Reservoir fracturing influence factor analysis method and device, electronic equipment and medium

Abstract

The invention discloses a reservoir fracturing influence factor analysis method and device, electronic equipment and a medium. The method comprises the following steps: determining a fracturing degree and a plurality of influence factors thereof; initializing the weight, and calculating the distance between each influence factor and other influence factors in sequence; aiming at each influence factor and the fracturing degree corresponding to the influence factor, establishing a same-group sample set and a different-group sample set; according to the same-group sample set and the different-group sample set, the weight is updated, and the updated weight is the sensitivity degree of the corresponding influence factor. According to the method, shale reservoir fracturing influence factor analysis is carried out based on data-driven supervised learning.

Description

Technical Field

[0001] The present invention relates to the field of reservoir fracturing analysis, and more particularly, to a method, device, electronic device and medium for analyzing factors affecting reservoir fracturability. Background Art

[0002] The fracturability of shale reservoirs generally refers to the possibility that shale reservoirs can be effectively fractured under the conditions of hydraulic fracturing. Fracturability is the most critical evaluation index in the process of shale development. Generally, the fracturability of shale gas can be divided into two categories according to the data sources: one is through numerical simulation, and the other is through laboratory petrophysical determination. The former has the advantages of high flexibility and low cost. However, numerical simulation is usually based on certain assumptions and simplifications, and its accuracy and reliability will be affected to a certain extent in this process. The biggest advantage of the latter is its directness, speed and efficiency, but the cost may be high. Whether it is numerical simulation or laboratory petrophysical determination, the ultimate goal is to obtain some geological parameters of shale reservoirs, such as lithology, porosity, permeability, gas content, organic carbon content, elastic modulus, Poisson's ratio, formation pressure and other data (most of which come from logging, logging interpretation results, or from laboratories), the burial depth of the reservoir, the thickness of the reservoir, elastic modulus, Poisson's ratio, formation pressure, in-situ stress (from seismic) and other engineering parameters such as horizontal section length, high-quality reservoir encounter rate, sectioning and clustering, fracturing scale, etc., and determine the fracturability of shale reservoirs and evaluate the fracturability of shale reservoirs through these parameters. Due to the large number of parameters, how to find sensitive parameters or combinations of sensitive parameters from among these numerous parameters is the most urgent task for evaluating the fracturability of shale gas field reservoirs.

[0003] Currently, there is still a need to develop a method for analyzing factors affecting reservoir fracturability.

[0004] The information disclosed in the background art section of the present invention is only intended to deepen the understanding of the general background art of the present invention, and should not be regarded as an admission or any form of suggestion that this information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, device, electronic device and medium for analyzing factors affecting reservoir fracturability, which can realize the analysis of factors affecting the fracturability of shale reservoirs through data-driven supervised learning.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for analyzing factors affecting reservoir fracturability, including:

[0007] Determine the degree of fracturing and its multiple influencing factors;

[0008] Initialize the weights and calculate the distance between each influencing factor and other influencing factors in sequence;

[0009] For each influencing factor and its corresponding fracturing degree, establish a same-group sample set and a different-group sample set;

[0010] Update the weights according to the same-group sample set and the different-group sample set, and the updated weights are the sensitivity degrees of the corresponding influencing factors.

[0011] As a specific implementation manner of the embodiments of the present disclosure, calculating the distance between each influencing factor and other influencing factors is:

[0012]

[0013] As a specific implementation manner of the embodiments of the present disclosure, for each influencing factor and its corresponding fracturing degree, the same-group sample set established includes:

[0014] Select samples with the smallest distance from this influencing factor and the same fracturing degree as it to form the same-group sample set of this influencing factor.

[0015] As a specific implementation manner of the embodiments of the present disclosure, for each influencing factor and its corresponding fracturing degree, the different-group sample set established includes:

[0016] For each of all other fracturing degree values different from this fracturing degree, find corresponding multiple samples adjacent to this influencing factor to form the different-group sample set of this influencing factor.

[0017] As a specific implementation manner of the embodiments of the present disclosure, updating the weights according to the same-group sample set and the different-group sample set includes:

[0018] If the target of sample x jr is the same as the target of x tr , then there is:

[0019]

[0020] As a specific implementation manner of the embodiments of the present disclosure, updating the weights according to the same-group sample set and the different-group sample set includes:

[0021] If the target of sample x jr is different from the target of x tr , then there is:

[0022]

[0023] where p yj represents the probability of the fracturing effect category to which sample x jr belongs, pyt Denote the sample as x tr The probability of the fracturing effect category to which it belongs μ is an integer parameter greater than 1

[0024] As a specific implementation manner of the embodiments of the present disclosure, it further includes:

[0025] Calculate the sensitivity of the influencing factors through different supervised learning algorithms, and the final sensitivity of the influencing factor is the average value of the sensitivities obtained by different learning algorithms

[0026] In a second aspect, the embodiments of the present disclosure further provide an analysis device for influencing factors of reservoir fracturability, including:

[0027] A determination module for determining the fracturing degree and its multiple influencing factors

[0028] An initialization module for initializing weights and calculating the distance between each influencing factor and other influencing factors in sequence

[0029] A sample set establishment module for establishing a same-group sample set and a different-group sample set for each influencing factor and its corresponding fracturing degree

[0030] A calculation module for updating the weights according to the same-group sample set and the different-group sample set, and the updated weights are the sensitivities of the corresponding influencing factors

[0031] As a specific implementation manner of the embodiments of the present disclosure, calculating the distance between each influencing factor and other influencing factors is:

[0032]

[0033] As a specific implementation manner of the embodiments of the present disclosure, establishing a same-group sample set for each influencing factor and its corresponding fracturing degree includes:

[0034] Select samples with the smallest distance from this influencing factor and the same fracturing degree to form the same-group sample set of this influencing factor

[0035] As a specific implementation manner of the embodiments of the present disclosure, establishing a different-group sample set for each influencing factor and its corresponding fracturing degree includes:

[0036] For all other fracturing degree values different from this fracturing degree, find corresponding multiple samples adjacent to this influencing factor to form the different-group sample set of this influencing factor

[0037] As a specific implementation manner of the embodiments of the present disclosure, updating the weight according to the same-group sample set and the different-group sample set includes:

[0038] If the target of sample x jr is the same as the target of x tr , then there is:

[0039]

[0040] As a specific implementation manner of the embodiments of the present disclosure, updating the weight according to the same-group sample set and the different-group sample set includes:

[0041] If the target of sample x jr is different from the target of x tr , then there is:

[0042]

[0043] Wherein, p yj represents the probability of the fracturing effect category to which sample x jr belongs, and p yt represents the probability of the fracturing effect category to which sample x tr belongs, μ is an integer parameter greater than 1.

[0044] As a specific implementation manner of the embodiments of the present disclosure, it further includes:

[0045] Calculating the sensitivity of the influencing factors through different supervised learning algorithms, and the final sensitivity of the influencing factors is the average value of the sensitivities obtained by different learning algorithms.

[0046] In a third aspect, an embodiment of the present disclosure further provides an electronic device, which includes:

[0047] A memory storing executable instructions;

[0048] A processor, the processor runs the executable instructions in the memory to implement the method for analyzing the influencing factors of reservoir fracturability.

[0049] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for analyzing the influencing factors of reservoir fracturability is implemented.

[0050] The methods and apparatuses of the present invention have other characteristics and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and the following detailed description, which are incorporated herein and together serve to explain the specific principles of the present invention. Description of the Drawings

[0051] The above and other objects, features, and advantages of the present invention will become more apparent by describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, in which like reference numerals generally represent like components in the exemplary embodiments of the present invention.

[0052] Figure 1 A flowchart showing the steps of a method for analyzing factors affecting reservoir fracturability according to an embodiment of the present invention is shown.

[0053] Figure 2 A schematic diagram of a histogram of sensitivity levels according to an embodiment of the present invention is shown.

[0054] Figure 3 A schematic diagram of a target-parameter sensitivity matrix according to an embodiment of the present invention is shown.

[0055] Figure 4 A block diagram of an apparatus for analyzing factors affecting reservoir fracturability according to an embodiment of the present invention is shown.

[0056] Description of the Reference Numerals:

[0057] 201, determination module; 202, initialization module; 203, sample set establishment module; 204, calculation module. Detailed Embodiments

[0058] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0059] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that these examples are only for facilitating the understanding of the present invention, and no specific details are intended to limit the present invention in any way.

[0060] Example 1

[0061] Figure 1 A flowchart showing the steps of a method for analyzing factors affecting reservoir fracturability according to an embodiment of the present invention is shown.

[0062] As Figure 1As shown in the figure, the method for analyzing the factors affecting the fracturability of the reservoir includes: Step 101, determining the fracturing degree and its multiple influencing factors; Step 102, initializing the weights and calculating the distance between each influencing factor and other influencing factors in sequence; Step 103, for each influencing factor and its corresponding fracturing degree, establishing a same-group sample set and a different-group sample set; Step 104, updating the weights according to the same-group sample set and the different-group sample set, and the updated weights are the sensitivity degrees of the corresponding influencing factors.

[0063] In one example, calculating the distance between each influencing factor and other influencing factors is:

[0064]

[0065] In one example, for each influencing factor and its corresponding fracturing degree, establishing a same-group sample set includes:

[0066] Selecting the samples with the smallest distance from this influencing factor and the same fracturing degree to form the same-group sample set of this influencing factor.

[0067] In one example, for each influencing factor and its corresponding fracturing degree, establishing a different-group sample set includes:

[0068] For all other fracturing degree values that are different from this fracturing degree, for each of them, finding a plurality of samples adjacent to this influencing factor to form the different-group sample set of this influencing factor.

[0069] In one example, updating the weights according to the same-group sample set and the different-group sample set includes:

[0070] If the target of sample x jr is the same as the target of x tr , then there is:

[0071]

[0072] In one example, updating the weights according to the same-group sample set and the different-group sample set includes:

[0073] If the target of sample x jr is different from the target of x tr , then there is:

[0074]

[0075] where p yj represents the probability of the fracturing effect category to which sample x jr belongs, and p yt represents the probability of the fracturing effect category to which sample x tr belongs, μ is an integer parameter greater than 1.

[0076] In one example, it further includes:

[0077] Calculate the sensitivity of influencing factors through different supervised learning algorithms, and the final sensitivity of the influencing factor is the average of the sensitivities obtained by different learning algorithms.

[0078] Specifically, determine the fracturing degree and its multiple influencing factors; initialize the weights and calculate the distance between each influencing factor and other influencing factors in turn:

[0079]

[0080] For each influencing factor and its corresponding fracturing degree, establish a same-group sample set and a different-group sample set:

[0081] Select the samples with the smallest distance from this influencing factor and the same fracturing degree to form the same-group sample set of this influencing factor;

[0082] For all other fracturing degree values different from this fracturing degree, find the corresponding multiple samples adjacent to this influencing factor to form the different-group sample set of this influencing factor.

[0083] Update the weights according to the same-group sample set and the different-group sample set:

[0084] If the target of sample x jr is the same as the target of x tr then there is:

[0085]

[0086] If the target of sample x jr is different from the target of x tr then there is:

[0087]

[0088] where p yj represents the probability of the fracturing effect category to which sample x jr belongs, p yt represents the probability of the fracturing effect category to which sample x tr belongs, and the updated weight is the sensitivity of the corresponding influencing factor.

[0089] Calculate the sensitivity of influencing factors through different supervised learning algorithms, and the final sensitivity of the influencing factor is the average of the sensitivities obtained by different learning algorithms.

[0090] Example 2

[0091] The present invention also provides an analysis device for factors affecting the fracturability of a reservoir, including:

[0092] A determination module, which determines the fracturing degree and its multiple influencing factors;

[0093] An initialization module, which initializes weights and calculates the distance between each influencing factor and other influencing factors in sequence;

[0094] A sample set establishment module, which establishes a same-group sample set and a different-group sample set for each influencing factor and its corresponding fracturing degree;

[0095] A calculation module, which updates the weights according to the same-group sample set and the different-group sample set, and the updated weights are the sensitivity degrees of the corresponding influencing factors.

[0096] In one example, calculating the distance between each influencing factor and other influencing factors is:

[0097]

[0098] In one example, for each influencing factor and its corresponding fracturing degree, establishing the same-group sample set includes:

[0099] Selecting the samples with the smallest distance from this influencing factor and the same fracturing degree to form the same-group sample set of this influencing factor.

[0100] In one example, for each influencing factor and its corresponding fracturing degree, establishing the different-group sample set includes:

[0101] For all other fracturing degree values different from this fracturing degree, finding multiple samples adjacent to this influencing factor for each to form the different-group sample set of this influencing factor.

[0102] In one example, updating the weights according to the same-group sample set and the different-group sample set includes:

[0103] If the target of sample x jr is the same as the target of x tr then there is:

[0104]

[0105] In one example, updating the weights according to the same-group sample set and the different-group sample set includes:

[0106] If the target of sample x jr is different from the target of x tr then there is:

[0107]

[0108] Among them, p yj represents the probability of the fracturing effect category to which the sample x jr belongs, and p yt represents the probability of the fracturing effect category to which the sample x tr belongs, μ is an integer parameter greater than 1.

[0109] In one example, it further includes:

[0110] Calculate the sensitivity of the influencing factors through different supervised learning algorithms, and the final sensitivity of the influencing factor is the average of the sensitivities obtained by different learning algorithms.

[0111] Specifically, determine the fracturing degree and its multiple influencing factors; initialize the weights, and calculate the distance between each influencing factor and other influencing factors in turn:

[0112]

[0113] For each influencing factor and its corresponding fracturing degree, establish a same-group sample set and a different-group sample set:

[0114] Select the samples with the smallest distance from the influencing factor and the same fracturing degree to form the same-group sample set of the influencing factor;

[0115] For all other fracturing degree values different from this fracturing degree, find the corresponding multiple samples adjacent to the influencing factor for each to form the different-group sample set of the influencing factor.

[0116] Update the weights according to the same-group sample set and the different-group sample set:

[0117] If the target of the sample x jr is the same as the target of x tr , then there is:

[0118]

[0119] If the target of the sample x jr is different from the target of x tr , then there is:

[0120]

[0121] Among them, p yj represents the probability of the fracturing effect category to which the sample x jr belongs, and p yt represents the probability of the fracturing effect category to which the sample x tr belongs. The updated weight is the sensitivity of the corresponding influencing factor.

[0122] Calculate the sensitivity of influencing factors through different supervised learning algorithms, and the final sensitivity of the influencing factor is the average of the sensitivities obtained by different learning algorithms.

[0123] Example 3

[0124] First, based on the laboratory measurements of the natural fracture development degree, natural fracture effectiveness, and porosity, permeability, elastic modulus, Poisson's ratio, mineral composition (quartz and other mineral contents), TOC content, gas content (or free gas content), brittleness and other fracturability influencing factors (parameters) of the core, then conduct fracturing on the corresponding core in the laboratory (simulating actual engineering fracturing), use XTR to detect and determine the fracturing fracture height, and use the average value of the fracture porosity, fracture dip dispersion, and the normalized values of fracture height and fracture aperture to characterize the fracture complexity. Define the core fracturability index as the ratio of fracture complexity to strength, and combine the pressure drop data after fracturing to observe and evaluate the fracturing effect. Then, the above parameters and the fracturing effect evaluation results form the training sample data.

[0125] The above laboratory core measurement data and its fracturing evaluation results together constitute the training dataset D(X ij ,Y i ), where X ij represents the measurement data of each core, where i = 1, 2,..., N represents the data point serial number, and j = 1, 2,..., P represents the variable serial number (data dimension) of the data at each data point, and Y i represents the fracturing degree (effect).

[0126] Assume that the probability of a certain category effect k in the above fracturing effect judgment appearing in the whole category is: p k , then 0 < p k < 1, and The process of analyzing the fracturability influencing factors through supervised learning based on the above data is as follows:

[0127] S1, w r can all be initialized to 0, r = 1, 2,..., p;

[0128] S2, calculate the distance between the sample x i. and all other samples x j. :

[0129]

[0130] S3, for each sample x t. and the target y t , select the one with the smallest distance from the sample x t. and whose target is the same as yt The same M samples form the sample x t. 's homologous sample set H(x t. ). Meanwhile, for all other target values that are different from the target y of x t. , for each one, find the corresponding M samples adjacent to x t to form the heterogeneous sample set M(x t. ). t. ). t. )

[0131] S4. Then, for each factor (parameter) affecting the fracturability, calculate the attribute weight in the following form:

[0132] If the target of the sample x j. is the same as the target of x t. , then:

[0133]

[0134] Otherwise:

[0135]

[0136] Among them, p y. represents the probability of the fracturing effect category to which the sample belongs, and the final weight represents the sensitivity of each parameter.

[0137] Figure 2 shows a schematic diagram of the sensitivity histogram according to an embodiment of the present invention.

[0138] Figure 3 shows a schematic diagram of the target-parameter sensitivity matrix according to an embodiment of the present invention.

[0139] For each fracturing effect category, calculate the sensitivity of each parameter in the above manner in sequence and display it in the form of a histogram, as Figure 2 shown; the parameter sensitivities of all fracturing effect categories form a target-parameter sensitivity matrix and are displayed in the form of a matrix diagram, as Figure 3 shown.

[0140] The above process can be carried out one by one for different supervised learning algorithms. Suppose there are T different supervised learning algorithms: H 1 , H 2 , …, H T , and the sensitivity of each parameter is obtained by each learning algorithm in the above manner. Then the final sensitivity of this influencing factor is the average of the sensitivities obtained by different learning algorithms.

[0141] Example 4

[0142] Figure 4 The block diagram of an analysis device for factors affecting reservoir fracturability according to an embodiment of the present invention is shown.

[0143] As Figure 4 shown, the analysis device for factors affecting reservoir fracturability includes:

[0144] A determination module 201 for determining the fracturing degree and its multiple influencing factors;

[0145] An initialization module 202 for initializing weights and calculating the distance between each influencing factor and other influencing factors in sequence;

[0146] A sample set establishment module 203 for establishing a same-group sample set and a different-group sample set for each influencing factor and its corresponding fracturing degree;

[0147] A calculation module 204 for updating the weights according to the same-group sample set and the different-group sample set, and the updated weights are the sensitivity degrees of the corresponding influencing factors.

[0148] As an optional solution, calculating the distance between each influencing factor and other influencing factors is:

[0149]

[0150] As an optional solution, for each influencing factor and its corresponding fracturing degree, establishing the same-group sample set includes:

[0151] Selecting the samples with the smallest distance from this influencing factor and the same fracturing degree to form the same-group sample set of this influencing factor.

[0152] As an optional solution, for each influencing factor and its corresponding fracturing degree, establishing the different-group sample set includes:

[0153] For all other fracturing degree values different from this fracturing degree, finding a plurality of samples adjacent to this influencing factor for each to form the different-group sample set of this influencing factor.

[0154] As an optional solution, updating the weights according to the same-group sample set and the different-group sample set includes:

[0155] If the target of sample x jr is the same as the target of x tr then there is:

[0156]

[0157] As an optional solution, updating the weights according to the same-group sample set and the different-group sample set includes:

[0158] If sample xjr The target of tr is different from that of x, then there is:

[0159]

[0160] where p yj represents the probability of the sample x jr belonging to the fracturing effect category, and p yt represents the probability of the sample x tr belonging to the fracturing effect category. μ is an integer parameter greater than 1.

[0161] As an alternative, it also includes:

[0162] Calculate the sensitivity of the influencing factors through different supervised learning algorithms, and the final sensitivity of the influencing factor is the average of the sensitivities obtained by different learning algorithms.

[0163] Example 5

[0164] This embodiment provides an electronic device, which includes: a memory storing executable instructions; a processor that runs the executable instructions in the memory to implement the above-mentioned method for analyzing the influencing factors of reservoir fracturability.

[0165] The electronic device according to the embodiment of the present disclosure includes a memory and a processor.

[0166] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0167] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0168] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of the present disclosure.

[0169] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0170] Example 6

[0171] This embodiment provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for analyzing influencing factors of reservoir fracturability is implemented.

[0172] According to the computer-readable storage medium of the embodiments of the present disclosure, non-temporary computer-readable instructions are stored thereon. When the non-temporary computer-readable instructions are run by a processor, all or part of the steps of the methods of the foregoing embodiments of the present disclosure are executed.

[0173] The above computer-readable storage medium includes but is not limited to: optical storage media (such as CD-ROMs and DVDs), magneto-optical storage media (such as MOs), magnetic storage media (such as magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (such as memory cards), and media with built-in ROMs (such as ROM cartridges).

[0174] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0175] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for analyzing influencing factors of reservoir fracturability, characterized in that, it includes: Determine the fracturing degree and its multiple influencing factors; Initialize the weights, and calculate the distance between each influencing factor and other influencing factors in sequence; For each influencing factor and its corresponding fracturing degree, establish a same-group sample set and a different-group sample set; According to the same-group sample set and the different-group sample set, update the weights, and the updated weights are the sensitivity degrees of the corresponding influencing factors.

2. The method for analyzing influencing factors of reservoir fracturability according to claim 1, wherein, Calculating the distance between each influencing factor and other influencing factors is:

3. The method for analyzing influencing factors of reservoir fracturability according to claim 1, wherein, For each influencing factor and its corresponding fracturing degree, establishing a same-group sample set includes: Select samples with the smallest distance from this influencing factor and the same fracturing degree to form the same-group sample set of this influencing factor.

4. The method for analyzing influencing factors of reservoir fracturability according to claim 1, wherein, For each influencing factor and its corresponding fracturing degree, establishing a different-group sample set includes: For all other fracturing degree values that are different from this fracturing degree, find corresponding multiple samples adjacent to this influencing factor to form the different-group sample set of this influencing factor.

5. The method for analyzing influencing factors of reservoir fracturability according to claim 1, wherein, According to the same-group sample set and the different-group sample set, updating the weights includes: If the sample x jr has the same target as x tr , then we have:

6. The method for analyzing influencing factors of reservoir fracturability according to claim 1, wherein, According to the same-group sample set and the different-group sample set, updating the weights includes: If the sample x jr has a different target from x tr , then there is: Among them, p yj represents the probability of the fracturing effect category to which the sample x jr belongs, and p yt represents the probability of the fracturing effect category to which the sample x tr belongs, μ is an integer parameter greater than 1.

7. The method for analyzing influencing factors of reservoir fracturability according to claim 1, wherein, It further includes: Calculate the sensitivity degree of the influencing factor through different supervised learning algorithms, and the final sensitivity degree of this influencing factor is the average value of the sensitivity degrees obtained by different learning algorithms.

8. An apparatus for analyzing influencing factors of reservoir fracturability, characterized in that, it includes: A determination module for determining the fracturing degree and its multiple influencing factors; An initialization module for initializing the weights and calculating the distance between each influencing factor and other influencing factors in sequence; A sample set establishment module for establishing a same-group sample set and a different-group sample set for each influencing factor and its corresponding fracturing degree; A calculation module for updating the weights according to the same-group sample set and the different-group sample set, and the updated weights are the sensitivity degrees of the corresponding influencing factors.

9. An electronic device, characterized in that, the electronic device includes: A memory storing executable instructions; A processor that runs the executable instructions in the memory to implement the method for analyzing influencing factors of reservoir fracturability according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, this computer-readable storage medium stores a computer program, and when this computer program is executed by a processor, it implements the method for analyzing influencing factors of reservoir fracturability according to any one of claims 1-7.

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