Integral-based method and device for predicting productivity of deep shale gas reservoir

Abstract

The application discloses a kind of integral-based deep shale gas reservoir productivity prediction method and device, the method takes horizontal well as research object, with the top depth and bottom depth of actual fracturing length as boundary, using integral equation principle, the integral result of parameter in this length range is obtained, to represent the contribution of this parameter, build reservoir gas index, and based on the fracturing liquid intensity and sand intensity reflecting reservoir engineering quality, build reservoir fracturing reconstruction index;Based on reservoir gas index and reservoir fracturing reconstruction index, build deep shale gas reservoir horizontal well productivity prediction model.The present application avoids the error evaluation that may occur in productivity prediction due to the use of arithmetic mean, more fine, accurate and reasonable representation of the contribution of each parameter of reservoir to productivity, and the productivity prediction accuracy is further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil exploration and development, in particular to a deep shale gas reservoir productivity prediction method and device based on integration. BACKGROUND

[0002] As a clean and efficient unconventional natural gas resource, shale gas has important significance for improving China's energy structure and ensuring national energy security. As a low-porosity and low-permeability reservoir, horizontal well and volume fracturing have become important technical means for developing shale gas reservoirs. Productivity, as a direct representation of the evaluation of development effect, accurate prediction of productivity is of great significance for evaluating development effect, shale gas development plan design and optimizing engineering reconstruction scheme. At present, the methods for predicting the productivity of shale gas mainly include analogy experience method, analytical method and numerical method. Analytical method mainly describes the fluid state under different conditions through physical model. Numerical method is widely used, mainly including finite difference method and finite element method. The analogy experience method is most widely used in oilfield field application, which can directly guide oilfield development design. It mainly selects the parameter with the highest correlation with the test productivity under the premise of obtaining the factors affecting the productivity, and uses regression or neural network method for prediction. In the existing productivity prediction methods, the model calculated by the analytical method is idealized. In the actual development process, due to the heterogeneity and uncertainty of fracturing reconstruction, the prediction accuracy of productivity is low. The model parameters designed by numerical simulation method need to be adjusted manually, which has certain subjectivity in this process, and the prediction accuracy of productivity is not guaranteed. Therefore, in the field application, the analogy experience method is most widely used. However, in the current method, when selecting the main parameters affecting the productivity, the average value of a certain interval is usually taken as the representative for parameter modeling. However, the average value of the parameter may not represent the quality of the parameter, so the importance of the parameter in the influence of the productivity may be incorrectly evaluated. SUMMARY

[0003] The present application provides a deep shale gas reservoir productivity prediction method and device based on integration, which takes horizontal well as the research object, takes the top depth and the bottom depth of the actual fracturing length as the boundary, uses the integral equation principle to obtain the integral result of the parameters in this length range, takes the contribution of the parameter as the representative, and performs reservoir quality modeling analysis. Meanwhile, the geological quality and engineering quality of the reservoir are comprehensively considered to establish a deep shale gas productivity prediction model.

[0004] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0005] The present application provides a deep shale gas reservoir productivity prediction method based on integration, which comprises the following steps:

[0006] Based on the organic carbon curve, the porosity curve and the gas content curve of each horizontal well in the deep shale gas reservoir in the research area, the top depth and the bottom depth of the fracturing reconstruction well section of the horizontal well are taken as the integral interval, and the integral results of the organic carbon, the porosity and the gas content of each horizontal well are calculated in an integral manner;

[0007] The weight coefficients of the integral results of the organic carbon, the porosity and the gas content in the reservoir quality evaluation are obtained, and a reservoir gas-bearing index is constructed;

[0008] The reservoir fracturing reconstruction index is constructed based on the fracturing fluid intensity and the sand intensity reflecting the engineering quality of the reservoir;

[0009] Based on the reservoir gas-bearing index and the reservoir fracturing reconstruction index, a horizontal well productivity prediction model of the deep shale gas reservoir is constructed;

[0010] The productivity prediction model is used to predict the productivity of the target well in the deep shale gas reservoir.

[0011] Further, the method further comprises:

[0012] The geological parameters and the engineering parameters of each horizontal well in the deep shale gas reservoir in the research area are collected in advance; the geological parameters include organic carbon, porosity, gas content, I-class reservoir continuous thickness and formation pressure coefficient, and the engineering parameters include I-class reservoir thickness, fracturing fluid intensity and sand intensity.

[0013] Further, the integral results of the organic carbon, the porosity and the gas content of each horizontal well are calculated in an integral manner, comprising:

[0014]

[0015]

[0016]

[0017] Wherein, S TOC , S POR , S GAS are the integral results of the organic carbon, the porosity and the gas content respectively; TOC, POR and GAS are the collected organic carbon curve, the porosity curve and the gas content curve respectively; STDP is the depth of the top boundary of the perforation; ETDP is the depth of the bottom boundary of the perforation.

[0018] Further, the weight coefficients of the integral results of the organic carbon, the porosity and the gas content in the reservoir quality evaluation are obtained, comprising:

[0019] According to the importance of the organic carbon integral result, the porosity integral result and the gas content integral result, the analytic hierarchy process is used to obtain the weight coefficients of the organic carbon integral result, the porosity integral result and the gas content integral result in the reservoir quality evaluation.

[0020] Further, the reservoir gas content index is constructed, including:

[0021] Ig=(R TOC ×S TOC +R POR ×S POR +R GAS ×S GAS )×h×Kf;

[0022] Wherein, Ig is the reservoir gas content index; R TOC , R POR , R GAS are the organic matter weight coefficient, the porosity weight coefficient and the gas content weight coefficient respectively; h is the continuous thickness of the I-type reservoir; Kf is the formation pressure coefficient.

[0023] Further, the reservoir fracturing reconstruction index is constructed based on the fracturing fluid intensity and the sand intensity reflecting the reservoir engineering quality, including:

[0024] The fracturing fluid intensity and the sand intensity reflecting the reservoir engineering quality are normalized as follows:

[0025]

[0026]

[0027] Wherein, NVF is the normalized fluid intensity; NVS is the normalized sand intensity; VF is the actual fracturing fluid intensity; VF MIN is the minimum value of the regional fluid intensity; VF MAX is the maximum value of the regional fluid intensity; VS is the actual fracturing sand intensity; VS MAX is the maximum value of the regional sand intensity; VS MIN is the minimum value of the regional sand intensity.

[0028] The reservoir fracturing reconstruction index is constructed based on the normalized fluid intensity and the normalized sand intensity as follows:

[0029] FI=(NVF+NVS)×L;

[0030] Wherein, FI is the reservoir fracturing reconstruction index, and L is the length of the horizontal section of the I-type reservoir.

[0031] Further, the deep shale gas reservoir horizontal well productivity prediction model is constructed based on the reservoir gas content index and the reservoir fracturing reconstruction index, including:

[0032] The horizontal well productivity prediction model of the deep shale gas reservoir is:

[0033] AOFg=a*Ig+b*FI+c;

[0034] In the formula, AOFg is the open flow capacity of the horizontal well; a, b and c are fitting coefficients;

[0035] Based on the collected geological parameters and engineering parameters of each horizontal well of the deep shale gas reservoir in the research area, the fitting coefficients a, b and c are obtained by fitting the prediction model by using the least square method.

[0036] The application further provides a deep shale gas reservoir productivity prediction device based on integration, which is used for realizing the deep shale gas reservoir productivity prediction method based on integration.

[0037] The integral module is used for calculating the organic carbon integral result, the porosity integral result and the gas content integral result of each horizontal well by taking the top depth and the bottom depth of the fracturing reconstruction well section of the horizontal well as the integral interval based on the organic carbon curve, the porosity curve and the gas content curve of each horizontal well of the deep shale gas reservoir in the research area.

[0038] The first calculation module is used for obtaining the weight coefficients of the organic carbon integral result, the porosity integral result and the gas content integral result in the reservoir quality evaluation, and constructing a reservoir gas-bearing index.

[0039] The second calculation module is used for constructing a reservoir fracturing reconstruction index based on the fracturing fluid intensity and the sand intensity reflecting the engineering quality of the reservoir.

[0040] The model construction module is used for constructing a horizontal well productivity prediction model of the deep shale gas reservoir based on the reservoir gas-bearing index and the reservoir fracturing reconstruction index.

[0041] The prediction module is used for predicting the productivity of the target well of the deep shale gas reservoir by using the productivity prediction model.

[0042] The application has the following beneficial effects:

[0043] Compared with the prior art, the application changes the defects of the prior method of evaluating the reservoir productivity by using the arithmetic mean value, and no longer relies on the arithmetic mean value of the parameters, but uses the integral result of the reservoir parameters in the fracturing reconstruction range for analysis, which well avoids the wrong evaluation of the productivity prediction caused by using the arithmetic mean value, and more finely, accurately and reasonably represents the contribution of each parameter of the reservoir to the productivity, so that the productivity prediction accuracy is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 A deep shale gas reservoir productivity prediction method based on integration provided by the present application is shown in the flow chart;

[0045] Figure 2 The test productivity and the predicted productivity are compared for the embodiment of the present application. DETAILED DESCRIPTION

[0046] The present application is further described below. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and cannot be used to limit the protection scope of the present application.

[0047] The present application provides a deep shale gas reservoir productivity prediction method based on integration, as shown in Figure 1 , comprising the following steps:

[0048] S1, collecting geological parameters and engineering parameters of deep shale gas reservoirs in the study area; wherein the geological parameters include organic carbon, porosity, gas content, I-class reservoir continuous thickness and formation pressure coefficient of each horizontal well, and the engineering parameters include I-class reservoir thickness, fracturing liquid intensity and sanding intensity of each horizontal well;

[0049] S2, taking the top depth and the bottom depth of the fracturing reconstruction well section of the horizontal well as the integral interval, and using the integral method to calculate the organic carbon integral result S TOC , the porosity integral result S POR and the gas content integral result S GAS of each horizontal well;

[0050] S3, using the analytic hierarchy process to obtain the weight coefficients of S TOC , S POR and S GAS in the reservoir quality evaluation, and constructing a reservoir gas-bearing index;

[0051] S4, normalizing the fracturing liquid intensity and the sanding intensity reflecting the engineering quality of the reservoir to obtain the normalized liquid intensity and the sanding intensity, and constructing a reservoir fracturing reconstruction index;

[0052] S5, based on the reservoir gas-bearing index and the reservoir fracturing reconstruction index, applying the least square principle to establish a deep shale gas reservoir horizontal well productivity prediction model;

[0053] S6, using the established productivity prediction model to predict the productivity of the target well of the deep shale gas reservoir.

[0054] In the present application, the calculation methods of the organic carbon S TOC , the porosity S POR and the gas content S GAS are as follows:

[0055]

[0056]

[0057]

[0058] In the formula, S TOC , S POR , S GAS are organic carbon integral results, porosity integral results and gas content integral results respectively; TOC, POR and GAS are collected organic carbon curves, porosity curves and gas content curves respectively; STDP is the depth of the perforation top boundary, m; ETDP is the depth of the perforation bottom boundary, m.

[0059] In the application, the S TOC , S POR , S GAS are obtained by using the analytic hierarchy process.

[0060] According to the importance of the organic carbon integral results S TOC , the porosity integral results S POR and the gas content integral results S GAS , the quantitative value analysis is carried out according to the analytic hierarchy process scale table, and the judgment matrix is established.

[0061] The maximum eigenvalue of the judgment matrix and the corresponding eigenvector are solved, that is, the weight of the level factor in the previous level is obtained.

[0062] It should be noted that the weight coefficient obtained by using the analytic hierarchy process can be realized based on the software YAAHP.

[0063] In the application, the reservoir gas-bearing index Ig is constructed, and the specific calculation is as follows:

[0064] Ig=(R TOC ×S TOC +R POR ×S POR +R GAS ×S GAS )×h×Kf (4)

[0065] In the formula, Ig is the reservoir gas-bearing index; R TOC , R POR , R GAS are the organic matter weight coefficient, the porosity weight coefficient and the gas content weight coefficient respectively; h is the continuous thickness of the I-type reservoir, m; and Kf is the formation pressure coefficient.

[0066] In the application, the fracturing fluid intensity and the sanding intensity reflecting the reservoir engineering quality are normalized, as follows:

[0067]

[0068]

[0069] In the formula, NVF is normalized fluid intensity; NVS is normalized sand intensity, t / m; VF is actual fracturing fluid intensity, m 3 / m; VF MIN is the minimum value of regional fluid intensity, m 3 / m; VF MAX is the maximum value of regional fluid intensity, m 3 / m; VS is actual fracturing sand intensity, t / m; VS MAX is the maximum value of regional sand intensity, t / m; VS MIN is the minimum value of regional sand intensity, t / m;

[0070] In the application, the reservoir fracturing reconstruction index is constructed as follows:

[0071] FI=(NVF+NVS)×L (7)

[0072] In the formula, FI is the reservoir fracturing reconstruction index, and L is the length of the horizontal section I type reservoir, m.

[0073] In the application, the deep shale gas reservoir productivity prediction model is established as follows:

[0074] AOFg=a×Ig+b×FI+c (8)

[0075] In the formula, AOFg is the open flow capacity of the horizontal well, 10 4 m 3 / d; a, b and c are fitting coefficients.

[0076] Based on the collected geological parameters and engineering parameters of the deep shale gas reservoir in the research area, the least square method is used to fit the fitting coefficients.

[0077] The above deep shale gas reservoir productivity prediction model is applied, and the corresponding parameters of the target well are brought in, so that the productivity of the target well can be predicted.

[0078] Embodiment

[0079] Based on the above application concept, the embodiment takes 23 wells in the deep shale gas in the south of Sichuan as an example to predict the productivity, and the specific process is as follows:

[0080] A1, collect and arrange the geological parameters (organic carbon, porosity, gas content, continuous thickness of I type reservoir, formation pressure coefficient) and engineering parameters (thickness of I type reservoir, fracturing fluid intensity and sand intensity) of the deep shale gas reservoir in the research area, as shown in Table 1;

[0081] A2, with the top and bottom depth of the actual fracturing length as the boundary, the organic carbon, porosity and gas content are calculated by integration, and the calculation results are shown in Table 1; the calculation method is shown in the above formula (1), formula (2) and formula (3);

[0082] A3, the weight coefficients of the organic carbon, porosity and gas content in the reservoir quality evaluation are obtained by using the analytic hierarchy process;

[0083] In this embodiment, according to the importance of the organic carbon integral result, the porosity integral result and the gas content integral result, the quantitative value analysis is carried out according to the analytic hierarchy process proportional scale table (Table 2), the judgment matrix (Table 3) is established, the maximum eigenvalue of the judgment matrix Z and the corresponding eigenvector are obtained, that is, the weight of the level factor in the previous level (Table 4) is obtained, and the calculation process is realized in the software YAAHP.

[0084] A4, combined with the continuous thickness of the I-type reservoir and the formation pressure coefficient, the reservoir gas content index is constructed; the calculation results are shown in Table 1; the calculation method is shown in the above formula (4);

[0085] A5, the fluid intensity and the sand intensity reflecting the engineering quality of the reservoir are normalized to obtain the normalized fluid intensity and sand intensity; the calculation results are shown in Table 1; the calculation method is shown in the above formula (5) and formula (6);

[0086] A6, the reservoir fracturing reconstruction index is constructed; the calculation results are shown in Table 1; the calculation method is shown in the above formula (7);

[0087] A7, combined with the reservoir gas content index and the reservoir fracturing reconstruction index, the least square principle is applied to establish a deep shale gas reservoir productivity prediction model;

[0088] The deep shale gas reservoir productivity prediction model is established as follows:

[0089] AOFg=a×Ig+b×FI+c;

[0090] Based on the data collected in Table 1 of this embodiment, the fitting coefficients a, b and c are obtained by using the least square principle, a=0.000637, b=0.005363, c=3.356.

[0091] A8, using the productivity prediction model obtained above, the corresponding parameters of the target well are brought in, and the productivity of the target well can be predicted.

[0092] The productivity prediction results are shown in Table 1 and Figure 2According to the method, the well data collected in the research area is analyzed, and it is found that the predicted productivity is consistent with the test productivity, and the correlation coefficient is greater than 0.89. The model is verified by the test well, and the absolute error of the predicted productivity of the three test wells is 0.3-0.9 m / t, the relative error of the well with the test productivity greater than 10 m / t is less than 5%, and the relative error of the well with the test productivity less than 10 m / t is less than 20%, thereby improving the prediction accuracy of the productivity.

[0093] Table 1 basic data and calculation results of collected wells

[0094]

[0095]

[0096] Table 2 analytic hierarchy process scale table

[0097]

[0098] Table 3 analytic hierarchy process judgment matrix

[0099] Influencing factors [SA POR ]] [SA TOC ]] [SA GAS ]] [SA POR ]] 1 2 1 [SA TOC ]] 1 / 2 1 1 / 2 [SA GAS ]] 1 2 1

[0100] Table 4 analytic hierarchy process calculation result table

[0101] Influencing factors [SA TOC ]] [SA POR ]] [SA GAS ]]> Proportionality factor 0.2 0.4 0.4

[0102] Based on the above inventive concept, the application also provides an integral-based deep shale gas reservoir productivity prediction device for realizing the integral-based deep shale gas reservoir productivity prediction method, and the device comprises:

[0103] An integral module is configured to calculate organic carbon integral results, porosity integral results and gas content integral results of each horizontal well by taking the top depth and the bottom depth of the fracturing reconstruction well section of the horizontal well as the integral interval based on the organic carbon curve, the porosity curve and the gas content curve of each horizontal well of the deep shale gas reservoir in the research area.

[0104] A first calculation module is configured to obtain the weight coefficients of the organic carbon integral results, the porosity integral results and the gas content integral results in the reservoir quality evaluation, and construct a reservoir gas-bearing index.

[0105] A second calculation module is configured to construct a reservoir fracturing reconstruction index based on the fracturing fluid intensity and the sand intensity reflecting the engineering quality of the reservoir.

[0106] A model construction module is configured to construct a deep shale gas reservoir horizontal well productivity prediction model based on the reservoir gas-bearing index and the reservoir fracturing reconstruction index.

[0107] The prediction module is configured to predict the productivity of the target well of the deep shale gas reservoir by using the productivity prediction model.

[0108] It is worth noting that the device embodiment corresponds to the method embodiment described above, and the implementation manners of the method embodiment are applicable to the device embodiment and can achieve the same or similar technical effects, and thus will not be described here.

[0109] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0110] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in the flowcharts and / or block diagrams.

[0111] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in the flowcharts and / or block diagrams.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in the flowcharts and / or block diagrams.

[0113] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it. Although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for predicting the productivity of deep shale gas reservoirs based on integration, characterized in that: The following steps are involved: Based on the organic carbon curves, porosity curves, and gas content curves of each horizontal well in the deep shale gas reservoir in the study area, the top and bottom depths of the horizontal well fracturing section were used as the integration intervals, and the organic carbon integral results, porosity integral results, and gas content integral results of each horizontal well were calculated using the integration method. Obtaining weight coefficients of the organic carbon integral result, the porosity integral result, and the gas content integral result in reservoir quality evaluation, and constructing a reservoir gas content index; Constructing a reservoir fracturing reformation index based on the fracturing fluid strength and sand addition strength that reflect the reservoir engineering quality; Based on the reservoir gas content index and the reservoir fracturing transformation index, a horizontal well productivity prediction model for deep shale gas reservoirs is constructed; The productivity prediction model is used to predict the productivity of target wells in deep shale gas reservoirs.

2. The method for predicting deep shale gas reservoir productivity based on integration according to claim 1, characterized in that: The method further comprises: The geological parameters and engineering parameters of each horizontal well in the deep shale gas reservoir in the study area are collected in advance; the geological parameters include organic carbon, porosity, gas content, continuous thickness of Class I reservoir and formation pressure coefficient; the engineering parameters include Class I reservoir thickness, fracturing fluid strength and sand addition strength.

3. The method for predicting deep shale gas reservoir productivity based on integration according to claim 2, characterized in that: The integral method of calculating the organic carbon integral result, porosity integral result and gas content integral result of each horizontal well includes: Among them, S TOC 、S POR 、S GAS are the organic carbon integration results, porosity integration results and gas content integration results respectively; TOC, POR and GAS are the collected organic carbon curve, porosity curve and gas content curve respectively; STDP is the depth of the top boundary of the perforation; ETDP is the depth of the bottom boundary of the perforation.

4. The method for predicting deep shale gas reservoir productivity based on integration according to claim 3, characterized in that: Obtaining weight coefficients of the organic carbon integral result, porosity integral result, and gas content integral result in reservoir quality evaluation, including: According to the importance of the organic carbon integral result, the porosity integral result and the gas content integral result, the hierarchical analysis method is used to obtain the weight coefficients of the organic carbon integral result, the porosity integral result and the gas content integral result in reservoir quality evaluation.

5. The method for predicting deep shale gas reservoir productivity based on integration according to claim 4, characterized in that: The step of constructing a reservoir gas index includes: Ig=(R TOC ×S TOC +R POR ×S POR +R GAS ×S GAS )×h×Kf; Where Ig is the reservoir gas content index; R TOC 、R POR 、R GAS are the organic matter weight coefficient, porosity weight coefficient and gas content weight coefficient respectively; h is the continuous thickness of Class I reservoir; Kf is the formation pressure coefficient.

6. The method for predicting deep shale gas reservoir productivity based on integration according to claim 5, characterized in that: The reservoir fracturing reformation index is constructed based on the fracturing fluid strength and sand addition strength that reflect the reservoir engineering quality, including: The fracturing fluid strength and sand addition strength, which reflect the reservoir engineering quality, are normalized as follows: Among them, NVF is the normalized fluid intensity; NVS is the normalized sand intensity; VF is the fluid intensity used in actual fracturing; VF MIN VF is the minimum value of the regional liquid intensity; MAX is the maximum regional fluid intensity; VS is the actual sand intensity used in fracturing; VS MAX The maximum value of the sand adding intensity in the area; VS MIN The minimum value of the sand adding intensity for the area; The reservoir fracturing stimulation index is constructed based on the normalized fluid intensity and sand addition intensity as follows: FI = (NVF + NVS) × L; Wherein, FI is the reservoir fracturing reformation index, and L is the length of the horizontal section Class I reservoir.

7. The method for predicting deep shale gas reservoir productivity based on integration according to claim 6, characterized in that: Based on the reservoir gas content index and the reservoir fracturing transformation index, a horizontal well productivity prediction model for deep shale gas reservoirs is constructed, including: The horizontal well productivity prediction model for deep shale gas reservoirs is established as follows: AOFg=a×Ig+b×FI+c; Where AOFg is the open-flow capacity of the horizontal well; a, b, and c are fitting coefficients; Based on the collected geological parameters and engineering parameters of each horizontal well in the deep shale gas reservoir in the study area, the least squares method is used to fit the prediction model to obtain the fitting coefficients a, b, and c.

8. A deep shale gas reservoir productivity prediction device based on integration, characterized in that: For implementing the method for predicting the productivity of a deep shale gas reservoir based on integration according to any one of claims 1 to 7, the device comprises: The integration module is used to calculate the organic carbon integral results, porosity integral results, and gas content integral results of each horizontal well based on the organic carbon curve, porosity curve, and gas content curve of each horizontal well in the deep shale gas reservoir in the study area, with the top depth and bottom depth of the horizontal well fracturing stimulation section as the integration interval; A first calculation module is used to obtain weight coefficients of the organic carbon integral result, the porosity integral result, and the gas content integral result in reservoir quality evaluation, and to construct a reservoir gas content index; The second calculation module is used to construct a reservoir fracturing reformation index based on the fracturing fluid strength and sand addition strength that reflect the reservoir engineering quality; A model building module is used to build a horizontal well productivity prediction model for deep shale gas reservoirs based on the reservoir gas content index and the reservoir fracturing transformation index; The prediction module is used to use the productivity prediction model to predict the productivity of target wells in deep shale gas reservoirs.

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