Seam network complexity determination method and device and nonvolatile storage medium

Abstract

The invention discloses a seam network complexity determination method and device and a nonvolatile storage medium. The method comprises the steps that geomechanical parameters corresponding to a fracturing section are determined; fracturing transformation parameters and oil and gas productivity corresponding to the fracturing section are obtained, and the fracturing transformation parameters are parameters used for conducting fracturing treatment on the fracturing section to obtain oil and gas output; and based on the geomechanical parameters, the fracturing transformation parameters and the oil and gas productivity, the fracture network complexity in the fracturing section is determined. According to the invention, the technical problem of high time and material resource cost caused by high limitation and low universality of obtaining the complexity of the seam network in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of petroleum and natural gas engineering, and in particular to a method and device for determining seam network complexity and a non-volatile storage medium. Background Art

[0002] At present, large-scale segmented multi-cluster volume fracturing of horizontal wells is the key technology for the efficient development of shale oil and gas. Shale oil and gas resources are oil replacement resources with great potential. Establishing a complex fracture network in the reservoir is conducive to improving the oil and gas production capacity obtained by fracturing. The complexity of the fracture network is a key indicator for evaluating fracturing transformation methods, and plays a key role in the optimization and improvement of fracturing technology. However, there are many factors that affect the complexity of the fracture network after fracturing, and the relationship is complex. Microseismic monitoring is often used in related technologies to determine the complexity of the fracture network, which consumes a lot of manpower, material resources, and time costs. There is also a lack of universal evaluation for reservoirs of the same type in different regions, resulting in a waste of resources and a high limitation in obtaining the complexity of the fracture network.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and non-volatile storage medium for determining seam network complexity, so as to at least solve the technical problem that obtaining seam network complexity in related technologies has high limitations, low versatility and high time and material costs.

[0005] According to one aspect of an embodiment of the present invention, a fracture network complexity method is provided, comprising: determining geomechanical parameters corresponding to a fracturing segment; obtaining fracturing transformation parameters and oil and gas production capacity corresponding to the fracturing segment, wherein the fracturing transformation parameters are parameters for fracturing the fracturing segment to obtain oil and gas production; and determining the fracture network complexity in the fracturing segment based on the geomechanical parameters, the fracturing transformation parameters and the oil and gas production capacity.

[0006] According to another aspect of an embodiment of the present invention, a fracture network complexity device is provided, comprising: a first determination module, used to determine the geomechanical parameters corresponding to a fracturing segment; an acquisition module, used to acquire fracturing transformation parameters and oil and gas production capacity corresponding to the fracturing segment, wherein the fracturing transformation parameters are parameters for fracturing the fracturing segment to obtain oil and gas production; and a second determination module, used to determine the fracture network complexity in the fracturing segment based on the geomechanical parameters, the fracturing transformation parameters and the oil and gas production capacity.

[0007] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, wherein the non-volatile storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing any one of the seam network complexity methods.

[0008] According to another aspect of an embodiment of the present invention, there is provided an electronic device, comprising: one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the seam network complexity methods.

[0009] In an embodiment of the present invention, the geomechanical parameters corresponding to the fracturing section are determined; the fracturing transformation parameters and oil and gas production capacity corresponding to the fracturing section are obtained, wherein the fracturing transformation parameters are parameters for fracturing the fracturing section to obtain oil and gas production; based on the geomechanical parameters, the fracturing transformation parameters and the oil and gas production capacity, the complexity of the fracture network in the fracturing section is determined. The purpose of combining geomechanical parameters and fracturing transformation parameters to improve the accuracy of fracture network complexity is achieved, and the technical effect of reducing the difficulty and cost of obtaining fracture network complexity and improving the versatility of fracture network complexity evaluation methods is achieved, thereby solving the technical problem of high time-consuming and high material costs due to the high limitations and low versatility of obtaining fracture network complexity in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0011] Figure 1 is a flow chart of an optional method for determining seam network complexity provided according to an embodiment of the present invention;

[0012] Figure 2 is a schematic diagram of mineral difference factors of an optional method for determining fracture network complexity provided according to an embodiment of the present invention;

[0013] Figure 3 is a schematic diagram of a microcrack index of an optional method for determining the complexity of a crack network provided according to an embodiment of the present invention;

[0014] Figure 4 is a schematic diagram of correlation between horizontal geostress differences of an optional method for determining fracture network complexity provided according to an embodiment of the present invention;

[0015] Figure 5 is a schematic diagram of the correlation of microcrack indexes of an optional method for determining the complexity of a crack network provided according to an embodiment of the present invention;

[0016] Figure 6 is a schematic diagram of correlation of the number of fracturing clusters in an optional method for determining the complexity of a fracture network provided in an embodiment of the present invention;

[0017] Figure 7 is a schematic diagram of the correlation of liquid inflow intensity of an optional method for determining seam network complexity provided according to an embodiment of the present invention;

[0018] Figure 8 is a schematic diagram of sand addition intensity correlation of an optional method for determining seam network complexity provided according to an embodiment of the present invention;

[0019] Fig. 9 is a schematic diagram of seam network complexity of an optional seam network complexity determination method provided according to an embodiment of the present invention;

[0020] Fig.10 is a schematic diagram of an optional seam network complexity determination device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] Large-scale segmented multi-cluster volume fracturing of horizontal wells is a key technology for the efficient development of shale oil and gas, and is of great significance to energy independence. Shale oil and gas resources are oil replacement resources with huge potential. Establishing a complex fracture network in the reservoir to maximize the oil and gas seepage contact area, minimize the seepage resistance, and minimize the seepage distance is the core of unconventional volume fracturing. The complexity of the fracture network is a key indicator for evaluating fracturing transformation methods, and plays a key role in optimizing and improving fracturing technology. However, there are many factors that affect the complexity of the fracture network after fracturing and the relationship is complex. There are mainly the following ways to determine the complexity of the fracture network in related technologies:

[0024] (1) In a related technology, a tight reservoir combined geostress field calculation model considering multi-fracture stress interference and a tight reservoir volume fracturing horizontal well fracture network expansion theoretical model are established. The influence of different factors on the volume fracturing network structure is comprehensively analyzed, and the fracture network structure and attribute characteristics are characterized by defining multiple characteristic parameters. Finally, a set of tight reservoir volume fracturing network expansion simulation and characterization methods is established. However, in the above processing, only the engineering parameters of the rock are considered, and multiple models are used to jointly optimize the simulation characterization method, without considering the influence of the mechanical and mineral properties of the rock itself, resulting in unsatisfactory accuracy of the fracture network complexity.

[0025] (2) In another related technology, the mineral component deviation coefficient is determined according to the shale mineral composition, and the dynamic Young's modulus and dynamic Poisson's ratio of various minerals are determined. According to the dynamic Young's modulus and dynamic Poisson's ratio of various minerals, the estimated Young's modulus and estimated Poisson's ratio of the reservoir are determined using the component model. Through linear fitting, the linear fitting formula of the fitted Young's modulus and fitted Poisson's ratio corresponding to the dynamic Young's modulus and dynamic Poisson's ratio is obtained to calculate the brittleness coefficient. In the above method, the influence of minerals and mechanical properties is taken into account, but the scope is relatively limited. For different blocks, the standard needs to be changed, and the method for obtaining the complexity of the fracture network is not very universal.

[0026] (3) In another related technology, a model for calculating the wave and volume of a fracture network by subdividing and cutting volume was proposed. The relationship between key geological engineering parameters and microseismic coverage volume was established by using the multivariate linear regression method. The relationship was further corrected using the actual production data of the mine, and an empirical formula for quantitative characterization of the wave and volume of the fracture network was established. Then, a correlation chart with production capacity was drawn to provide guidance for the optimization of volume fracturing engineering parameters. In the above method, a relationship that conforms to this block was made using field data, and a model was established. However, it also has the same problem as related technology (2). It does not deeply explore the properties of the rock itself and does not have strong universality.

[0027] In response to the above problems, an embodiment of the present invention provides a method embodiment for determining the complexity of a seam network. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] Figure 1 is a flow chart of a method for determining seam network complexity according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0029] Step S102, determining the geomechanical parameters corresponding to the fracturing stage.

[0030] It can be understood that the fracturing section is a section of the geological layer after fracturing transformation, and geomechanical parameters are used to characterize the geological structure and stress conditions in the fracturing section.

[0031] In an optional embodiment, the above-mentioned determination of the geomechanical parameters corresponding to the above-mentioned fracturing section includes: when the above-mentioned geomechanical parameters include a microfracture index, obtaining a core sample in the above-mentioned fracturing section, wherein the above-mentioned microfracture index characterizes the ability of forming a fracture network in the above-mentioned fracturing section; performing a component analysis on the above-mentioned core sample to determine the component quantities of multiple mineral components included in the above-mentioned core sample, and determining a mineral difference factor corresponding to the above-mentioned core sample, wherein the above-mentioned mineral difference factor characterizes the difference between the above-mentioned multiple mineral components; based on the above-mentioned mineral difference factor and the above-mentioned component quantity, determining the above-mentioned microfracture index corresponding to the above-mentioned fracturing section.

[0032] It can be understood that the geomechanical parameters include the microcrack index, which is used to characterize the ability to form a fracture network in the fracturing section. It is necessary to perform core sampling operations on the fracturing section to obtain core samples. The core samples are analyzed for composition to determine the number of components of the various mineral components included in the core samples, and the mineral difference factors corresponding to the core samples are determined. The mineral difference factors characterize the differences between the various mineral components, and further characterize the rock heterogeneity in the fracturing section. The larger the mineral difference factor, the stronger the rock heterogeneity. Based on the mineral difference factor and the number of components, the microcrack index corresponding to the fracturing section is determined.

[0033] Optionally, there are many ways to determine the mineral difference factor, for example: establish a mathematical expression of the mineral difference factor as:

[0034]

[0035] Where D is the mineral difference factor, expressed in percentage; i is the identifier of the i-th mineral component in the core sample, which is a dimensionless quantity; m i is the mass percentage of the i-th mineral component in the core sample, expressed in percentage; n is the number of components, expressed in species.

[0036] Optionally, there are multiple ways to determine the microcrack index, for example, a mathematical expression of the microcrack index is established as:

[0037]

[0038] Among them, MI is the microcrack index, which is a dimensionless quantity; D is the mineral difference factor, which is expressed in percentage; a, b, and c are preset coefficients, which are dimensionless quantities; and n is the number of components, which is expressed in species. Preferably, the a value is set to 2.6, the b value is set to 4, and the c value is set to 2.

[0039] In an optional embodiment, the above-mentioned determination of the mineral difference factor corresponding to the above-mentioned core sample includes: determining the mass percentages of the above-mentioned multiple mineral components in the above-mentioned core sample; and determining the above-mentioned mineral difference factor based on the mass percentages corresponding to the above-mentioned multiple mineral components and the quantity of the above-mentioned components.

[0040] Optionally, X-ray diffractometer testing is used to determine the various mineral compositions of the core samples.

[0041] It can be understood that by testing the mineral composition of the core sample, the mass percentage of each mineral component in the core sample can be obtained. Based on the mass percentage and component quantity corresponding to each of the multiple mineral components, the mineral difference factor of the core sample is determined. Through the above processing, by collecting core samples and measuring the composition of the core samples, the required actual acquisition parameters can be obtained. Compared with the related technology that requires the use of microseismic monitoring to obtain the complexity of the fracture network, the required actual measurement method is simple, which greatly reduces the cost of manpower and material resources and improves the efficiency of obtaining the complexity of the fracture network.

[0042] Optionally, there may be multiple ways to determine the mass percentages of the various mineral components in the core sample, for example, by establishing a mathematical expression as:

[0043]

[0044] Among them, M i is the mass of the i-th mineral component among the multiple mineral components in the core sample, in grams; i is the identifier of the i-th mineral component among the multiple mineral components in the core sample, which is a dimensionless quantity; m i is the mass percentage of the i-th mineral component in the core sample, expressed in percentage; n is the number of components, expressed in species.

[0045] Step S104, obtaining the fracturing transformation parameters and oil and gas production capacity corresponding to the above-mentioned fracturing section, wherein the above-mentioned fracturing transformation parameters are parameters for fracturing the above-mentioned fracturing section to obtain oil and gas production.

[0046] It can be understood that the fracturing transformation parameters are the parameters for obtaining oil and gas production by fracturing the above-mentioned fracturing section, and characterizing the fracturing section from an engineering perspective. Therefore, it is necessary to obtain the fracturing transformation parameters for fracturing treatment and the oil and gas production capacity obtained by the fracturing section when the fracturing transformation parameters are used.

[0047] In an optional embodiment, the above-mentioned determination of the fracture network complexity corresponding to the above-mentioned fracturing section based on the above-mentioned geomechanical parameters, the above-mentioned fracturing transformation parameters and the above-mentioned oil and gas production capacity includes: determining a first correlation coefficient characterizing the correlation between the above-mentioned fracturing transformation parameters and the above-mentioned oil and gas production capacity, and a second correlation coefficient characterizing the correlation between the above-mentioned geomechanical parameters and the above-mentioned oil and gas production capacity; determining the above-mentioned fracture network complexity based on the first correlation coefficient corresponding to the above-mentioned fracturing transformation parameters, the second correlation coefficient corresponding to the above-mentioned geomechanical parameters, the above-mentioned fracturing transformation parameters, and the above-mentioned geomechanical parameters.

[0048] It can be understood that the first correlation coefficient representing the correlation between the fracturing transformation parameters and the oil and gas production capacity and the second correlation coefficient representing the correlation between the geomechanical parameters and the oil and gas production capacity are determined. In other words, from the two perspectives of geology and engineering, the contribution of the fracturing transformation parameters to the oil and gas production capacity and the contribution of the geomechanical parameters to the oil and gas production capacity are determined. Based on the first correlation coefficient corresponding to the fracturing transformation parameters, the second correlation coefficient corresponding to the geomechanical parameters, the fracturing transformation parameters, and the geomechanical parameters, the fracture network complexity is determined.

[0049] Optionally, there are multiple ways to obtain the first correlation coefficient and the second correlation coefficient, for example: using a hierarchical analysis method or a data fitting method to determine a first correlation coefficient characterizing the correlation between the fracturing transformation parameters and the oil and gas production capacity, and a second correlation coefficient characterizing the correlation between the geomechanical parameters and the oil and gas production capacity.

[0050] In an optional embodiment, the above-mentioned determination of the above-mentioned fracture network complexity based on the first correlation coefficient corresponding to the above-mentioned fracturing transformation parameter, the second correlation coefficient corresponding to the above-mentioned geomechanical parameter, the above-mentioned fracturing transformation parameter, and the above-mentioned geomechanical parameter includes: summing the first correlation coefficient corresponding to the above-mentioned fracturing transformation parameter and the second correlation coefficient corresponding to the above-mentioned geomechanical parameter to obtain a total value of the coefficients; determining a first weight value corresponding to the above-mentioned fracturing transformation parameter based on a ratio of the first correlation coefficient corresponding to the above-mentioned fracturing transformation parameter to the above-mentioned total value of the coefficients; determining a second weight value corresponding to the above-mentioned geomechanical parameter based on a ratio of the second correlation coefficient corresponding to the above-mentioned geomechanical parameter to the above-mentioned total value of the coefficients; determining the above-mentioned fracture network complexity based on the first weight value corresponding to the above-mentioned fracturing transformation parameter, the second weight value corresponding to the above-mentioned geomechanical parameter, the above-mentioned fracturing transformation parameter, and the above-mentioned geomechanical parameter.

[0051] It can be understood that in order to comprehensively consider the fracturing transformation parameters and geomechanical parameters and remove the influence of the numerical value of the correlation coefficient, the weight value is obtained by using a weighted method. First, the first correlation coefficient corresponding to the fracturing transformation parameter and the second correlation coefficient corresponding to the geomechanical parameter are summed to obtain the total value of the coefficient. Based on the ratio of the first correlation coefficient corresponding to the fracturing transformation parameter to the total value of the coefficient, the first weight value corresponding to the fracturing transformation parameter is determined. Based on the ratio of the second correlation coefficient corresponding to the geomechanical parameter to the total value of the coefficient, the second weight value corresponding to the geomechanical parameter is determined. Based on the first weight value corresponding to the fracturing transformation parameter, the second weight value corresponding to the geomechanical parameter, the fracturing transformation parameter, and the geomechanical parameter, the fracture network complexity is determined.

[0052] Optionally, there are multiple ways to determine the first weight value corresponding to the fracturing transformation parameter and the second weight value corresponding to the geomechanical parameter. For example, both the fracturing transformation parameter and the geomechanical parameter are considered as factors affecting the complexity of the fracture network, and a mathematical expression is established as follows:

[0053]

[0054] Among them, W j is the weight value corresponding to the jth factor affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters), a dimensionless quantity; p is the number of factors affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters), in units; R j is the correlation coefficient corresponding to the jth factor affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters), and j is the identifier of the factor affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters).

[0055] In an optional embodiment, the above-mentioned determination of the above-mentioned fracture network complexity based on the first weight value corresponding to the above-mentioned fracturing transformation parameter, the second weight value corresponding to the above-mentioned geomechanical parameter, the above-mentioned fracturing transformation parameter, and the above-mentioned geomechanical parameter includes: in the case where there are multiple fracturing segments and the multiple fracturing segments correspond to the fracturing transformation parameter and the geomechanical parameter respectively, determining the first normalized value of the fracturing transformation parameter corresponding to the above-mentioned multiple fracturing segments respectively, and determining the second normalized value of the geomechanical parameter corresponding to the above-mentioned multiple fracturing segments respectively; determining the above-mentioned fracture network complexity based on the first normalized value of the fracturing transformation parameter corresponding to the above-mentioned multiple fracturing segments respectively, the second normalized value of the geomechanical parameter corresponding to the above-mentioned multiple fracturing segments respectively, the first weight value corresponding to the fracturing transformation parameter corresponding to the above-mentioned multiple fracturing segments respectively, and the second weight value corresponding to the geomechanical parameter corresponding to the above-mentioned multiple fracturing segments respectively.

[0056] It can be understood that there may be multiple fracturing sections in a single horizontal well, and each of the multiple fracturing sections has its own fracturing transformation parameters and geomechanical parameters. When there are multiple fracturing sections, and each of the multiple fracturing sections corresponds to the fracturing transformation parameters and geomechanical parameters, the first normalized values ​​of the fracturing transformation parameters corresponding to each of the multiple fracturing sections are determined, and the second normalized values ​​of the geomechanical parameters corresponding to each of the multiple fracturing sections are determined, and dimensionless processing is performed. Based on the first normalized values ​​of the fracturing transformation parameters corresponding to each of the multiple fracturing sections, the second normalized values ​​of the geomechanical parameters corresponding to each of the multiple fracturing sections, the first weight values ​​corresponding to the fracturing transformation parameters corresponding to each of the multiple fracturing sections, and the second weight values ​​corresponding to the geomechanical parameters corresponding to each of the multiple fracturing sections, the complexity of the fracture network is determined.

[0057] Optionally, there are various ways to determine the complexity of the seam network, for example:

[0058]

[0059] Among them, FI is the complexity of the seam network, X q is the factor affecting the complexity of the fracture network corresponding to the qth segment in multiple fracturing segments (including fracturing transformation parameters and geomechanical parameters), X max is the maximum value of the factors affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters) corresponding to multiple fracturing stages, j is the weight value corresponding to the jth factor affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters), a dimensionless quantity; p is the number of factors affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters), in units of pieces.

[0060] Step S106, determining the complexity of the fracture network in the fracturing section based on the geomechanical parameters, the fracturing stimulation parameters and the oil and gas production capacity.

[0061] It can be understood that fracturing transformation parameters have an important impact on oil and gas production. For the same type of reservoir (i.e., similar geomechanical parameters), using better fracturing transformation parameters can form a complex fracture network, thereby obtaining better fracturing effects and more oil and gas production. Fracture network complexity is a post-parameter used to characterize the effect of fracturing transformation parameters in the fracturing section, hoping to pay the least fracturing transformation cost and obtain the most oil and gas production capacity. Therefore, based on geomechanical parameters, fracturing transformation parameters and oil and gas production capacity are combined from the perspectives of geology and engineering to determine the complexity of the fracture network in the fracturing section.

[0062] In an optional embodiment, the above-mentioned fracturing transformation parameters include the number of fracturing clusters, the liquid injection intensity, and the sand addition intensity. The above-mentioned geomechanical parameters include a microcrack index and a horizontal ground stress difference, wherein the above-mentioned fracturing cluster number is the number of fracture clusters obtained by fracturing the above-mentioned fracturing section, the above-mentioned liquid injection intensity is the amount of liquid injected into the above-mentioned fracturing section, the above-mentioned sand addition intensity is the amount of sand injected into the above-mentioned fracturing section, the above-mentioned microcrack index characterizes the ability to form a fracture network in the above-mentioned fracturing section, and the above-mentioned horizontal ground stress difference is the difference between the maximum horizontal ground stress of the above-mentioned fracturing section and the minimum horizontal ground stress of the above-mentioned fracturing section.

[0063] It can be understood that the fracturing transformation parameters may include: the number of fracturing clusters, the fluid injection intensity, and the sand addition intensity, which are used to characterize the fracturing treatment implementation method for the fracturing section. The geomechanical parameters include the microcrack index and the horizontal ground stress difference, which characterize the stress state in the fracturing section and the ability to form a fracture network.

[0064] Through the above steps, the purpose of combining geomechanical parameters with fracturing stimulation parameters to improve the accuracy of fracture network complexity can be achieved, and the technical effect of reducing the difficulty and cost of obtaining fracture network complexity and improving the versatility of fracture network complexity assessment methods is achieved, thereby solving the technical problem of high time and material costs due to the high limitations and low versatility of obtaining fracture network complexity in related technologies.

[0065] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation mode, which is specifically described by way of example for ease of understanding.

[0066] A shale oil reservoir is transformed by horizontal well volume fracturing. The well that needs to evaluate the complexity of the fracture network is recorded as H1 well. There are a total of 20 fracturing sections in H1 well. Six of the fracturing sections were selected for downhole coring. The six fracturing sections are numbered C1 to C6. In order to evaluate the fracturing sections and obtain the production capacity ratio during normal production after the corresponding volume fracturing, the geomechanics and fracturing transformation strength parameters of the fracturing sections are used to evaluate the fracturing sections. The specific implementation process of the present invention is described below.

[0067] A multi-level evaluation system is established using the analytic hierarchy process, and the factors affecting the complexity of the horizontal well volume fracturing network are divided into geomechanical parameters and fracturing transformation parameters. The geomechanical parameters include: horizontal ground stress difference and microcrack index, and the fracturing transformation parameters include: number of fracturing clusters, fluid injection intensity, and sand addition intensity.

[0068] In order to obtain the microcrack index in the geomechanical parameters, it is necessary to test the mineral composition of the core samples of the fracturing section. Before the volume fracturing of the shale reservoir, the C1 to C6 fracturing sections were cored downhole, and the core samples were numbered the same as the corresponding fracturing sections, and the C1 to C6 core samples were obtained. The horizontal geostress difference, number of fracturing clusters, liquid injection intensity, sand addition intensity, and oil and gas production capacity ratio corresponding to the C1 to C6 fracturing sections were collected. Table 1 is a parameter diagram of multiple fracturing sections in the H1 well, including the horizontal geostress difference, number of fracturing clusters, liquid injection intensity, sand addition intensity, and oil and gas production capacity ratio corresponding to the C1 to C6 fracturing sections. Among them, the number of fracturing clusters is the number of fracture clusters obtained by fracturing the fracturing section, the liquid injection intensity is the amount of liquid injected into the fracturing section, the sand addition intensity is the amount of sand injected into the fracturing section, and the horizontal geostress difference is the difference between the maximum horizontal geostress of the fracturing section and the minimum horizontal geostress of the fracturing section. The following specific values ​​are for reference only and are not limited.

[0069] Table 1

[0070]

[0071] At the same time, the mineral composition of the core samples of the fracturing section was tested and evaluated by X-ray diffractometer. Table 2 shows the mineral composition. Table 2 shows that the various mineral components included in the multiple fracturing sections are: dolomite, quartz, calcite, feldspar, pyrite, clay, and the corresponding masses of the various mineral components are obtained. The following specific values ​​are for illustration only and are not limited.

[0072] Table 2

[0073]

[0074]

[0075] Then the micro-fracture index is calculated by using the mass percentage of various mineral components in each core sample. The micro-fracture index is used to evaluate the ability of reservoir volume fracturing to form a fracture network, which specifically includes the following contents:

[0076] According to the masses of various mineral components in the core samples, the mass percentages of various mineral components in the core samples are determined, and the mathematical expression is established as follows:

[0077]

[0078] Among them, M i is the mass of the i-th mineral component among the multiple mineral components in the core sample, in grams; i is the identifier of the i-th mineral component among the multiple mineral components in the core sample, which is a dimensionless quantity; m iis the mass percentage of the i-th mineral component in the core sample, expressed in percentage; n is the number of components, expressed in species.

[0079] Table 3 is a schematic diagram of the mass percentages of mineral components, such as the mass percentages of various mineral components included in the core samples corresponding to multiple fracturing stages in Well H1. The following specific values ​​are for illustration only and are not intended to be limiting.

[0080] Table 3

[0081]

[0082] Figure 2 is a schematic diagram of mineral difference factors of an optional method for determining fracture network complexity provided according to an embodiment of the present invention, such as Figure 2 As shown, the mineral difference factor of core sample C1 is 80.4% (percent sign), the mineral difference factor of core sample C2 is 69.0%, the mineral difference factor of core sample C3 is 59.7%, the mineral difference factor of core sample C4 is 53.1%, the mineral difference factor of core sample C5 is 60.6%, and the mineral difference factor of core sample C6 is 43.9%. According to the different percentages of minerals, the corresponding differences are also different. It is necessary to determine the number of components of the various mineral components included in the core sample, and determine the mineral difference factor corresponding to the core sample through the following mathematical expression:

[0083]

[0084] Where D is the mineral difference factor, expressed in percentage; i is the identifier of the i-th mineral component in the core sample, which is a dimensionless quantity; m i is the mass percentage of the i-th mineral component in the core sample, expressed in percentage; n is the number of components, expressed in species.

[0085] Table 4 is a schematic diagram of mineral difference factors. The mineral components in Table 4 include six types: dolomite, quartz, calcite, feldspar, pyrite and clay. The number of components n is equal to 6. The calculated mineral difference factors are shown in Table 4. The following specific values ​​are for illustration only and are not limited.

[0086] Table 4

[0087]

[0088] Based on the mineral difference factor and the number of components, the microfracture index corresponding to the fracturing section is determined in the following way:

[0089]

[0090] Among them, MI is the microcrack index, which is a dimensionless quantity; D is the mineral difference factor, the unit is percentage; n is the number of components, the unit is species.

[0091] Figure 3 : This is a schematic diagram of microfracture index of an optional method for determining fracture network complexity provided according to an embodiment of the present invention, showing that the microfracture index of core sample C1 is 0.6776, the microfracture index of core sample C2 is 0.7458, the microfracture index of core sample C3 is 0.8016, the microfracture index of core sample C4 is 0.8413, the microfracture index of core sample C5 is 0.7963, and the microfracture index of core sample C6 is 0.8966. Table 5 is a schematic diagram of microfracture index, showing the mineral difference factor and microfracture index of each core sample. The following specific values ​​are for illustration only and are not limited.

[0092] Table 5

[0093]

[0094]

[0095] The factors affecting the complexity of the fracture network in the multi-level evaluation system are coupled and a comprehensive evaluation is performed to calculate the complexity of the fracture network. The larger the value of the fracture network complexity, the higher the complexity of the fracture network.

[0096] Firstly, the hierarchical analysis method was used to determine the first correlation coefficient characterizing the correlation between fracturing transformation parameters and oil and gas production capacity, and the second correlation coefficient characterizing the correlation between geomechanical parameters and oil and gas production capacity. In other words, from the two perspectives of geology and engineering, the contribution of fracturing transformation parameters to oil and gas production capacity and the contribution of geomechanical parameters to oil and gas production capacity were determined.

[0097] Figure 4 is a schematic diagram of correlation between horizontal ground stress differences of an optional method for determining fracture network complexity provided according to an embodiment of the present invention, such as Figure 4 As shown in the figure, a data fit between the horizontal stress difference corresponding to the six fracturing stages and the oil and gas production capacity was established, and y = -3.2599x 1 +29.544, where y is the percentage of oil and gas production capacity, x is 1 is the horizontal in-situ stress difference, which is used to characterize the fitting degree between the horizontal in-situ stress difference and the oil and gas production capacity ratio. 1 2 It is 0.687, and the first correlation coefficient corresponding to the horizontal stress difference is 0.687.

[0098] Figure 5 is a schematic diagram of the correlation of microcrack indexes of an optional method for determining the complexity of a crack network provided according to an embodiment of the present invention, such as Figure 5As shown in the figure, the data fitting between the microcrack index and oil and gas production capacity corresponding to the six fracturing stages was established, and y = -63.742x 2 -41.277, where y is the percentage of oil and gas production capacity, x is 2 is the microcrack index, and R is used to characterize the fitting degree between the microcrack index and the oil and gas production capacity ratio. 2 2 It is 0.8744, and the first correlation coefficient corresponding to the microcrack index is 0.8744.

[0099] Figure 6 FIG. 1 is a schematic diagram of correlation of the number of fracturing clusters according to an optional method for determining the complexity of a fracture network provided in an embodiment of the present invention. Figure 6 As shown in the figure, a data fit between the number of fracturing clusters and oil and gas production capacity corresponding to the six fracturing stages was established, and y = 2.2069x 3 -4.2759, where y is the oil and gas production capacity share, x is 3 is the number of fracturing clusters, which is used to characterize the fitting degree between the number of fracturing clusters and the proportion of oil and gas production capacity. 3 2 The second correlation coefficient corresponding to the number of fracturing clusters is 0.1766.

[0100] Figure 7 FIG. 1 is a schematic diagram of the correlation of liquid inflow intensity of an optional method for determining seam network complexity provided according to an embodiment of the present invention, such as Figure 7 As shown in the figure, the data fitting between the fluid inflow intensity and oil and gas production capacity corresponding to the six fracturing stages was established, and y = 1.1216x 4 -19.867, where y is the percentage of oil and gas production capacity, x is 4 is the liquid injection intensity, and R is used to characterize the degree of fit between the liquid injection intensity and the oil and gas production capacity ratio. 4 2 It is 0.5942, and the second correlation coefficient corresponding to the liquid inflow intensity is 0.5942.

[0101] Figure 8 FIG. 1 is a schematic diagram of the correlation of sand adding intensity of an optional method for determining seam network complexity provided according to an embodiment of the present invention, such as Figure 8 As shown in the figure, the data fitting between the sand addition intensity and oil and gas production capacity corresponding to the six fracturing stages was established, and y = 10.532x 5 -16.119, where y is the percentage of oil and gas production capacity, x is 5 is the sand adding intensity, which is used to characterize the fitting degree between the sand adding intensity and the oil and gas production capacity ratio. 5 2 It is 0.7557, and the second correlation coefficient corresponding to the sand adding intensity is 0.7557.

[0102] The fracturing parameters and geomechanical parameters are considered as factors affecting the complexity of the fracture network, and the mathematical expression is established as follows:

[0103]

[0104] Among them, W j is the weight value corresponding to the jth factor affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters), a dimensionless quantity; p is the number of factors affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters), in units; R j is the correlation coefficient corresponding to the jth factor affecting the complexity of the fracture network (including the fracturing transformation parameters and the geomechanical parameters), and j is the identifier of the factor affecting the complexity of the fracture network (including the fracturing transformation parameters and the geomechanical parameters). Determine the first weight values ​​corresponding to the multiple fracturing transformation parameters and the second weight values ​​corresponding to the multiple geomechanical parameters.

[0105] Table 6 is a schematic diagram of the correlation coefficients and weight values. The geomechanical parameters in Table 6 include horizontal geostress difference and microcrack index, and the fracturing transformation parameters include the number of fracturing clusters, fluid injection intensity, and sand addition intensity. The following specific values ​​are for illustration only and are not limited.

[0106] Table 6

[0107] Influencing factors Correlation coefficient Weight value Horizontal ground stress difference 0.6870 0.2225 Microcrack Index 0.8744 0.2832 Fracturing cluster number 0.1766 0.0572 Liquid inlet strength 0.5942 0.1924 Sand Adding Strength 0.7557 0.2447

[0108] Since the horizontal stress difference, microcrack index, number of fracturing clusters, fluid inflow strength, sand addition strength, etc. have differences in numerical range and units, dedimensionalization is performed for comprehensive evaluation. The first normalized values ​​of the fracturing transformation parameters corresponding to the multiple fracturing stages are determined, and the second normalized values ​​of the geomechanical parameters corresponding to the multiple fracturing stages are determined. There are multiple ways to determine the complexity of the fracture network:

[0109]

[0110] Among them, FI is the complexity of the seam network, X q is the factor affecting the complexity of the fracture network corresponding to the qth segment in multiple fracturing segments (including fracturing transformation parameters and geomechanical parameters), X max is the maximum value of the factors affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters) corresponding to multiple fracturing stages, j is the weight value corresponding to the jth factor affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters), a dimensionless quantity; p is the number of factors affecting the complexity of the fracture network (including fracturing transformation parameters and geomechanical parameters), in units of pieces.

[0111] Fig. 9is a schematic diagram of seam network complexity of an optional seam network complexity determination method provided according to an embodiment of the present invention, such as Fig. 9 As shown, the fracture network complexity of core sample C1 is 0.7731, the fracture network complexity of core sample C2 is 0.7829, the fracture network complexity of core sample C3 is 0.8301, the fracture network complexity of core sample C4 is 0.7897, the fracture network complexity of core sample C5 is 0.8367, and the fracture network complexity of core sample C6 is 0.8939.

[0112] Table 7 shows the fracture network complexity. According to the order of core samples C1 to C6, the values ​​of fracture network complexity calculated are 0.7731, 0.7829, 0.8301, 0.7897, 0.8637, and 0.8939, respectively. The order of evaluating fracture network complexity is: C6>C5>C3>C4>C2>C1, so the fracture network effect of the fracturing section is C6>C5>C3>C4>C2>C1.

[0113] Table 7

[0114] Core sample number Seam complexity Sort by complexity C1 0.7731 6 C2 0.7829 5 C3 0.8301 3 C4 0.7897 4 C5 0.8367 2 C6 0.8939 1

[0115] The above optional implementation method achieves at least the following effects: taking into account the geological and engineering factors that affect the complexity of the fracture network in the shale layer, and comprehensively and quantitatively characterizing the integration of geology and engineering. By calculating the microfracture index, the development degree of microfractures before volume fracturing of the horizontal well is quantitatively characterized, which greatly improves the accuracy of the evaluation of the complexity of the fracture network after volume fracturing. Compared with the current microseismic monitoring method for evaluating the complexity of the fracture network, the testing cost is greatly saved. The calculation method provided in this embodiment is simple and feasible, and is also applicable to the evaluation of the volume fracturing effect of the same type of unconventional reservoirs. It has good application prospects and plays a good guiding role in the exploration and development of unconventional resources.

[0116] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0117] In this embodiment, a device for determining the complexity of a seam network is also provided, and the device is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the terms "module" and "device" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0118] According to an embodiment of the present invention, a device embodiment for implementing the seam network complexity determination method is also provided. Fig.10 is a schematic diagram of a seam network complexity determination device according to an embodiment of the present invention. Fig.10 As shown, the above-mentioned seam network complexity determination device includes: a first determination module 1002, an acquisition module 1004, and a second determination module 1006. The device is described below.

[0119] The first determination module 1002 is used to determine the geomechanical parameters corresponding to the fracturing stage;

[0120] The acquisition module 1004 is connected to the first determination module 1002 and is used to obtain the fracturing transformation parameters and oil and gas production capacity corresponding to the above-mentioned fracturing stage, wherein the above-mentioned fracturing transformation parameters are parameters for fracturing the above-mentioned fracturing stage to obtain oil and gas production;

[0121] The second determination module 1006 is connected to the acquisition module 1004 and is used to determine the complexity of the fracture network in the fracturing section based on the geomechanical parameters, the fracturing transformation parameters and the oil and gas production capacity.

[0122] In a device for determining the complexity of a fracture network provided by an embodiment of the present invention, a first determination module 1002 is provided to determine the geomechanical parameters corresponding to the fracturing section; an acquisition module 1004 is connected to the first determination module 1002 to obtain the fracturing transformation parameters and oil and gas production capacity corresponding to the above-mentioned fracturing section, wherein the above-mentioned fracturing transformation parameters are parameters for fracturing the above-mentioned fracturing section to obtain oil and gas production; a second determination module 1006 is connected to the acquisition module 1004 to determine the complexity of the fracture network in the above-mentioned fracturing section based on the above-mentioned geomechanical parameters, the above-mentioned fracturing transformation parameters and the above-mentioned oil and gas production capacity. The purpose of combining the geomechanical parameters and the fracturing transformation parameters to improve the accuracy of the fracture network complexity is achieved, and the technical effect of reducing the difficulty and cost of obtaining the fracture network complexity and improving the versatility of the fracture network complexity evaluation method is achieved, thereby solving the technical problem that the acquisition of the fracture network complexity in the related technology is limited and has low versatility, resulting in high time and material costs.

[0123] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0124] It should be noted that the first determination module 1002, the acquisition module 1004, and the second determination module 1006 correspond to steps S102 to S106 in the embodiment, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the modules as part of the device can be run in a computer terminal.

[0125] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

[0126] The above-mentioned seam network complexity determination device may also include a processor and a memory. The first determination module 1002, the acquisition module 1004, the second determination module 1006, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0127] The processor includes a kernel, which retrieves the corresponding program unit from the memory. There can be one or more kernels. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0128] An embodiment of the present invention provides a non-volatile storage medium on which a program is stored. When the program is executed by a processor, a method for determining seam network complexity is implemented.

[0129] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and can be run on the processor. When the processor executes the program, the following steps are implemented: determining the geomechanical parameters corresponding to the fracturing section; obtaining the fracturing transformation parameters and oil and gas production capacity corresponding to the above-mentioned fracturing section, wherein the above-mentioned fracturing transformation parameters are parameters for fracturing the above-mentioned fracturing section to obtain oil and gas production; based on the above-mentioned geomechanical parameters, the above-mentioned fracturing transformation parameters and the above-mentioned oil and gas production capacity, determining the complexity of the fracture network in the above-mentioned fracturing section. The device in this article can be a server, a PC, etc.

[0130] The present invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with the following method steps: determining geomechanical parameters corresponding to a fracturing segment; obtaining fracturing transformation parameters and oil and gas production capacity corresponding to the above-mentioned fracturing segment, wherein the above-mentioned fracturing transformation parameters are parameters for fracturing the above-mentioned fracturing segment to obtain oil and gas production; based on the above-mentioned geomechanical parameters, the above-mentioned fracturing transformation parameters and the above-mentioned oil and gas production capacity, determining the complexity of the fracture network in the above-mentioned fracturing segment.

[0131] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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-ROM, optical storage, etc.) containing computer-usable program code.

[0132] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0133] 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.

[0134] 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 The steps for the functions specified in one or more boxes.

[0135] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0136] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0137] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0138] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0139] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may 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-ROM, optical storage, etc.) containing computer-usable program codes.

[0140] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for determining seam network complexity, characterized in that: include: Determine the geomechanical parameters corresponding to the fracturing stage; Acquire the fracturing transformation parameters and oil and gas production capacity corresponding to the fracturing section, wherein the fracturing transformation parameters are parameters for fracturing the fracturing section to obtain oil and gas production; Based on the geomechanical parameters, the fracturing transformation parameters and the oil and gas production capacity, the complexity of the fracture network in the fracturing section is determined.

2. The method according to claim 1, characterized in that Determining the geomechanical parameters corresponding to the fracturing stage includes: In the case where the geomechanical parameter includes a microfracture index, obtaining a core sample in the fracturing section, wherein the microfracture index represents the ability of forming a fracture network in the fracturing section; Performing component analysis on the core sample to determine the component quantities of multiple mineral components included in the core sample, and determining a mineral difference factor corresponding to the core sample, wherein the mineral difference factor represents the difference between the multiple mineral components; The microcrack index corresponding to the fracturing stage is determined based on the mineral difference factor and the component quantity.

3. The method according to claim 2, characterized in that Determining the mineral difference factor corresponding to the core sample includes: Determine the mass percentage of the multiple mineral components in the core sample; The mineral difference factor is determined based on the mass percentages corresponding to the multiple mineral components and the quantity of the components.

4. The method according to claim 1, characterized in that: The determining of the complexity of the fracture network corresponding to the fracturing section based on the geomechanical parameters, the fracturing transformation parameters and the oil and gas production capacity includes: Determine a first correlation coefficient representing the correlation between the fracturing transformation parameter and the oil and gas productivity, and a second correlation coefficient representing the correlation between the geomechanical parameter and the oil and gas productivity; The fracture network complexity is determined based on a first correlation coefficient corresponding to the hydraulic fracturing stimulation parameter, a second correlation coefficient corresponding to the geomechanical parameter, the hydraulic fracturing stimulation parameter, and the geomechanical parameter.

5. The method according to claim 4, characterized in that The determining of the fracture network complexity based on the first correlation coefficient corresponding to the hydraulic fracturing stimulation parameter, the second correlation coefficient corresponding to the geomechanical parameter, the hydraulic fracturing stimulation parameter, and the geomechanical parameter includes: Summing the first correlation coefficient corresponding to the hydraulic fracturing transformation parameter and the second correlation coefficient corresponding to the geomechanical parameter to obtain a total coefficient value; Determining a first weight value corresponding to the hydraulic fracturing transformation parameter based on a ratio of a first correlation coefficient corresponding to the hydraulic fracturing transformation parameter to the total value of the coefficients; Determining a second weight value corresponding to the geomechanical parameter based on a ratio of a second correlation coefficient corresponding to the geomechanical parameter to the total value of the coefficients; The fracture network complexity is determined based on a first weight value corresponding to the hydraulic fracturing stimulation parameter, a second weight value corresponding to the geomechanical parameter, the hydraulic fracturing stimulation parameter, and the geomechanical parameter.

6. The method according to claim 5, characterized in that The determining of the fracture network complexity based on the first weight value corresponding to the hydraulic fracturing modification parameter, the second weight value corresponding to the geomechanical parameter, the hydraulic fracturing modification parameter, and the geomechanical parameter includes: In the case where there are multiple fracturing stages, and the multiple fracturing stages correspond to fracturing transformation parameters and geomechanical parameters respectively, determining first normalized values ​​of the fracturing transformation parameters respectively corresponding to the multiple fracturing stages, and determining second normalized values ​​of the geomechanical parameters respectively corresponding to the multiple fracturing stages; The fracture network complexity is determined based on first normalized values ​​of fracturing transformation parameters respectively corresponding to the multiple fracturing stages, second normalized values ​​of geomechanical parameters respectively corresponding to the multiple fracturing stages, first weight values ​​corresponding to the fracturing transformation parameters respectively corresponding to the multiple fracturing stages, and second weight values ​​corresponding to the geomechanical parameters respectively corresponding to the multiple fracturing stages.

7. The method according to any one of claims 1 to 6, characterized in that The fracturing transformation parameters include the number of fracturing clusters, the liquid injection intensity, and the sand addition intensity. The geomechanical parameters include the microcrack index and the horizontal ground stress difference. The number of fracturing clusters is the number of fracture clusters obtained by fracturing the fracturing section, the liquid injection intensity is the amount of liquid injected into the fracturing section, the sand addition intensity is the amount of sand injected into the fracturing section, the microcrack index represents the ability to form a fracture network in the fracturing section, and the horizontal ground stress difference is the difference between the maximum horizontal ground stress of the fracturing section and the minimum horizontal ground stress of the fracturing section.

8. A device for determining seam network complexity, characterized in that: include: A first determination module is used to determine the geomechanical parameters corresponding to the fracturing stage; An acquisition module, used to acquire the fracturing transformation parameters and oil and gas production capacity corresponding to the fracturing section, wherein the fracturing transformation parameters are parameters for fracturing the fracturing section to obtain oil and gas production; The second determination module is used to determine the complexity of the fracture network in the fracturing section based on the geomechanical parameters, the fracturing transformation parameters and the oil and gas production capacity.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the seam network complexity determination method described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the seam network complexity determination method described in any one of claims 1 to 7.

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