Deep shale gas reservoir productivity prediction method and device based on integration

Abstract

The invention discloses an integral-based deep shale gas reservoir productivity prediction method and device, and the method comprises the steps: taking a horizontal well as a research object, taking the top depth and the bottom depth of an actual fracturing length as boundaries, obtaining an integral result of a parameter in a length range through an integral equation principle, representing the contribution of the parameter, constructing a reservoir gas index, and predicting the productivity of a deep shale gas reservoir. Constructing a reservoir fracturing transformation index based on the fracturing fluid strength and the sand adding strength which reflect the reservoir engineering quality; and on the basis of the reservoir gas index and the reservoir fracturing transformation index, a deep shale gas reservoir horizontal well productivity prediction model is constructed. According to the method, wrong evaluation possibly occurring on productivity prediction due to the adoption of an arithmetic mean value is well avoided, contribution of each parameter of the reservoir to the productivity is represented more finely, accurately and reasonably, and the productivity prediction precision is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum exploration and development, and in particular to a method and device for predicting the productivity of deep shale gas reservoirs based on integration. Background Art

[0002] As a clean and efficient unconventional natural gas resource, shale gas is of great significance to improving my country's energy structure and ensuring national energy security. As a low-porosity and low-permeability reservoir, the development of horizontal wells and volume fracturing have become important technical means to develop shale gas reservoirs. As a direct characterization parameter for evaluating development effects, accurate prediction of production capacity is of great significance for evaluating development effects, designing shale gas development plans, and optimizing engineering transformation plans. At present, the production capacity prediction methods for shale gas mainly include analogy experience method, analytical method and numerical method. The analytical method mainly describes the fluid state under different conditions through physical models. The numerical method is widely used, mainly including finite difference method and finite element method. The analogy experience method is most widely used in oil field sites and can directly guide oil field development design. It mainly selects the parameters with the highest correlation with the test production capacity on the premise of obtaining the factors affecting production capacity, and uses regression or neural network methods for prediction. Among the existing capacity prediction methods, the model calculated by analytical method is relatively idealized. In the actual development process, due to the heterogeneity and uncertainty of fracturing transformation, the accuracy of capacity prediction is low; the model parameters designed by numerical simulation method need to be manually intervened and adjusted, which has a certain degree of subjectivity in the process, and the accuracy of capacity prediction is not guaranteed. Therefore, in field applications, analogy experience method is the most widely used. However, in the current method, when selecting the main parameters affecting capacity, the average value of a certain layer section is usually used as a representative for parameter modeling. However, the average value of the parameter sometimes cannot represent the quality of the parameter, so the importance of the parameter in the factors affecting capacity may be misjudged. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for predicting the production capacity of deep shale gas reservoirs based on integration. The method takes horizontal wells as the research object, takes the top depth and bottom depth of the actual fracturing length as the boundary, adopts the principle of integral equation, obtains the integral result of the parameter within this length range, and uses this to represent the contribution of the parameter to carry out reservoir quality modeling analysis. At the same time, the geological quality and engineering quality of the reservoir are comprehensively considered to establish a deep shale gas production capacity prediction model.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] The present invention provides a method for predicting the productivity of deep shale gas reservoirs based on integration, comprising the following steps:

[0006] 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, the top depth and bottom depth of the horizontal well fracturing section are used as the integration interval, and the organic carbon integral results, porosity integral results and gas content integral results of each horizontal well are calculated by integration.

[0007] 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;

[0008] The reservoir fracturing transformation index is constructed based on the fracturing fluid strength and sand addition strength that reflect the reservoir engineering quality;

[0009] 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;

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

[0011] Furthermore, the method further comprises:

[0012] 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, and the engineering parameters include thickness of Class I reservoir, strength of fracturing fluid and strength of sand addition.

[0013] Furthermore, the integral method of calculating the organic carbon integral result, porosity integral result and gas content integral result of each horizontal well includes:

[0014]

[0015]

[0016]

[0017] Among them, S TOC , S POR , S GAS They are respectively the organic carbon integral result, porosity integral result and gas content integral result; 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.

[0018] Furthermore, the weight coefficients of the organic carbon integral result, the porosity integral result and the gas content integral result in the reservoir quality evaluation are obtained, including:

[0019] 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 the reservoir quality evaluation.

[0020] Furthermore, the construction of the reservoir gas content index includes:

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

[0022] 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.

[0023] Furthermore, the reservoir fracturing transformation index is constructed based on the fracturing fluid strength and sand addition strength reflecting the reservoir engineering quality, including:

[0024] The fracturing fluid strength and sand addition strength, which reflect the reservoir engineering quality, are normalized as follows:

[0025]

[0026]

[0027] 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 sand intensity used in actual fracturing; VS MAX The maximum value of the sand adding intensity in the area; VS MIN The minimum value of sand adding intensity for the area;

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

[0029] FI = (NVF + NVS) × L;

[0030] Among them, FI is the reservoir fracturing reformation index, and L is the length of the horizontal section Class I reservoir.

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

[0032] The horizontal well productivity prediction model for deep shale gas reservoirs is established as follows:

[0033] AOFg=a×Ig+b×FI+c;

[0034] Where 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 in the deep shale gas reservoir in the study area, the prediction model is fitted using the least squares method to obtain the fitting coefficients a, b, and c.

[0036] The present invention also provides a deep shale gas reservoir productivity prediction device based on integration, which is used to implement the aforementioned deep shale gas reservoir productivity prediction method based on integration, and the device comprises:

[0037] 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 transformation section as the integration interval, and use the integration method to calculate the organic carbon integral results, porosity integral results and gas content integral results of each horizontal well;

[0038] The first calculation module 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, and to construct a reservoir gas content index;

[0039] The second calculation module is used to construct a reservoir fracturing transformation index based on the fracturing fluid strength and sand addition strength reflecting the reservoir engineering quality;

[0040] A model building module, used to build a deep shale gas reservoir horizontal well productivity prediction model based on the reservoir gas content index and the reservoir fracturing transformation index;

[0041] The prediction module is used to use the productivity prediction model to predict the productivity of the target well in the deep shale gas reservoir.

[0042] The beneficial effects of the present invention are:

[0043] Compared with the prior art, the present invention changes the defect of the prior method of using arithmetic mean to evaluate reservoir capacity. It no longer relies on the arithmetic mean of parameters, but uses the results of reservoir parameter integration within the scope of fracturing transformation for analysis, which effectively avoids the possible erroneous evaluation of capacity prediction due to the use of arithmetic mean, and more finely, accurately and reasonably characterizes the contribution of each reservoir parameter to capacity, and further improves the accuracy of capacity prediction. The actual well data capacity prediction results show that this method has strong theoretical basis, high accuracy, simple operation, and is of great significance for oilfield capacity prediction, evaluation of development effects, and optimization of program design. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flow chart of a method for predicting the productivity of deep shale gas reservoirs based on integration provided by the present invention;

[0045] Figure 2 The comparison result between the test capacity and the predicted capacity provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention is further described below. The following examples are only used to more clearly illustrate the technical solution of the present invention, and are not intended to limit the protection scope of the present invention.

[0047] The present invention provides a method for predicting the productivity of deep shale gas reservoirs based on integration, see Figure 1 , including the following steps:

[0048] S1. Collect geological parameters and engineering parameters of deep shale gas reservoirs in the study area; geological parameters include organic carbon, porosity, gas content, continuous thickness of Class I reservoirs and formation pressure coefficient of each horizontal well; engineering parameters include thickness of Class I reservoirs, fracturing fluid strength and sand addition strength of each horizontal well;

[0049] S2. Taking the top depth and bottom depth of the horizontal well section for fracturing as the integral interval, the organic carbon integral result S of each horizontal well is calculated by integration. TOC , porosity integral result S POR And the gas content integral result S GAS ;

[0050] S3, using the analytic hierarchy process to obtain S TOC , S POR , S GAS Weight coefficients in reservoir quality evaluation and construction of reservoir gas index;

[0051] S4. Normalizing the fracturing fluid intensity and sand adding intensity reflecting the reservoir engineering quality to obtain the normalized fluid intensity and sand adding intensity, and constructing a reservoir fracturing transformation index;

[0052] S5. Based on the reservoir gas content index and reservoir fracturing transformation index, the least squares principle is used to establish a horizontal well productivity prediction model for deep shale gas reservoirs;

[0053] S6. Use the constructed production capacity prediction model to predict the production capacity of target wells in deep shale gas reservoirs.

[0054] In the present invention, organic carbon S TOC , porosity S POR With gas content S GAS The calculation method is:

[0055]

[0056]

[0057]

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

[0059] In the present invention, the hierarchical analysis method is used to obtain S TOC , S POR , S GAS The specific implementation process of the weight coefficient in reservoir quality evaluation is as follows:

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

[0061] By finding the maximum eigenvalue and the corresponding eigenvector of the judgment matrix, we can obtain the weight of the factor at this level in the previous level.

[0062] It should be noted that the use of the analytic hierarchy process to obtain weight coefficients can be implemented based on the software YAAHP.

[0063] In the present invention, the reservoir gas 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] 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, m; Kf is the formation pressure coefficient.

[0066] In the present invention, the fracturing fluid strength and sand addition strength reflecting the reservoir engineering quality are normalized as follows:

[0067]

[0068]

[0069] Where NVF is the normalized fluid intensity; NVS is the normalized sand strength, t / m; VF is the actual fluid intensity used in fracturing, m 3 / m; VF MIN is the minimum value of the regional liquid intensity, m 3 / m; VF MAX is the maximum value of the regional liquid intensity, m 3 / m; VS is the sand adding strength used in actual fracturing, t / m; VS MAX is the maximum value of the regional sand adding intensity, t / m; VS MIN is the minimum value of the regional sand adding intensity, t / m;

[0070] In the present invention, the reservoir fracturing reformation index is constructed as follows:

[0071] FI=(NVF+NVS)×L (7)

[0072] Where FI is the reservoir fracturing reformation index, and L is the length of the horizontal section of Class I reservoir, in m.

[0073] In the present invention, a deep shale gas reservoir capacity prediction model is established as follows:

[0074] AOFg=a×Ig+b×FI+c (8)

[0075] Where AOFg is the open-flow rate of the horizontal well, 10 4 m 3 / d; a, b, c are fitting coefficients;

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

[0077] By applying the above-mentioned deep shale gas reservoir capacity prediction model and inputting the corresponding parameters of the target well, the capacity of the target well can be predicted.

[0078] Example

[0079] Based on the above invention concept, this embodiment takes 23 deep shale gas wells in southern Sichuan as an example to perform capacity prediction. The specific process is as follows:

[0080] A1. Collect and organize the geological parameters (organic carbon, porosity, gas content, continuous thickness of Class I reservoir, formation pressure coefficient) and engineering parameters (thickness of Class I reservoir, strength of fracturing fluid and strength of sand addition) of deep shale gas reservoirs in the study area, as shown in Table 1;

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

[0082] A3. Use the analytic hierarchy process to obtain the weight coefficients of organic carbon, porosity and gas content in reservoir quality evaluation;

[0083] In this embodiment, according to the importance of the organic carbon integral results, the porosity integral results and the gas content integral results, a quantitative value analysis is performed according to the hierarchical analysis method proportional scale table (Table 2), a judgment matrix (Table 3) is established, and the maximum eigenvalue of the judgment matrix Z and its corresponding eigenvector are obtained, that is, the weight of the hierarchical factor in the previous level is obtained (Table 4). This calculation process is implemented in the software YAAHP.

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

[0085] A5. Normalize the liquid intensity and sand adding intensity reflecting the reservoir engineering quality to obtain the normalized liquid intensity and sand adding intensity. The calculation results are shown in Table 1. The calculation method is shown in the above formula (5) and formula (6);

[0086] A6. Construct a reservoir fracturing reformation index. The calculation results are shown in Table 1. The calculation method is shown in the above formula (7).

[0087] A7. Combining the reservoir gas content index and the reservoir fracturing transformation index, the least squares principle is used to establish a deep shale gas reservoir capacity prediction model;

[0088] The deep shale gas reservoir capacity 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 are obtained by fitting using the least squares principle: a=0.000637, b=0.005363, and c=3.356.

[0091] A8. Using the above-obtained capacity prediction model and inputting the corresponding parameters of the target well, the capacity of the target well can be predicted.

[0092] The capacity forecast results are shown in Table 1 and Figure 2As shown in the figure, the well data collected in the study area were analyzed for productivity according to the method of the present invention, and it can be found that the predicted productivity is very consistent with the tested productivity, with a correlation coefficient of more than 0.89. The model was verified using test wells, and the absolute error of the productivity prediction results of the three test wells was 0.3-0.9m / t, the relative error of the prediction of wells with a tested productivity of >10m / t was within 5%, and the relative error of the prediction of wells with a tested productivity of <10m / t was within 20%, which improved the accuracy of productivity prediction.

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

[0094]

[0095]

[0096] Table 2 Analytical hierarchy process scale table

[0097]

[0098] Table 3 AHP judgment matrix

[0099] Influencing factors <![CDATA[S POR ]]> <![CDATA[S TOC ]]> <![CDATA[S GAS ]]> <![CDATA[S POR ]]> 1 2 1 <![CDATA[S TOC ]]> 1 / 2 1 1 / 2 <![CDATA[S GAS ]]> 1 2 1

[0100] Table 4 Hierarchy analysis calculation results

[0101] Influencing factors <![CDATA[S TOC ]]> <![CDATA[S POR ]]> <![CDATA[S GAS ]]> Proportional coefficient 0.2 0.4 0.4

[0102] Based on the above inventive concept, the present invention also provides a deep shale gas reservoir productivity prediction device based on integration, which is used to implement the above deep shale gas reservoir productivity prediction method based on integration, and the device comprises:

[0103] 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 transformation section as the integration interval, and use the integration method to calculate the organic carbon integral results, porosity integral results and gas content integral results of each horizontal well;

[0104] The first calculation module 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, and to construct a reservoir gas content index;

[0105] The second calculation module is used to construct a reservoir fracturing transformation index based on the fracturing fluid strength and sand addition strength reflecting the reservoir engineering quality;

[0106] A model building module, used to build a deep shale gas reservoir horizontal well productivity prediction model based on the reservoir gas content index and the reservoir fracturing transformation index;

[0107] The prediction module is used to use the productivity prediction model to predict the productivity of the target well in the deep shale gas reservoir.

[0108] It is worth pointing out that the device embodiment corresponds to the above-mentioned method embodiment, and the implementation methods of the above-mentioned method embodiments are all applicable to the device embodiment and can achieve the same or similar technical effects, so they will not be repeated here.

[0109] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

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 curve, porosity curve and gas content curve of each horizontal well in the deep shale gas reservoir in the study area, the top depth and bottom depth of the horizontal well fracturing section are used as the integration interval, and the organic carbon integral results, porosity integral results and gas content integral results of each horizontal well are calculated by integration. 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; The reservoir fracturing transformation index is constructed 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 the productivity of deep shale gas reservoirs 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, and the engineering parameters include thickness of Class I reservoir, strength of fracturing fluid and strength of sand addition.

3. The method for predicting the productivity of deep shale gas reservoirs based on integration according to claim 2, characterized in that: The integral method is used to calculate the organic carbon integral result, porosity integral result and gas content integral result of each horizontal well, including: Among them, S TOC , S POR , S GAS They are respectively the organic carbon integral result, porosity integral result and gas content integral result; 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 the productivity of deep shale gas reservoirs 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 the reservoir quality evaluation.

5. The method for predicting the productivity of deep shale gas reservoirs based on integration according to claim 4, characterized in that: The step of constructing a reservoir gas index comprises: 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 the productivity of deep shale gas reservoirs based on integration according to claim 5, characterized in that: The reservoir fracturing transformation index is constructed based on the fracturing fluid strength and sand addition strength reflecting 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 sand intensity used in actual fracturing; VS MAX The maximum value of the sand adding intensity in the area; VS MIN The minimum value of sand adding intensity for the area; The reservoir fracturing transformation index is constructed based on the normalized fluid intensity and sand addition intensity as follows: FI = (NVF + NVS) × L; Among them, FI is the reservoir fracturing reformation index, and L is the length of the horizontal section Class I reservoir.

7. The method for predicting the productivity of deep shale gas reservoirs based on integration according to claim 6, characterized in that: Based on the reservoir gas content index and the reservoir fracturing transformation index, a deep shale gas reservoir horizontal well productivity prediction model 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 prediction model is fitted using the least squares method to obtain the fitting coefficients a, b, and c.

8. A deep shale gas reservoir productivity prediction device based on integration, characterized in that: The device is used to implement the deep shale gas reservoir productivity prediction method based on integration as described in any one of claims 1 to 7, comprising: 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 transformation section as the integration interval, and use the integration method to calculate the organic carbon integral results, porosity integral results and gas content integral results of each horizontal well; The first calculation module 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, and to construct a reservoir gas content index; The second calculation module is used to construct a reservoir fracturing transformation index based on the fracturing fluid strength and sand addition strength reflecting the reservoir engineering quality; A model building module, used to build a deep shale gas reservoir horizontal well productivity prediction model 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 the target well in the deep shale gas reservoir.

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