around the core / taking core.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and more specifically, to a method, medium, and apparatus for predicting the density of horizontal fracture lines in marine shale cores. Background Technology

[0002] With the deepening of unconventional oil and gas exploration and development, marine shale gas has received increasing attention and has gradually emerged as an important natural gas resource. In recent years, with the continuous improvement and perfection of the main processes for marine shale gas development in southern my country, large-scale and efficient shale gas development has been achieved, making shale gas the most realistic direction and field for my country to achieve a substantial increase in natural gas production.

[0003] Shale gas reservoirs are artificially modified gas reservoirs, meaning they require extensive fracturing and artificial fracture networks to achieve large-scale, efficient development. The successful and efficient extraction of shale gas benefits from numerous natural fractures and artificial fracture networks. A complex fracture system is a prerequisite for increased shale gas production and an engineering indicator for selecting sweet spot zones in hydraulic fracturing. Practical experience in shale gas development in the Wufeng Formation–Longmaxi Formation of the Sichuan Basin shows that, on the one hand, natural fractures can effectively promote shale gas enrichment, desorption, diffusion, seepage, and production (Wu Jianfa, et al., Characteristics of fracture development in organic-rich shale of the Longmaxi Formation in Changning, southern Sichuan, and its relationship with gas content, Acta Petrolei Sinica, 2021, Vol.42, No.4); on the other hand, horizontal fractures will affect the extension of the artificial fracture network. The artificial hydraulic fracture network will preferentially extend horizontally along the direction of the horizontal fractures, weakening the hydraulic fracture energy and affecting the height of the artificially modified fracture network. Horizontal fractures in core samples are natural fractures that penetrate the core and whose dip angle is less than 15° to the formation horizontal plane (Wang Xingmeng et al., Characteristics of Natural Fractures in Shale and Their Controlling Effect on Shale Gas Accumulation and Development, Science, Technology and Engineering, 2018, Vol.18, No.8). These fractures occur in a still-water depositional environment during shale sedimentation, where the sedimentary shale exhibits distinct laminae, and the plane containing these laminae is the formation horizontal plane. Current monitoring of artificially engineered fracture networks in southern Sichuan shale gas shows that the fracture network extension height is typically within 30m, failing to fully utilize and develop the main development layer, the Wufeng Formation to the Longyi 1 sub-section (mainly 40m-70m). Currently, detailed evaluation of horizontal fractures in geology mainly relies on detailed artificial descriptions of core samples and determination of the vertical distribution density of individual wells. This evaluation method requires fixed labor costs, has low efficiency, and cannot quantitatively evaluate well sections without core sampling.

[0004] Therefore, there is an urgent need for a method to predict the horizontal fracture density of shale cores that can reduce costs and improve efficiency, so as to achieve quantitative evaluation of cored and uncored sections, thereby facilitating the fine evaluation of shale reservoirs and the differentiated design of fracturing construction parameters. Summary of the Invention

[0005] This invention aims to provide a method, medium, and device for predicting the density of horizontal fractures in marine shale cores, in order to solve the problems of low efficiency in the existing manual description of natural fractures in shale cores and the inability to evaluate uncored segments.

[0006] This invention provides a method for predicting the density of horizontal fracture lines in marine shale cores, comprising the following steps:

[0007] S100: Description of horizontal fractures in the core sample well, obtaining linear density data of horizontal fractures throughout the entire well section;

[0008] S200: Corrects the core depth of the horizontal fracture density data of the entire well section to the logging depth to obtain the corrected core horizontal fracture density data;

[0009] S300: Correlation analysis is performed between the corrected core horizontal fracture density data and the conventional shale gas logging curves to obtain several logging curves;

[0010] S400: Establish a horizontal fracture density prediction model using several logging curves obtained in step S300;

[0011] S500: Horizontal suture density prediction model is used to predict the horizontal suture density of marine shale cores.

[0012] Furthermore, in step S100, it is necessary to determine the depth interval of the horizontal fracture description based on the formation thickness, and then perform a statistical analysis of the horizontal fracture density in the core of the entire well section to obtain the horizontal fracture density data of the core of the entire well section.

[0013] Furthermore, the horizontal fracture density data of the entire well section core obtained in step S100 needs to be normalized. The normalization formula is as follows:

[0014]

[0015] In the formula, FS i The normalized horizontal fracture density of the entire well core section, unit: fractures / Bm; F i The density of horizontal seams at a certain depth, per seam (L); i For the description of F i The corresponding core length is in meters (m); B is the normalized core length in meters (m).

[0016] Further, step S200 includes the following sub-steps:

[0017] S201, core marker layer selected;

[0018] S202, Determine the correction depth for horizontal suture density data:

[0019] D j =D c-D y

[0020] In the formula, D j Correction depth for horizontal suture density data, in meters; D c D represents the logging depth of the core marker layer, in meters. y The core depth of the core marker layer is expressed in meters.

[0021] S203 uses horizontal fracture density data to correct the core depth of the horizontal fracture density data of the entire well section to the logging depth, and obtains corrected core horizontal fracture density data.

[0022] Furthermore, the horizontal suture density data correction depth D j Possibly positive or negative:

[0023] When the horizontal suture density data is corrected to depth D j When the value is positive, the core depth correction of the horizontal fracture density data of the entire well section to the logging depth requires adding the horizontal fracture density data correction depth D to the horizontal fracture density data of the entire well section. j ;

[0024] When the horizontal suture density data is corrected to depth D j When the value is negative, the core depth correction for the horizontal fracture density data of the entire well section to the logging depth requires subtracting the horizontal fracture density data correction depth D from the total core horizontal fracture density data. j .

[0025] Further, step S300 includes the following sub-steps:

[0026] S301, Correlation analysis was performed between the corrected core horizontal fracture density data obtained from core descriptions of core wells in the same structural location and the conventional logging curves of shale gas.

[0027] S302 is a module for acquiring input data of corrected core horizontal fracture density data and 8 conventional shale gas logging curves.

[0028] S303, using computer terminal equipment, the Pearson correlation coefficient analysis was performed on the horizontal fracture density data of the corrected core obtained by the data acquisition module and 8 conventional shale gas logging curves to obtain the correlation coefficient R between the horizontal fracture distribution density of the same structural part in the same study block and the 8 logging curves;

[0029] S304. Select several logging curves based on the correlation coefficient R to establish a horizontal fracture density prediction model in step S400.

[0030] Furthermore, the method for selecting the natural gamma curve, wellbore curve, density curve, and shallow resistivity curve based on the correlation coefficient R in step S304 is as follows:

[0031] The logging curve with |R|>X is selected for use in step S400 to establish the horizontal fracture density prediction model; where X is the set threshold of the correlation coefficient R.

[0032] When selecting logging curves based on |R|, for natural gamma curves and uranium-free gamma curves, as well as shallow resistivity curves and deep resistivity curves, since the two curves in each type represent the same meaning, the two curves with higher correlation are selected to establish the horizontal fracture density prediction model; that is, among natural gamma curves and uranium-free gamma curves, at most one of these two curves participates in establishing the horizontal fracture density prediction model; among shallow resistivity curves and deep resistivity curves, at most one of these two curves can participate in establishing the horizontal fracture density prediction model.

[0033] Furthermore, the horizontal suture density prediction model established in step S400 is as follows:

[0034] FS i =a0+a1*Q1 i +a2*Q2 i +a3*Q3 i +…+a n *Qn i

[0035] Among them, Q1 i Q2 i Q3 i ...,Qn i These are the logging curves selected in step S300; a0, a1, a2, a3, ..., a n The coefficients of the horizontal suture density prediction model are constants determined by the data fitting results, namely a0, a1, a2, a3, ..., a n The range of values ​​for is any real number, n≤6.

[0036] The present invention also provides a computer terminal storage medium storing computer terminal executable instructions, characterized in that the computer terminal executable instructions are used to execute the above-described method for predicting the horizontal fracture density of marine shale cores.

[0037] The present invention also provides a computing device, comprising:

[0038] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method for predicting the horizontal suture density of marine shale cores as described above.

[0039] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0040] This invention describes the horizontal fracture density observed in existing shale gas well cores. By correcting for core depth and logging depth, it obtains the actual horizontal fracture density corresponding to the logging curve at a certain depth. It then performs correlation analysis between the horizontal fracture density and the logging curve, selecting strongly correlated logging curves for the establishment of a prediction model. The established prediction model for horizontal fracture density in the same structural location or around the well can effectively and rapidly predict the horizontal fracture density of cores from evaluation wells (both cored and uncorked) and horizontal wells within the model block. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of a method for predicting the horizontal fracture density of marine shale cores provided in an embodiment of this application.

[0043] Figure 2 This is a map showing the distribution characteristics of horizontal fractures in a marine shale core, provided in an embodiment of this application.

[0044] Figure 3 This is a fitting diagram of the predicted and actual observed values ​​of horizontal fracture density in a marine shale core, provided in an embodiment of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0046] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0047] Example

[0048] like Figure 1 As shown in the figure, this embodiment proposes a method for predicting the density of horizontal fracture lines in marine shale cores, including the following steps:

[0049] S100: Description of horizontal fractures in the core sample from the core well, obtaining linear density data of horizontal fractures throughout the entire well section; details are as follows:

[0050] The horizontal fractures in typical core samples from the study block are described as follows:

[0051] First, the core sample needs to be placed and wiped clean; such as Figure 2 The image shows the development characteristics of horizontal fractures in Block A. The horizontal fractures can be seen to run through the entire core and are partially filled with bright calcite.

[0052] Secondly, the depth interval for describing horizontal fractures needs to be determined based on the formation thickness, and then the horizontal fracture linear density of the core sample throughout the well needs to be statistically analyzed to obtain the horizontal fracture linear density data for the entire well. That is, to make the core linear density data more comparable vertically, the thickness of the described sub-layers needs to be considered when determining the depth interval for describing horizontal fractures. For example, the thinnest layer in Block A is 1.5–10 m thick. To ensure that there are at least two observation data points within each sub-layer and that the data is more representative, the horizontal fracture description interval for Block A is determined to be 0.5–5 m. Based on the 0.5 m depth interval, the description depth range can be appropriately widened to 5 m according to the sub-layer thickness. At the same time, the normalized depth range takes the smallest description depth interval, i.e., 0.5 m for B. Therefore, the obtained horizontal fracture linear density data for the entire well needs to be normalized. The normalization formula is as follows:

[0053]

[0054] In the formula, FS i The normalized horizontal fracture density of the entire well core section, unit: fractures / Bm; F i The density of horizontal seams at a certain depth, per seam (L); i For the description of F i The corresponding core length is in meters (m); B is the normalized core length in meters (m).

[0055] S200: Corrects the core depth of the horizontal fracture density data of the entire well section to the logging depth to obtain the corrected core horizontal fracture density data;

[0056] Oil and gas exploration and development considers well depth data to be more accurate than core depth logging. Therefore, it is necessary to correct the core depth of horizontal fracture density data for the entire well section to the logging depth (usually 0 to ±5m) to facilitate subsequent correlation calculations and analysis of horizontal fracture density prediction models. The specific details are as follows:

[0057] S201, core marker layer selected;

[0058] In this embodiment, the main shale gas development section of the Wufeng Formation to Longmaxi Formation in the Sichuan Basin is the bottom Wufeng Formation to Longyi 1 sub-section. The top of the Wufeng Formation has a 0.1 to 2m Guanyin Bridge. The core is shell-bearing marl, and the core features are obvious and easy to identify. Therefore, the top of the Wufeng Formation is selected as the core marker layer.

[0059] S202, Determine the correction depth for horizontal suture density data:

[0060] D j =D c -D y

[0061] In the formula, D j Correction depth for horizontal suture density data, in meters; D c The logging depth for the core marker layer (top of the Wufeng Formation in this example) is in meters; D y The core depth of the core marker layer (top of the Wufeng Formation in this example) is in meters. Horizontal suture density data correction depth D. j Possibly positive or negative:

[0062] When the horizontal suture density data is corrected to depth D j When the value is positive, the core depth correction of the horizontal fracture density data of the entire well section to the logging depth requires adding the horizontal fracture density data correction depth D to the horizontal fracture density data of the entire well section. j ;

[0063] When the horizontal suture density data is corrected to depth D j When the value is negative, the core depth correction for the horizontal fracture density data of the entire well section to the logging depth requires subtracting the horizontal fracture density data correction depth D from the total core horizontal fracture density data. j .

[0064] Therefore, for the typical well HA-1 in Block A, the core depth at the top of the Wufeng Formation (D) c The logging depth at the top of the Wufeng Formation is 3099.8m. yThe depth of the horizontal suture density data correction is 4103.5m, therefore the correction depth D is... j =4103.5-3099.8=3.7m. That is, the core depth of the horizontal fracture density data described by the HA-1 well core needs to be increased by 3.7m to correct to the accurate logging depth.

[0065] S203, using the horizontal fracture density data to correct the depth, the core depth of the horizontal fracture density data of the entire well section was corrected to the logging depth, and the corrected core horizontal fracture density data was obtained, as shown in Table 1.

[0066] Table 1. Depth Correction Data for Horizontal Fracture Density in Typical Sections of Well HA-1:

[0067]

[0068] S300: Correlation analysis was performed between the corrected core horizontal fracture density data and conventional shale gas logging curves to obtain several logging curves; details are as follows:

[0069] S301, the development of natural fractures in different structural locations (anticlines, slopes, synclines) has different characteristics. Therefore, the correlation analysis was performed between the corrected core horizontal fracture density data obtained from core descriptions of core wells taken from the same structural location and the conventional logging curves of shale gas.

[0070] S302 is a module for acquiring data from the corrected core horizontal fracture density data and eight conventional shale gas logging curves. The eight conventional shale gas logging curves include the natural gamma curve GR, the uranium-free gamma curve KTH, the caliber curve CAL, the sonic curve AC, the neutron curve CNL, the density curve DEN, the shallow resistivity curve RXO, and the deep resistivity curve RT.

[0071] S303, using computer terminal equipment, the Pearson correlation coefficient analysis was performed on the horizontal fracture density data of the corrected core obtained by the data acquisition module and 8 conventional shale gas logging curves to obtain the correlation coefficient R between the horizontal fracture distribution density of the same structural part in the same study block and the 8 logging curves;

[0072] in,

[0073] S304, Based on the correlation coefficient R, select several logging curves for use in step S400 to establish a horizontal fracture density prediction model. Wherein:

[0074] (1) The numerical range of the correlation coefficient R is generally between -1 and +1; the closer the absolute value of the correlation coefficient R, |R|, is to 1, the stronger the linear relationship between the variables; the closer the absolute value of |R| is to 0, the weaker the linear relationship between the variables; when |R| < X, it is a weak correlation; then the logging curve with |R| > X is selected for the establishment of the horizontal fracture density prediction model in step S400; where X is the set threshold of the correlation coefficient R, and in this embodiment, X = 0.3 is taken.

[0075] (2) When selecting logging curves based on |R|, for the natural gamma curve GR and the uranium-free gamma curve KTH, as well as the shallow resistivity curve RXO and the deep resistivity curve RT, since the two curves in each curve type represent the same meaning, the two curves with higher correlation are selected to establish the horizontal fracture density prediction model; that is, in the natural gamma curve GR and the uranium-free gamma curve KTH, at most one of these two curves participates in establishing the horizontal fracture density prediction model; in the shallow resistivity curve RXO and the deep resistivity curve RT, at most one of these two curves can participate in establishing the horizontal fracture density prediction model.

[0076] In this embodiment, the horizontal fracture density and Pearson correlation coefficient of the logging curves of 15 sample points from 2 horizontal wells in the synclinal zone of Block A are shown in Table 2.

[0077] Table 2. Statistical table of correlation coefficients between horizontal fracture density and well logging curves in the synclinal zone of Block A:

[0078]

[0079] As shown in Table 2, both the natural gamma ray curve GR and the uranium-free gamma ray curve KTH have |R| greater than 0.3, but the natural gamma ray curve GR has a better correlation. Therefore, the natural gamma ray curve GR is preferred as the model fitting gamma ray curve. Similarly, both the shallow resistivity curve RXO and the deep resistivity curve RT have |R| greater than 0.3, but the shallow resistivity curve RXO has a better correlation. Therefore, the shallow resistivity curve RXO is preferred as the model fitting resistivity curve. Finally, the four logging curves—natural gamma ray curve GR, wellbore diameter curve CAL, density curve DEN, and shallow resistivity curve RXO—are selected for establishing the horizontal fracture density prediction model in step S400.

[0080] S400: Establish a horizontal fracture density prediction model using several logging curves obtained in step S300; details are as follows:

[0081] Based on the horizontal fracture density (FS) data from the core and the values ​​of four logging curves—natural gamma ray curve (GR), borehole caliper curve (CAL), density curve (DEN), and shallow resistivity curve (RXO)—multivariate regression analysis was used to establish the horizontal fracture density (FS) of the shale cores in the synclinal zone of Block A. iThe prediction model, specifically Formula 2, is as follows:

[0082] FS i =a0+a1*GR i +a2*CAL i +a3*DEN i +a4*RXO i

[0083] Among them, GR i This represents the natural gamma rays of rocks at a certain depth, measured in API (Advanced Permeability) or CAL (Cal). i DEN represents the diameter of a rock well at a certain depth, in meters. i This indicates the density of rock at a certain depth, expressed in g / cm³. 3 RXO i The value represents the rock resistivity at a certain depth, in Ω·m; a0, a1, a2, a3, and a4 are the coefficients of the horizontal suture density prediction model, which are constants determined by the data fitting results. The values ​​of a0, a1, a2, a3, and a4 can be any real number.

[0084] Based on the statistical data of 15 sets of horizontal fracture density from 2 horizontal wells in Block A, the horizontal fracture density (FS) of the well cores in Block A was finally established through data fitting and regression. i The prediction model, specifically the formula FS, is as follows. i = -86.4513 + 0.0350 * GR i +13.3893*CAL i -13.4893*DEN i +0.0502*RXO i .

[0085] S500: The horizontal fracture density prediction model obtained in step S400 can be used to predict the horizontal fracture density of marine shale cores. Horizontal fracture density (FS) of cores in the synclinal zone of well A. i The absolute value of the correlation coefficient of the prediction model, |R|, is 0.95, and the squared correlation coefficient, R0, is also 0.95. 2 It is 0.91, such as Figure 3 As shown, it can achieve accurate and rapid prediction of the horizontal fracture density of uncorked shale gas appraisal wells and horizontal wells in Block A.

[0086] Furthermore, in some embodiments, a computer terminal storage medium is proposed, storing computer terminal executable instructions for executing the method for predicting the horizontal fracture density of marine shale cores as described in the preceding embodiments. Examples of computer storage media include magnetic storage media (e.g., floppy disks, hard disks, etc.), optical recording media (e.g., CD-ROMs, DVDs, etc.), or memory such as memory cards, ROMs, or RAMs. The computer storage medium can also be distributed across a network-connected computer system, for example, as an application store.

[0087] Furthermore, in some embodiments, a computing device is proposed, comprising: at least one processor; and a memory communicatively connected to said at least one processor; wherein the memory stores instructions executable by said at least one processor, said instructions being executed by said at least one processor to enable said at least one processor to perform the method for predicting the horizontal suture density of marine shale cores as described in the foregoing embodiments. Examples of computing devices include PCs, tablets, smartphones, or PDAs.

[0088] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the density of horizontal fracture lines in marine shale cores, characterized in that, Includes the following steps: S100: Description of horizontal fractures in the core sample well, obtaining linear density data of horizontal fractures throughout the entire well section; S200: Corrects the core depth of the horizontal fracture density data of the entire well section to the logging depth to obtain the corrected core horizontal fracture density data; S300: Correlation analysis is performed between the corrected core horizontal fracture density data and the conventional shale gas logging curves to obtain several logging curves; S400: Establish a horizontal fracture density prediction model using several logging curves obtained in step S300; S500: Horizontal suture density prediction model is used to predict the horizontal suture density of marine shale cores. Step S200 includes the following sub-steps: S201, core marker layer selected; S202, Determine the correction depth for horizontal suture density data: In the formula, The correction depth for horizontal suture density data, in meters; The depth of the core marker layer is measured in meters. The core depth of the core marker layer is expressed in meters. S203, using horizontal fracture density data to correct the core depth of the horizontal fracture density data of the entire well section to the logging depth, and obtain the corrected core horizontal fracture density data. The horizontal suture density prediction model established in step S400 is as follows: in, , , … These are the logging curves selected in step S300; a0, a1, a2, a3, ..., a n The coefficients of the horizontal suture density prediction model are constants determined by the data fitting results, namely a0, a1, a2, a3, ..., a n The range of values ​​for is any real number, n≤6.

2. The method for predicting the density of horizontal fracture lines in marine shale cores according to claim 1, characterized in that, In step S100, the depth interval of the horizontal fracture description needs to be determined based on the formation thickness, and then the horizontal fracture density of the core sample throughout the well section is statistically analyzed to obtain the horizontal fracture density data of the core sample throughout the well section.

3. The method for predicting the density of horizontal fracture lines in marine shale cores according to claim 2, characterized in that, The horizontal fracture density data of the entire well section core obtained in step S100 needs to be normalized. The normalization formula is as follows: In the formula, This represents the normalized horizontal fracture density of the entire well core section, expressed in units of fractures / Bm. The density of horizontal seams at a certain depth, in units of seams / Lm; For the description The corresponding core length, in meters; B represents the normalized core length, in meters (m).

4. The method for predicting the density of horizontal fracture lines in marine shale cores according to claim 3, characterized in that, Horizontal suture density data correction depth Possibly positive or negative: When horizontal suture density data is corrected for depth When the value is positive, the core depth correction of the horizontal fracture density data of the entire well section to the logging depth requires adding the horizontal fracture density data correction depth to the entire well section core horizontal fracture density data. ; When horizontal suture density data is corrected for depth When the value is negative, the core depth correction of the horizontal fracture density data of the entire well section to the logging depth requires subtracting the correction depth of the horizontal fracture density data from the entire well section core horizontal fracture density data. .

5. The method for predicting the density of horizontal fracture lines in marine shale cores according to claim 3, characterized in that, Step S300 includes the following sub-steps: S301, Correlation analysis was performed between the corrected core horizontal fracture density data obtained from core descriptions of core wells in the same structural location and the conventional logging curves of shale gas. S302 is a module for acquiring input data of corrected core horizontal fracture density data and 8 conventional shale gas logging curves. S303, using computer terminal equipment, the Pearson correlation coefficient analysis was performed on the horizontal fracture density data of the corrected core obtained by the data acquisition module and 8 conventional shale gas logging curves to obtain the correlation coefficient R between the horizontal fracture distribution density of the same structural part in the same study block and the 8 logging curves; S304. Select several logging curves based on the correlation coefficient R to establish a horizontal fracture density prediction model in step S400.

6. The method for predicting the density of horizontal fracture lines in marine shale cores according to claim 5, characterized in that, The method for selecting the natural gamma curve, wellbore diameter curve, density curve, and shallow resistivity curve based on the correlation coefficient R in step S304 is as follows: The logging curve with |R|>X is selected for use in step S400 to establish the horizontal fracture density prediction model; where X is the set threshold of the correlation coefficient R. When selecting logging curves based on |R|, for natural gamma curves and uranium-free gamma curves, as well as shallow resistivity curves and deep resistivity curves, since the two curves in each type represent the same meaning, the two curves with higher correlation are selected to establish the horizontal fracture density prediction model; that is, among natural gamma curves and uranium-free gamma curves, at most one of these two curves participates in establishing the horizontal fracture density prediction model; among shallow resistivity curves and deep resistivity curves, at most one of these two curves can participate in establishing the horizontal fracture density prediction model.

7. A computer terminal storage medium storing computer terminal executable instructions, characterized in that, The computer terminal can execute instructions for performing the method for predicting the horizontal fracture density of marine shale cores as described in any one of claims 1-6.

8. A computing device, characterized in that, include: At least one processor; The system also includes a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for predicting the horizontal suture density of marine shale cores as described in any one of claims 1-6.

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