Geology-engineering double sweet spot based compressibility evaluation and perforation optimization method

Abstract

The present application relates to the geological-engineering double dessert-based compressibility evaluation and perforation optimization method, the compressibility evaluation method includes steps: S1, selecting the dessert evaluation parameter of target reservoir, the dessert evaluation parameter includes the geological dessert parameter and the engineering dessert parameter; S2, using the dessert evaluation parameter of multiple sounding of target reservoir, constructing evaluation matrix; S3, the weighted standardization processing is carried out to evaluation matrix, and the weight value of each dessert evaluation parameter is obtained, and the corresponding comparison matrix is established; S4, the compressibility evaluation index is calculated using the Euclidean distance method.The present application provides a kind of compressibility evaluation index evaluation method, according to compressibility index, consider the perforation cluster number in fracturing section and cluster spacing etc.Parameter has constructed the fracturing perforation position optimization model, can accurately locate perforation point, provides theoretical method for optimizing shale gas reservoir fracturing design.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration and development technology, and in particular to a compressibility evaluation and perforation optimization method based on a geological-engineering double sweet spot. Background Technology

[0002] China possesses abundant unconventional oil and gas resources, including tight gas, shale gas, and shale oil. Successful exploration and development of these resources have benefited from large-scale volumetric fracturing, which effectively "liberates" low-permeability reservoirs. During the stimulation of low-permeability reservoirs, the larger the stimulation volume and the more complex the fracture network, the higher the production of unconventional oil and gas wells. However, excellent reservoir performance and an ideal reservoir stimulation volume (SRV) are prerequisites for achieving economical production capacity. Due to differences in geological characteristics and data collection levels, the evaluation methods for reservoirs vary significantly across different work areas. The reservoir compressibility sweet spot is a crucial component in the evaluation of unconventional reservoirs such as tight gas, shale gas, and shale oil, and is of great significance for achieving large-scale, efficient development of unconventional oil and gas.

[0003] The compressibility sweet spot typically refers to a region in oil and gas reservoirs that is of good quality and easy to develop. It represents areas in shale oil and gas exploration and development that are easily fractured and have relatively high shale gas production. Many scholars have studied reservoir fracturing capabilities in existing technologies, characterizing them using factors such as reservoir rock brittleness, fracture toughness, in-situ stress, and the development of natural fractures. However, some existing compressibility evaluation methods focus on increasing the stimulation volume, assessing the ease of fracturing and the formation of complex fracture networks, without considering reservoir physical parameters. This leads to uncertainty in the selection and design of multi-cluster perforation locations in horizontal wells, and fails to comprehensively consider the relationship between shale reservoir physical properties and compressibility (i.e., whether horizontal well sections with large SRVs simultaneously possess high-quality reservoirs or high gas content). This leaves the direction for technical issues such as production well placement and selection of advantageous fracturing sections unclear. To achieve efficient reservoir development and maximize benefits, a comprehensive evaluation of reservoir compressibility must consider both geological and engineering sweet spot parameters. Furthermore, in the perforation application of existing technology for compressibility evaluation, there is a deficiency that the constraints of the number of clusters and the spacing between clusters within the fracturing section are not considered. Summary of the Invention

[0004] This invention provides a compressibility evaluation and perforation optimization method based on a geological-engineering double sweet spot. This method applies an improved TOPSIS evaluation method and analytic hierarchy process (AHP) to establish a comprehensive evaluation method for the compressibility of multi-source, dimension-reduced geological-engineering double sweet spot. A compressibility index is used to evaluate the reservoir. An unsupervised k-means clustering algorithm based on Euclidean distance is used to perform cluster analysis on the compressibility evaluation results, and a fracturing sweet spot optimization model is constructed with the objective of maximizing the compressibility of the geological-engineering double sweet spot at the optimal perforation location. This achieves precise location of the fracturing sweet spot. To achieve the above objectives, this invention provides the following solution:

[0005] A compressibility evaluation method based on a geological-engineering double sweet spot includes the following steps:

[0006] S1: Select sweet spot evaluation parameters for the target reservoir, including geological sweet spot parameters and engineering sweet spot parameters;

[0007] S2: Construct an evaluation matrix using sweet spot evaluation parameters from multiple depth measurements of the target reservoir;

[0008] S3: Perform weighted standardization on the evaluation matrix to obtain the weight values ​​of each dessert evaluation parameter and establish the corresponding comparison matrix;

[0009] S4: Calculate the compressibility evaluation index using the Euclidean distance method.

[0010] Furthermore, the geological sweet spot parameters include porosity, total organic carbon content, and gas content, while the engineering sweet spot parameters include rock brittleness index, fracture toughness, and reservoir in-situ stress.

[0011] Furthermore, step S2 also includes:

[0012] S21: Select n geological sweet spot parameters and engineering sweet spot evaluation parameters, obtain geological sweet spot parameters and engineering sweet spot evaluation parameters for m sounding locations, and construct the original evaluation matrix X from the n geological and engineering sweet spot parameters corresponding to the m sounding locations:

[0013]

[0014] Where: i = 1, 2, ..., m, j = 1, 2, ..., n; x ij This represents the specific value of the j-th evaluation parameter at the i-th data point;

[0015] S22: Standardize the evaluation parameters.

[0016] Furthermore, the standardization processing method in step S22 is as follows:

[0017] The standardization method for positive indicators is as follows:

[0018]

[0019] The standardization method for negative indicators is as follows:

[0020]

[0021] Obtain the processed standardized matrix R

[0022] R = (r ij ) m×n

[0023] In the formula: r ij The evaluation data is standardized and dimensionless; R is the standardization matrix.

[0024] Furthermore, step S3 also includes determining the weights of the dessert evaluation parameters using the analytic hierarchy process (AHP), specifically including:

[0025] S31: Construct a hierarchical structure for the evaluation matrix;

[0026] S32: Construct the comparison matrix;

[0027] S33: Calculate the weights of each dessert evaluation parameter;

[0028] S34: Perform a consistency check on the constructed comparison matrix. If the consistency check passes, determine the weights directly; if the check fails, readjust the comparison matrix until the check passes.

[0029] Furthermore, step S4 also includes:

[0030] Calculate the distance between the evaluation index value at point i in the fracturing segment and the ideal value sequence of the evaluation index:

[0031]

[0032]

[0033]

[0034] Wherein, SSF is the compressibility evaluation index, which is dimensionless; d i For European-style approximation, dimensionless; The positive Euclidean distance is dimensionless. It is a negative Euclidean distance, dimensionless.

[0035] On the other hand, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, cause the processor to perform the compressibility evaluation method based on the geological-engineering double sweet spot as described in any of the preceding claims.

[0036] On the other hand, the present invention provides a method for selecting the optimal perforation location, which uses the above-mentioned compressibility evaluation method to determine the compressibility of the fracturing section, and further includes the following steps:

[0037] Determine the depth range, cluster spacing range, and number of perforation clusters for the selected fracturing section. Consider the influence of the fracturing section length, number of perforation clusters, and cluster spacing to determine the perforation location.

[0038] Furthermore, it also includes:

[0039] Assume the depth measurement range of the selected fracturing section is L1 to L2, the cluster spacing is fixed at a to b, the number of perforation clusters is k, and the corresponding compressibility index is SSF. i ;

[0040] (1) Considering the cluster spacing constraint,

[0041] a≤MD i+1 -MD i ≤b

[0042] In the formula: a and b are the left and right endpoints of the cluster spacing range; MD i+1 -MD i The cluster spacing is in meters.

[0043] (2) Considering cluster number constraints:

[0044] Starting from the initial position of the fracturing section, the maximum number of clusters can be determined by selecting the minimum cluster spacing:

[0045] L1+a(k max -1)=L2

[0046] Solving for:

[0047]

[0048] This determines the range of cluster numbers:

[0049]

[0050] Using depth sounding (MD), compressibility index (SSF), and cluster number (k) as decision variables, the sum of the proximity of the selected perforation points is used as the criterion. To maximize the cluster spacing constraint within the target segment, an optimal point selection model is constructed:

[0051]

[0052]

[0053] This invention has the following advantages: It clarifies the comprehensive influence of the main controlling factors of geological engineering sweet spots, establishes a geological-engineering-based method for evaluating the compressibility of unconventional reservoirs, and constructs a model for optimizing the location of fracturing perforations based on the compressibility index, considering parameters such as the number of perforation clusters and the spacing between clusters within the fracturing section. This model can accurately locate perforation points and provides a theoretical method for optimizing the fracturing design of unconventional reservoirs. It is of great significance for reducing construction costs and increasing the production of gas wells in unconventional reservoirs. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0056] Figure 2 This is a schematic diagram of the compressibility evaluation results of well X1 in an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of the clustering analysis results of the perforation points in an embodiment of the present invention;

[0058] Figure 4 This is a schematic diagram illustrating the recommended perforation location clustering percentage in an embodiment of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] The purpose of this invention is to provide a compressibility evaluation method based on a geological-engineering double sweet spot and a method for optimizing perforation locations. To make the above-mentioned objectives, features, and advantages of this invention more apparent and understandable, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] To address the problems existing in current reservoir compressibility assessment methods, such as Figure 1 As shown, this invention provides a compressibility evaluation method based on a geological-engineering double sweet spot, comprising the following steps:

[0062] S1: Select sweet spot evaluation parameters for the target reservoir, including geological sweet spot parameters and engineering sweet spot parameters.

[0063] There are numerous existing sweet spot evaluation parameters for unconventional reservoirs, and those skilled in the art can select the appropriate parameters based on the specific conditions of the reservoir. In a preferred embodiment of the present invention, porosity, total organic carbon content, and gas content are selected as reservoir geological sweet spot parameters, while rock brittleness index, fracture toughness, and reservoir in-situ stress are commonly selected as engineering sweet spot parameters.

[0064] Porosity can be obtained by fitting conventional well logging data with core test data, thereby enabling well logging identification of shale porosity.

[0065] φ=0.026AC-1.114DEN+0.082URA

[0066] Total organic carbon content can be obtained through multiple regression analysis of well logging data.

[0067] TOC=-0.0455AC-9.3356DEN+0.0141GR+0.0050RT+0.0466SP+23.2504

[0068] Where: AC is the sonic transit time, μs / m; DEN is the density, g / cm3; GR is the natural gamma logging value, API; RT is the resistivity, Ω·m; SP is the spontaneous potential, mV.

[0069] The total gas content of a reservoir can be determined from two aspects: free gas content and adsorbed gas content. The total gas content of the reservoir can be obtained by calculating the free gas content and adsorbed gas content separately, and then summing these two parts.

[0070] V t =V a +V f

[0071] In the formula: V t For the total content, m 3 / t;V a The adsorbed gas content in shale, m 3 / t;V f For free gas content, m 3 / t.

[0072] The formula for calculating the free gas content is:

[0073]

[0074] In the formula: ψ is a constant, taken as 0.91 for shale; B g φ is the gas volume compressibility coefficient, taken as 0.0046 for shale; φ is the effective porosity of the reservoir; Sg This represents the gas saturation level of the reservoir.

[0075] The method for obtaining the rock brittleness index is as follows:

[0076] Calculate the dynamic Young's modulus and Poisson's ratio:

[0077]

[0078]

[0079] In the formula: E is Young's modulus, MPa; μ is Poisson's ratio, dimensionless; ρ is rock density, kg / m³ 3 V s V is the transverse wave velocity, in m / s; p Let be the longitudinal wave velocity, in m / s.

[0080]

[0081]

[0082]

[0083] In the formula: Brit is the brittleness index, which is dimensionless; E n The normalized Young's modulus, dimensionless; μ n The normalized Poisson's ratio is dimensionless.

[0084] The method for obtaining fracture toughness is as follows:

[0085] K IC =0.2176P c +0.0059S t 3 +0.0923S t 2 +0.517S t -0.3322

[0086] K IIC =0.046P c +0.1674S t -0.1851

[0087] Where: K IC and K IIC These represent the type I and type II fracture toughness of the formation, respectively, in MPa·m. 0.5 ;P c The confining pressure is MPa; S t denoted as σt, where σt is the tensile strength of the reservoir rock, expressed in MPa.

[0088] S2: Construct an evaluation matrix using geological sweet spot parameters and engineering sweet spot parameters from multiple depth measurements of the target reservoir.

[0089] If a total of n geological sweet spot parameters and engineering sweet spot evaluation parameters are selected, and the distribution of sweet spot parameters with well depth is obtained within the fracturing section, and there are m depth measurement locations, then the original geological engineering sweet spot evaluation matrix X can be constructed from the n geological engineering sweet spot parameters corresponding to the m depth measurement locations:

[0090]

[0091] Where: i = 1, 2, ..., m, j = 1, 2, ..., n; x ij Let x represent the specific value of the j-th evaluation index at the i-th data point. If the reservoir porosity is ranked first among the many evaluation indicators, and the porosity value at the second position is 0.2, then x... 21 =0.2.

[0092] To facilitate calculation, the original data needs to be standardized. This application employs an improved standardization method, which considers the differences between indicator values, accurately reflects the relationships between the original parameters, and simultaneously converts both positive and negative indicators into positive indicators. Specifically, the positive indicators are standardized using the following method:

[0093]

[0094] Negative indicators are standardized using the following method:

[0095]

[0096] R = (r ij ) m×n

[0097] In the formula: r ij The evaluation data is standardized and dimensionless; R is the standardization matrix.

[0098] A weighted standardized evaluation matrix is ​​constructed for the evaluation parameters.

[0099] The weighted standardized evaluation matrix mainly includes the following steps:

[0100] ① Define the decision problem and construct a hierarchical analysis structure

[0101] This step requires establishing a hierarchical structure for the evaluation system, mainly consisting of three levels: the objective level, the criteria level, and the scheme level. For this paper, it is only necessary to determine the weight values ​​of the dessert parameters, so it is only necessary to construct a criteria level containing multiple dessert parameters.

[0102] ② Construct a comparison matrix

[0103] Comparison matrix A = (a ij The n×n is derived by comparing the contribution of each factor to a factor in the previous layer pairwise.

[0104] ③ Calculate the weight value of each factor.

[0105] Based on the constructed comparison matrix, the eigenvector corresponding to its largest eigenvalue is calculated. After normalizing the eigenvector, the weight values ​​of each factor can be obtained. The mathematical equation for the weights is shown below.

[0106]

[0107] ④ Determine whether the comparison matrix can pass the consistency test.

[0108] The Analytic Hierarchy Process (AHP) requires a consistency check on the constructed comparison matrix. If the consistency check passes, the weights can be determined directly; if the check fails, the comparison matrix needs to be readjusted until it passes the check.

[0109] Specifically, if the consistency test index C R If C / R < 0.1, the test passes; otherwise, it fails and the comparison matrix needs to be readjusted. The formula for C is as follows, and R is the random consistency index, whose value depends on n.

[0110]

[0111] In the formula: λ max The largest eigenvalue of the comparison matrix A.

[0112] Based on the AHP method and combined with the geological sweet spot evaluation parameters selected in the embodiments of this application—porosity, TOC, and gas content—and the engineering sweet spot evaluation parameters—brittleness index, fracture toughness, and in-situ stress difference—a comparison matrix was constructed as shown in Table 1, and a consistency check was performed. The weight value of each sweet spot evaluation parameter was calculated through mathematical equations of weights. Considering that porosity, TOC, and gas content reflect the hydrocarbon characteristics of the shale reservoir and are the basis for obtaining high production, while the magnitude of the brittleness index directly and significantly affects the fracturing capability and reflects the ease of fracturing to form a fracture network, geological sweet spot parameters were given priority when constructing the comparison matrix, followed by the rock brittleness index. The contribution of fracture toughness and horizontal principal stress difference to the sweet spot was considered relatively weakly.

[0113] Table 1 Comparison Matrix of Dessert Evaluation Parameters

[0114]

[0115] The weights w of each dessert parameter are calculated based on the comparison matrix constructed in Table 1. jAs shown below, the consistency test index C R =0.0552<0.1, passing the consistency test.

[0116] w j =(w1,w2,w3,w4,w5,w6)=(0.1851,0.2523,0.3470,0.1099,0.0646,0.0411)

[0117] After determining the weights of the dessert evaluation parameters using the analytic hierarchy process (AHP), the weighted evaluation matrix and the positive and negative ideal solutions can be constructed:

[0118] Y = (y ij ) m×n =R m×n ×diag(w1,w2,…,w n ) n×n

[0119]

[0120] Where: Y is the weighted standardization matrix; It is a negative ideal solution and is dimensionless. For the positive ideal solution, it is dimensionless.

[0121] For the weighted and normalized evaluation matrix, the Euclidean distance is calculated using the ideal value sequence. This calculates the distance between all evaluation index values ​​at point i in the fracturing segment and the ideal value sequence of the evaluation index. The positive ideal Euclidean distance represents the degree of closeness to the best positive ideal index value; the negative ideal Euclidean distance represents the degree of closeness to the best negative ideal index value.

[0122]

[0123] Based on the positive and negative ideal Euclidean distances, the compressibility evaluation index is calculated using the following formula:

[0124]

[0125] In the formula: SSF is the compressibility evaluation index, which is dimensionless; d i For European approximation, dimensionless; d i + The positive Euclidean distance is dimensionless. It is a negative Euclidean distance, dimensionless.

[0126] The compressibility evaluation index is classified into three categories: Class I, Class II, and Class III, corresponding to good, medium, and poor reservoir sweet spot quality, respectively.

[0127] Specifically, for a given m×n sample:

[0128] R = (r ij ) m×n

[0129] The distance between the samples is:

[0130]

[0131] In the formula: d Ci x is the Euclidean distance from the data object to the cluster center. ij For data sample values; C l It is the l-th cluster center.

[0132] The sum of squared errors (SSE) of the entire dataset is used to characterize the quality of clustering results. The formula for calculating SSE is as follows:

[0133]

[0134] In the formula, the magnitude of J(x,C) indicates the quality of the clustering result; k is the number of cluster points.

[0135] Furthermore, the perforation location can be optimized using the compressibility evaluation method based on a geological-engineering double sweet spot proposed in this invention. The perforation location needs to be optimized within the fracturing section based on the comprehensive sweet spot evaluation results, while also considering factors such as the length of the fracturing section, the number of perforation clusters, and the cluster spacing. Let the depth measurement range of the candidate fracturing section be L1 to L2, the cluster spacing range be fixed at a to b, the number of perforation clusters be k, and the depth measurement of the preferred perforation point be MD. i The corresponding comprehensive compressibility index is SSF. i .

[0136] Selected fracturing location MD i The following constraints must be satisfied:

[0137] (1) Cluster spacing constraint, i.e.:

[0138] a≤MD i+1 -MD i ≤b

[0139] In the formula: a and b are the left and right endpoints of the cluster spacing range; MD i+1 -MD i m is the cluster spacing.

[0140] (2) Cluster number constraint:

[0141] Starting from the initial position of the fracturing section, the maximum number of clusters can be determined by selecting the minimum cluster spacing:

[0142] L1+a(k max -1)=L2

[0143] Solving for:

[0144]

[0145] This allows us to determine the range of cluster numbers:

[0146]

[0147] Using depth sounding (MD), comprehensive compressibility index (SSF), and cluster number (k) as decision variables, the sum of the proximity of the selected perforation points is used as the criterion. With the objective of maximizing the cluster spacing constraint within the target segment, the optimization point selection model is constructed as follows:

[0148]

[0149]

[0150] Solving the above optimization model will yield the fracturing perforation locations that meet the requirements for cluster spacing and cluster number within the fracturing section.

[0151] The optimal model for specific perforation points is based on the fracturing location MD. i The constraints that need to be met can be used to formulate the following perforation location optimization process:

[0152] (a) Prepare basic data: depth measurement (MD) of the selected fracturing section and the corresponding sweet spot comprehensive evaluation index (SSF).

[0153] (b) Sort all candidate aperture locations in descending order of proximity.

[0154] (c) Select the first perforation location from the sorted list of perforation points, i.e. the location with the highest comprehensive compressibility index, and add it to the perforation point selection list.

[0155] (d) Starting from the second perforation point, iterate through the remaining list of perforation points after sorting, find candidate points whose depth difference with the depth of the last point in the perforation point selection list is between a and b, and select the point with the largest comprehensive compressibility index from the candidate points to add to the perforation point selection list.

[0156] (e) Repeat step d until the number of perforation points in the selection list reaches k.

[0157] Based on the above algorithm, the recommended perforation location can be obtained by programming.

[0158] To verify the rationality of the evaluation method, well X1 in block XX of the Sichuan Basin was selected for evaluation and analysis. The horizontal section of this well is about 1500m long and is divided into 24 fracturing sections, with a section length of 55 to 70m and an average section length of 60.91m.

[0159] Figure 2Basic logging information, geological sweet spot parameters, engineering sweet spot parameters, and comprehensive compressibility evaluation results for the horizontal section of well X1 (depths from 3580 to 3780 m) are presented. Within a 200 m interval, the geological conditions from 3644 m to 3746 m are relatively good; however, the horizontal stress differences and fracture toughness are significant in this section, resulting in weak engineering compressibility and minimal difference in gas production. A comparative analysis of the established Geological Engineering Comprehensive Compressibility Evaluation Index (SSF) with the post-compression gas production profile test results shows that the index matches well with the gas production profile, consistent with compressibility evaluation results targeting post-compression production capacity, thus verifying the rationality of the comprehensive compressibility evaluation method presented in this paper.

[0160] Based on the comprehensive compressibility evaluation results, 26 perforation location optimization methods established in this paper were used to recommend perforation points within a 200m long well section. K-means cluster analysis was then applied to all locations within this section for cluster analysis. Figure 3-4 As shown. For Class I geological and engineering sweet spots, due to their good gas content, relatively high porosity, and good engineering compressibility, economical development can be achieved using conventional horizontal well volumetric fracturing technology, and perforation at these locations should be prioritized. However, for locations with clustering results of Class III, the reservoir rocks have high plasticity and low brittleness, making it difficult to create effective artificial flow channels through artificial fracturing, and the reservoir gas content is poor, contributing little to production; perforation at these locations should be avoided as much as possible. Within the entire section, Class I points account for only 31.17%, Class II points account for 42.72%, and Class III points account for 26.11% (…). Figure 3 Within the preferred 26 cluster perforation locations, the proportion of Class I points increased to 53.85%, Class II points to 26.92%, and Class III points decreased to 19.23%. Figure 4 The results show that the established method for selecting perforation point locations can select Class I perforation locations with good geological conditions and engineering compressibility, proving the rationality of the method in this application.

[0161] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimizing perforation location, employing a compressibility evaluation method based on a geological-engineering double sweet spot to determine the compressibility of the fracturing interval, comprising: S1: Select sweet spot evaluation parameters for the target reservoir, including geological sweet spot parameters and engineering sweet spot parameters; S2: Construct an evaluation matrix using sweet spot evaluation parameters from multiple depth measurements of the target reservoir; S3: Perform weighted standardization on the evaluation matrix to obtain the weight values ​​of each dessert evaluation parameter and establish the corresponding comparison matrix; S4: Calculate the compressibility evaluation index using the Euclidean distance method; Calculate the fracturing stage i The distance between the point evaluation index value and the ideal value sequence of the evaluation index: in, SSF This is a compressibility evaluation index, dimensionless; For European-style approximation, dimensionless; The positive Euclidean distance is dimensionless. It is a negative Euclidean distance, dimensionless; Determine the depth measurement range, cluster spacing range, and number of perforation clusters for the selected fracturing section, and determine the perforation location by considering the influence of the fracturing section length, number of perforation clusters, and cluster spacing. Let the depth measurement range of the selected fracturing section be... L 1 to L 2. The cluster spacing range is fixed as follows: a to b The number of perforation clusters is k The corresponding compressibility index is SSF i ; (1) Considering the cluster spacing constraint, In the formula: a , b These represent the left and right endpoints of the cluster spacing range; The cluster spacing is in meters. (2) Considering cluster number constraints: Starting from the initial position of the fracturing section, the maximum number of clusters can be determined by selecting the minimum cluster spacing: Solving for: This determines the range of cluster numbers: depth measurement MD Compressibility Index SSF and cluster number k As the decision variable, the sum of the Euclidean proximity of the selected perforation points. To maximize the cluster spacing constraint within the target segment, an optimal point selection model is constructed: 。 2. The method for selecting the perforation location as described in claim 1, wherein the geological sweet spot parameters include porosity, total organic carbon content, and gas content, and the engineering sweet spot parameters include rock brittleness index, fracture toughness, and reservoir stress.

3. The method for selecting the location of the perforation hole as described in claim 1, wherein step S2 further includes: S21: Select geological sweet spot parameters and engineering sweet spot evaluation parameters n One, get m The geological sweetness parameters and engineering sweetness evaluation parameters for each sounding location are derived from... m Each sounding location corresponds to n The original evaluation matrix was constructed using the parameters of the geological engineering sweet spot. X : in: , ; x ij Representing the j The evaluation parameter is at the _ ... i The specific value of each data point; S22: Standardize the evaluation parameters.

4. The method for optimizing the position of the perforation hole as described in claim 3, wherein the standardization process in step S22 is as follows: The standardization method for positive indicators is as follows: The standardization method for negative indicators is as follows: Obtain the processed standardized matrix R In the formula: r ij The evaluation data is standardized and dimensionless. R This is a standardized matrix.

5. The method for optimizing the location of the perforation hole as described in claim 1, wherein step S3 further includes determining the weights of the dessert evaluation parameters using the analytic hierarchy process (AHP), specifically including: S31: Construct a hierarchical structure for the evaluation matrix; S32: Construct the comparison matrix; S33: Calculate the weights of each dessert evaluation parameter; S34: Perform a consistency check on the constructed comparison matrix. If the consistency check passes, determine the weights directly; if the check fails, readjust the comparison matrix until the check passes.

6. A computer-readable storage medium having instructions stored thereon, which, when executed by a processor, cause the processor to perform the perforation location optimization method according to any one of claims 1-5.

7. A perforation location optimization device, which uses the compressibility evaluation method based on the geological-engineering double sweet spot as described in any one of claims 1-5 to determine the compressibility of the fracturing interval, and further includes a perforation location optimization module. The perforation location optimization module is used to determine the depth range, cluster spacing range, and number of perforation clusters of the selected fracturing section, and to take into account the influence of the fracturing section length, the number of perforation clusters, and the cluster spacing, thereby optimizing the perforation location.

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