Three-dimensional fracturing modeling method for tight gas reservoir

Abstract

The embodiment of the invention provides a tight gas reservoir three-dimensional fracturing modeling method, which relates to the technical field of oil and gas well engineering, and comprises the following steps: calculating a first fracturing index, a second fracturing index and a third fracturing index at each depth point of each gas recovery well according to logging data at each depth point of each gas recovery well of a reservoir, the first fracturing index represents the crack propagation capability of the rock under the action of fracturing fluid, the second fracturing index represents the capability of the rock to generate a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index represents the capability of the rock to crack under the heterogeneity driving of a ground stress field; clustering the first rock mechanical distribution data at each depth point according to the first fracturing index, the second fracturing index and the third fracturing index at each depth point; and according to the clustering result and the second rock mechanical distribution data of the reservoir, constructing a three-dimensional fracturing model of the reservoir.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of oil and gas well engineering technology, specifically to a method for three-dimensional fracturing modeling of tight gas reservoirs. Background Technology

[0002] Tight sandstone reservoirs are characterized by low porosity and low permeability. In order to improve the production of a single well and the effective period of stable production, it is generally necessary to combine directional wells / horizontal wells with multi-stage hydraulic fracturing. Reservoir fracturing evaluation technology is an important guarantee for well location deployment, selection of fracturing intervals and hydraulic fracturing design.

[0003] Current techniques for evaluating the fracturability of tight sandstone reservoirs typically involve constructing empirical formulas for brittleness indices related to mineral content, Young's modulus, and Poisson's ratio, and then calculating the compressibility coefficient at each point in the reservoir. However, existing fracturability evaluation methods do not consider the inherent mechanical or elastic properties of the rock, neglect the physical processes of fracture propagation and the evaluation of fracturing effectiveness, and fail to consider the organic integration of reservoir fracture mechanics parameters, porous media reservoir parameters, and geostress parameters, leading to significant biases in the fracturability evaluation results. Summary of the Invention

[0004] The purpose of the embodiments in this specification is to provide a three-dimensional fracturability modeling method for tight gas reservoirs, so as to overcome the problem that the evaluation results of the fracturability of tight sandstone reservoirs in existing methods have large deviations.

[0005] To solve the above-mentioned technical problems, the specific technical solutions of the embodiments in this specification are as follows:

[0006] On the one hand, the embodiments of this specification provide a method for three-dimensional fracturing modeling of tight gas reservoirs, including:

[0007] Based on the logging data at each depth point of each gas production well in the reservoir, the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well are calculated. The first fracturing index characterizes the crack propagation ability of the rock under the action of fracturing fluid, the second fracturing index characterizes the ability of the rock to form a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index characterizes the ability of the rock to fracture under the drive of heterogeneity of the geostress field.

[0008] Based on the first fracturing index, the second fracturing index, and the third fracturing index at each depth point, the first rock mechanics distribution data at each depth point are clustered.

[0009] Based on the clustering results and the second rock mechanics distribution data of the reservoir, a three-dimensional fracturing model of the reservoir is constructed.

[0010] On another front, embodiments of this specification provide a three-dimensional fracturing capability modeling device for tight gas reservoirs, comprising:

[0011] The calculation module is used to calculate the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well in the reservoir based on the logging data at each depth point. The first fracturing index represents the crack propagation ability of the rock under the action of fracturing fluid, the second fracturing index represents the ability of the rock to form a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index represents the ability of the rock to fracture under the drive of heterogeneity of the geostress field.

[0012] The clustering module is used to cluster the first rock mechanics distribution data at each depth point based on the first fracturing index, the second fracturing index and the third fracturing index at each depth point.

[0013] The module is used to construct a three-dimensional fracturing model of the reservoir based on the clustering results and the second rock mechanics distribution data of the reservoir.

[0014] In another aspect, a computer device is provided, including a memory for storing computer programs and a processor for executing the computer programs to implement the above-mentioned method for three-dimensional fracturing modeling of tight gas reservoirs.

[0015] Furthermore, embodiments of this specification also provide a computer program product, which, when run by the processor of a computer device, executes instructions for any of the methods described above.

[0016] As can be seen from the technical solutions provided in the embodiments of this specification above, these embodiments can calculate the first, second, and third fracturing indices at each depth point of each gas production well in the reservoir based on logging data from each well. The first fracturing index characterizes the crack propagation capability of the rock under the action of fracturing fluid; the second fracturing index characterizes the ability of the rock to form a three-dimensional fracture network under conditions of heterogeneous mineral composition distribution; and the third fracturing index characterizes the ability of the rock to fracture under the drive of heterogeneity of the geostress field. Based on the first, second, and third fracturing indices at each depth point, the first rock mechanical distribution data at each depth point are clustered. Based on the clustering results and the second rock mechanical distribution data of the reservoir, a three-dimensional fracturing model of the reservoir is constructed. By integrating the three major factors of fracturing fluid action, mineral composition heterogeneity, and geostress field heterogeneity, the first, second, and third fracturing indices are established respectively, forming a multi-mechanism synergistic fracturing evaluation system. This fundamentally solves the limitations of single-index evaluation and significantly improves the comprehensiveness and scientific nature of the evaluation. Building upon this foundation, cluster analysis of rock mechanics data using three fracturing indices establishes a nonlinear correlation between a fourth fracturing index and multidimensional rock mechanics parameters, laying the groundwork for subsequent accurate predictions. Furthermore, by combining wellpoint clustering patterns with three-dimensional rock mechanics data volumes, a leap from discrete wellpoint evaluation to continuous three-dimensional spatial prediction is achieved, thereby constructing a three-dimensional fracturing model covering the entire reservoir. Based on the fracturing prediction results across the entire reservoir, sweet spots and difficult zones within the reservoir can be accurately identified, guiding the optimal selection of perforation locations, design of fracturing section lengths, and optimization of construction parameters, significantly improving the targeting and effectiveness of fracturing stimulation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below.

[0018] Figure 1 This is a flowchart of a three-dimensional fracturing modeling method for tight gas reservoirs provided in the embodiments of this specification;

[0019] Figure 2 This is an overall logic flowchart of a three-dimensional fracturability modeling method for tight gas reservoirs provided in the embodiments of this specification;

[0020] Figure 3 This is a schematic diagram of the fracturing capability evaluation results of well LX1 in a specific embodiment provided in this specification.

[0021] Figure 4 This is a partial schematic diagram of the fracturing capability evaluation results of well LX1 in a specific embodiment provided in this specification.

[0022] Figure 5 This is a schematic diagram of the fracturing capability evaluation results of well LX2 in a specific embodiment provided in this specification.

[0023] Figure 6 This is a partial schematic diagram of the fracturing capability evaluation results of well LX2 in a specific embodiment provided in this specification.

[0024] Figure 7 This is a schematic diagram of the fracturing capability evaluation results of well LX3 in a specific embodiment provided in this specification.

[0025] Figure 8 This is a partial schematic diagram of the fracturing capability evaluation results of well LX3 in a specific embodiment provided in this specification;

[0026] Figure 9 This is a schematic diagram of a three-dimensional fracturing model in a specific embodiment provided in this specification;

[0027] Figure 10 This is a schematic diagram of a three-dimensional fracturing model in a specific embodiment provided in this specification;

[0028] Figure 11 This is a schematic diagram of the fracturing capability of a three-dimensional multi-directional profile horizontal well in a specific embodiment provided in this specification.

[0029] Figure 12 This is a schematic diagram of the structural composition of a three-dimensional fracturing modeling device for tight gas reservoirs provided in the embodiments of this specification;

[0030] Figure 13 This is a schematic diagram of the structural composition of the computer device provided in the embodiments of this specification. Detailed Implementation

[0031] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0032] It should be noted that the terms "first," "second," etc., used in this specification, claims, and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0033] In some embodiments, tight gas reservoirs can be sandstone reservoirs containing natural gas but with low rock permeability. The rock permeability of such reservoirs is typically less than 0.1 millidarcy, and in most cases even less than 0.01 millidarcy; in contrast, the permeability of conventional natural gas reservoirs generally ranges from tens to hundreds of millidarcy. This extremely low permeability causes the natural gas to be trapped in micropores and narrow throats, resulting in extremely high flow resistance, preventing it from flowing freely into the wellbore as in conventional reservoirs. Tight gas reservoirs are unconventional natural gas resources, characterized by extremely low natural production capacity. Artificial enhancement techniques, such as horizontal well drilling and large-scale hydraulic fracturing, must be employed to create a network of highly conductive fractures within the reservoir, transforming the originally tight rock mass into artificially created high-permeability channels with industrial exploitability.

[0034] Figure 1 This is a flowchart illustrating a three-dimensional fracturing capability modeling method for tight gas reservoirs provided in the embodiments of this specification. Figure 2 This is a flowchart illustrating the overall logic of a three-dimensional fracturing capability modeling method for tight gas reservoirs provided in the embodiments of this specification. In specific implementation, it may include the following steps:

[0035] S10: Based on the logging data at each depth point of each gas production well in the reservoir, calculate the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well. The first fracturing index characterizes the crack propagation ability of the rock under the action of fracturing fluid, the second fracturing index characterizes the ability of the rock to form a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index characterizes the ability of the rock to fracture under the drive of heterogeneity of the geostress field.

[0036] In some embodiments, based on logging data at each depth point of each gas production well in the reservoir, a first fracturing index, a second fracturing index, and a third fracturing index are calculated at each depth point of each gas production well. The first fracturing index characterizes the crack propagation capability of the rock under the action of fracturing fluid, the second fracturing index characterizes the ability of the rock to form a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index characterizes the ability of the rock to fracture under the drive of heterogeneity of the geostress field.

[0037] Based on the logging data obtained from each gas production well in the reservoir at different depths, the first fracturing index, the second fracturing index, and the third fracturing index at the corresponding depths can be calculated respectively.

[0038] The first fracturing index can comprehensively characterize the rock's fracturing sensitivity and fracture area extension efficiency under hydraulic fracturing by coupling rock elastic modulus, dynamic Poisson's ratio, fracture toughness and fracturing fluid rheological parameters.

[0039] The second compressibility index can establish a quantitative relationship between mineral spatial configuration and fracture complexity based on mineral composition matrix, brittleness index distribution spectrum and mineral heterogeneity coefficient, and thus evaluate the ability of rocks to form a three-dimensional multi-branched fracture network under heterogeneous mineral distribution conditions.

[0040] The third fracturing index can be used to construct a response model of geostress heterogeneity and rock fracturing tendency by integrating geostress anisotropy coefficient, stress difference ratio and rock fracturing energy criterion, reflecting the ability of geostress heterogeneity to control the spatial behavior of rock fracturing.

[0041] Specifically, the first fracturing index is calculated using a multi-parameter coupled fracture dynamics model, comprehensively incorporating the dual effects of rock mechanical response and fracturing fluid effectiveness. The second fracturing index, based on digital characterization of mineral composition and spatial variability analysis, can achieve a quantitative mapping between mineral heterogeneity and fracture network complexity. The third fracturing index, based on a fusion algorithm of geostress field reconstruction and rock fracture criteria, establishes a functional relationship between stress heterogeneity and fracture probability.

[0042] By constructing three indices reflecting fracturing fluid-rock interaction, mineral heterogeneity-controlled fracture mechanisms, and geostress-controlled fracture effects, this study overcomes the limitations of traditional single brittleness index evaluation and establishes a multi-mechanism coupled fracturability evaluation system. This multi-dimensional analysis system can more comprehensively reveal the intrinsic controlling factors of rock fracturability, significantly improving the scientific rigor and reliability of the evaluation results. Specifically, the first index incorporates the effect of fracturing fluid, organically combining the comprehensive influence of engineering operations and geological background, providing direct basis for optimizing the fracturing fluid system and construction parameters. The second index, by quantifying mineral heterogeneity, provides theoretical support for predicting fracture network complexity and optimizing segment cluster locations. The third index integrates geostress heterogeneity, enabling accurate identification of geostress-favorable zones.

[0043] By combining these three indices, the effectiveness of fracturing can be predicted more accurately. The first index predicts fracture propagation efficiency, the second assesses the potential for complex fracture network formation, and the third determines the ease of fracturing. The combination of these three indices enables prediction of the entire process from fracture initiation to propagation and network formation, providing a reliable basis for optimizing fracturing strategies and predicting production output.

[0044] S20: Cluster the first rock mechanics distribution data at each depth point based on the first fracturing index, the second fracturing index, and the third fracturing index.

[0045] In some embodiments, the weighting coefficients of the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well are calculated respectively; the weighting coefficients characterize the dispersion of the fracturing index of all depth points in the layer to which each depth point belongs; based on the first fracturing index, the second fracturing index, the third fracturing index and the corresponding weighting coefficients at each depth point, the fourth fracturing index at each depth point is calculated; based on the fourth fracturing index at each depth point, the first rock mechanics distribution data at each depth point are clustered.

[0046] The dynamic weighting coefficients corresponding to the first, second, and third fracturing indices at each depth point within the target interval of each gas well in the reservoir can be calculated separately. Specifically, the weighting coefficients can be determined based on the dispersion or coefficient of variation of each index at all depth points in the target interval, which can quantitatively characterize the heterogeneity and data dispersion characteristics of each index within the interval.

[0047] Based on the specific values ​​of the three fracturing indices at each depth point and their corresponding dynamic weighting coefficients, a fourth fracturing index at that depth point is calculated using a weighted fusion algorithm. The fourth fracturing index comprehensively reflects the overall fracturing characteristics of the first three indices after considering their spatial variability.

[0048] The first rock mechanics distribution data can be used to characterize the distribution of rock mechanical properties at different depths of multiple gas wells within a reservoir. It can cover three types of rock mechanical parameters at various depths of multiple gas wells: first, stress data, including core information related to the stress state of the rock, such as the magnitude and direction of geostress, which directly affects the stress response of the rock during gas well production; second, elastic modulus data, which reflects the rock's ability to resist deformation during the elastic deformation stage and is an important indicator for judging whether the rock mechanical properties are suitable for fracturing operations; and third, rock strength data, including the rock's compressive strength and tensile strength, which determines the rock's ultimate ability to resist damage during production. These three types of data together constitute the first rock mechanics distribution data, providing a precise basis for rock mechanics analysis of the gas well area.

[0049] Based on the fourth fracturing index at various depths of multiple gas production wells, all depths can be divided into several fracturing levels. Depths within the same level exhibit similar characteristics in their primary rock mechanics distribution data. By performing cluster analysis on these primary rock mechanics distribution data within each level, a cluster center can be obtained. This cluster center represents the most typical rock mechanics distribution characteristics of that level and can be considered a benchmark model characterizing its fracturing level. The above clustering process can be applied independently to each fracturing level, thereby establishing a unique representative cluster center for each level.

[0050] Based on the cluster centers corresponding to each fracturing level, further clustering results can be obtained. These results can include a mapping relationship between the fourth fracturing index and the cluster centers. In other words, through the above clustering analysis, a knowledge system centered on the mapping relationship between the fourth fracturing index and the cluster centers can be constructed. This system not only quantifies the intrinsic connection between the fourth fracturing index and the rock mechanics and combustion characteristics, but more importantly, it forms a decision-making tool: by querying the fourth fracturing index at any depth point, its most likely fracturing level and representative rock mechanics state can be quickly located, thereby achieving accurate assessment and scientific decision-making regarding reservoir fracturing capability.

[0051] By employing a dynamic weighting method based on dispersion or coefficient of variation, the subjectivity and experience-dependent nature of traditional manual weighting are overcome. The weighting coefficients automatically respond to the actual distribution characteristics of each index within the stratigraphic interval, assigning higher weights to indices with higher dispersion, ensuring that the comprehensive evaluation results more objectively reflect the reservoir's heterogeneous characteristics. Furthermore, a weighted fusion algorithm is used to integrate the three fracturing indices, preserving the original characteristics of each index while highlighting key factors contributing more to the interval's heterogeneity through weighting coefficients, making the generated fourth fracturing index more geologically representative and providing engineering guidance. In addition, unsupervised machine learning algorithms are used to cluster the rock mechanics data guided by the fourth index, automatically identifying geological units with similar fracturing characteristics, overcoming the subjective limitations of traditional manual stratification, and providing a more scientific basis for fracturing interval division and process parameter optimization.

[0052] S30: Based on the clustering results and the second rock mechanics distribution data of the reservoir, construct a three-dimensional fracturing model of the reservoir.

[0053] In some embodiments, a fracturing index calculation model is constructed based on the clustering results; the fracturing index calculation model is coupled with the mapping relationship between the fourth fracturing index of the rock and the rock mechanical distribution; a three-dimensional fracturing model of the reservoir is constructed based on the second rock mechanical distribution data of the reservoir and the fracturing index calculation model.

[0054] Compared to the first rock mechanics distribution data, the second rock mechanics distribution data has a wider coverage, characterizing the rock mechanics distribution features of all physical points within the reservoir, especially covering rock mechanics data for multiple physical points in blank areas where no gas wells exist. For each physical point within the reservoir, the corresponding second rock mechanics distribution data can also include three types of rock mechanics parameters: stress data, elastic modulus data, and rock strength data for that physical point.

[0055] Both the first rock mechanics distribution data, used to characterize gas well depths, and the second rock mechanics distribution data, covering all physical points in the reservoir, share a key advantage: they can be obtained without well logging data. This characteristic breaks the traditional reliance on well logging data in reservoir analysis, especially when well logging operations are impossible in certain areas of the reservoir due to technical conditions, geological environment, or other factors, resulting in a lack of well logging data. Based on this characteristic, in reservoir fracturing assessment practice, a specific mapping relationship can be constructed to accurately determine the fracturing capability of areas without well logging data. Specifically, for areas in the reservoir where well logging data exists, the acquired first rock mechanics distribution data for these areas is correlated with the fourth fracturing index calculated from well logging data, establishing a stable and reliable mapping relationship between the two. Since the first and second rock mechanics distribution data are consistent in data type and characterization logic, and neither requires well logging data, the established mapping relationship can be applied to areas in the reservoir where no well logging data exists. By utilizing the second rock mechanics distribution data already obtained in this area and combining it with the established mapping relationship, the fourth fracturing index corresponding to this area is derived in reverse. This allows for a precise assessment of the fracturing capability of reservoir areas without logging data, providing a scientific and comprehensive basis for reservoir development planning and fracturing operation optimization.

[0056] In some embodiments, a fracturing index calculation model is constructed, which couples a mapping relationship between a fourth fracturing index and a first rock mechanics distribution data cluster center; second rock mechanics distribution data for each physical point in the reservoir is obtained; based on the fracturing index calculation model, the distance between the second rock mechanics distribution data and each cluster center is calculated; the target cluster center with the smallest distance from the second rock mechanics distribution data is identified; the fracturing level bound to the target cluster center is assigned as the fifth fracturing index of the physical point; and a three-dimensional fracturing model of the reservoir is constructed based on the fifth fracturing indices of multiple physical points in the reservoir.

[0057] Specifically, based on the "fourth fracturability index - cluster center" mapping relationship established by the above cluster analysis, a fracturability index calculation model can be constructed. The operating mechanism of this fracturability index calculation model is as follows: For any given physical point's second rock mechanics distribution data, the fracturability index calculation model calculates its Euclidean distance (or other defined metric) to each cluster center in the feature space and executes a nearest neighbor classification strategy. By identifying the cluster center with the smallest distance, the fracturability level bound to that cluster center is assigned as the fifth fracturability index for that physical point. The fifth fracturability index comprehensively reflects the synergistic control effect of the physical point's geomechanical properties, rock heterogeneity, and geostress field characteristics on its fracturability. By repeating this process for all physical points in the reservoir, the obtained fifth fracturability index set is spatially interpolated and integrated to ultimately generate a comprehensive, numerical three-dimensional fracturability model, providing direct data support for fracturing design.

[0058] By combining the mapping relationship obtained from wellpoint clustering analysis with the three-dimensional rock mechanics data volume obtained from seismic inversion, the effective fusion of fine data at the well logging scale and spatial data at the seismic scale is achieved. This overcomes the limitation of traditional methods that can only provide one-dimensional evaluation of the wellbore and enables the prediction of fracturability in the entire reservoir in three-dimensional space. The machine learning mapping relationship established based on the clustering results fully considers the nonlinear correlation between the fourth fracturability index and multi-dimensional rock mechanics parameters, ensuring the geological rationality of the three-dimensional prediction model and making the prediction results more consistent with subsurface geological laws. The three-dimensional data volume of the fifth fracturability index comprehensively reflects the multiple controlling effects of geomechanical properties, rock heterogeneity, and geostress field characteristics, providing more comprehensive and accurate fracturability evaluation results and a reliable data foundation for fracturing optimization design. The generated three-dimensional fracturability data volume can be directly used for horizontal well trajectory optimization, fracturing segment cluster selection, and construction parameter design, realizing fracturing optimization decision-making based on the three-dimensional digital model of the entire reservoir and improving the pertinence and effectiveness of fracturing design.

[0059] In some embodiments, calculating the first fracturing index at each depth point in step S10 may specifically include: acquiring fracturing fluid viscosity data and fracturing fluid injection rate data at each depth point; determining the elastic modulus data, Poisson's ratio data, and stress intensity data of the rock at each depth point based on the logging data at each depth point; calculating the first fracturing index at each depth point based on the elastic modulus data, Poisson's ratio data, stress intensity data, fracturing fluid viscosity data, and fracturing fluid injection rate data at each depth point; the first fracturing index is negatively correlated with stress intensity, fracturing fluid viscosity, and fracturing fluid injection rate.

[0060] It can collect the fracturing fluid performance parameters at various depths within the target layer, including the apparent viscosity, rheological properties, and injection rate data in the construction design.

[0061] Based on conventional and special logging curve data at this depth point, such as P-wave, S-wave transit time and bulk density, the dynamic elastic modulus, static Poisson's ratio, and stress intensity factor based on the fracture toughness model of the rock are accurately calculated using rock physics equations and inversion algorithms.

[0062] The aforementioned elastic modulus, Poisson's ratio, stress intensity factor, fracturing fluid viscosity, and injection rate data can be input into a fracturability calculation model constructed based on linear elastic fracture mechanics and energy balance principles. The first fracturability index at that depth point can then be calculated using a multi-parameter coupling algorithm.

[0063] The first fracturing index shows a significant negative correlation with the rock stress intensity factor, fracturing fluid viscosity, and injection rate. Specifically, when the formation stress intensity factor is high, the rock's resistance to fracture propagation is enhanced; when the fracturing fluid viscosity is too high, the flow resistance of the fluid in the fracture increases, affecting the fracture creation efficiency; when the injection rate is too high, although it can increase the pressure inside the fracture, it may also lead to a surge in near-wellbore friction or a more complex fracture morphology. These factors combined result in a decrease in the first fracturing index value, significantly increasing the difficulty for the rock at this point to form effective fractures with ideal length, width, and height under hydraulic fracturing.

[0064] By simultaneously integrating geomechanical parameters such as rock elastic modulus, Poisson's ratio, and stress intensity factor with engineering parameters such as fracturing fluid viscosity and injection rate, this method overcomes the limitations of traditional evaluation methods that only consider geological factors. It establishes a comprehensive evaluation system that leverages the synergistic effect of geological characteristics and engineering parameters, significantly improving the engineering applicability of fracturability prediction. Furthermore, a computational model is constructed based on linear elastic fracture mechanics and energy balance principles, fully considering the energy dissipation mechanism and mechanical equilibrium conditions of fracture propagation. This gives the evaluation results clear physical meaning and theoretical basis, avoiding the applicability limitations of empirical formulas. In addition, by revealing the negative correlation between the fracturability index and stress intensity, fracturing fluid viscosity, and injection rate, the influence mechanism of each parameter on fracturing effectiveness is clarified. This allows the model to accurately reflect the impact of changes in construction parameters on fracturing effectiveness, providing quantitative guidance for parameter optimization. The quantitative fracturability index output by the model can be directly used to identify the optimal fracturing interval, guiding the selection of fracturing fluid systems, optimization of injection parameters, and design of construction schemes. This achieves an effective transformation from geological evaluation to engineering design, improving the targeting and effectiveness of fracturing stimulation.

[0065] In some embodiments, the calculation of the first fracturing index at each depth point may specifically include:

[0066] Based on the elastic modulus, Poisson's ratio, stress intensity, fracturing fluid viscosity, and fracturing fluid injection rate data at each depth point, the first fracturing index at each depth point is calculated using the following formula:

[0067] ;

[0068] In the formula, is the first compressibility index at each depth point, dimensionless; Let be the Poisson's ratio of the rock at each depth point, which is dimensionless; Let be the stress intensity factor of the rock at each depth point, in Pa·m. 0.5 G is the shear modulus of the rock at each depth point, Pa; E is the elastic modulus of the rock at each depth point, Pa. ρ is the fracturing fluid viscosity at each depth point, Pa·s; Q is the fracturing fluid injection rate at each depth point, m. 3 / s.

[0069] By using rock mechanical parameters (E, , ) and fracturing construction parameters ( Q) is incorporated into a unified calculation framework, breaking through the limitations of the traditional brittleness index which only considers static geological attributes. A dynamic evaluation model of the synergistic effect of geological characteristics and engineering parameters is established, which significantly improves the engineering applicability of the evaluation results.

[0070] The two terms in the denominator of the above formula correspond to the energy required to overcome the fracture toughness of the rock. ) and the energy dissipated to overcome the viscosity of the fracturing fluid ( This accurately reflects the energy distribution mechanism between rock fracture and fluid flow during the fracturing process, providing a theoretical basis for optimizing energy utilization efficiency.

[0071] The above formulas clearly reveal the influence of various rock mechanics parameters and fracturing operation parameters on fracturability. Specifically, Positively correlated with E, and , Q is negatively correlated with fracturing fluid. This quantitative relationship provides direct guidance for fracturing design. For example, fracturing capability can be improved by reducing the viscosity of the fracturing fluid or optimizing the injection rate, providing a scientific basis for optimizing engineering parameters.

[0072] The first fracturing index can not only evaluate the inherent fracturing capability of rock, but also predict the crack propagation effect under specific construction parameters. By adjusting... Parameters such as Q can be calculated directly. By varying the values, we can simulate and optimize fracturing schemes and predict their effects, supporting the transition from static evaluation to dynamic prediction.

[0073] Compared to traditional brittleness indices, which can only be used for preliminary screening, the first fracturing index can directly guide fracturing design and construction optimization, helping to improve fracturing success rate and stimulation effect, reduce construction costs, and provide technical support for the economical and efficient development of unconventional reservoirs.

[0074] In some embodiments, calculating the second fracturing index at each depth point in step S10 above may specifically include: determining the brittle mineral coefficient and mineral heterogeneity coefficient of the rock at each depth point of each gas production well in the reservoir based on the mineral composition data and fracturing effect data of each gas production well in the reservoir; determining the total brittle mineral content data, total mineral content data, and individual mineral content data of the rock at each depth point based on the logging data at each depth point; calculating the second fracturing index at each depth point based on the brittle mineral coefficient, mineral heterogeneity coefficient, total brittle mineral content data, total mineral content data, and individual mineral content data of the rock at each depth point; the second fracturing index is positively correlated with mineral heterogeneity.

[0075] It can collect mineral composition analysis data and corresponding post-fracture evaluation data for each gas production well in the reservoir. Post-fracture evaluation data, also known as fracturing effect data, may include, but is not limited to, fracture monitoring results, production dynamics, and fracturing operation curve characteristics.

[0076] A machine learning framework based on Bayesian optimization or genetic algorithms can be used to establish a nonlinear mapping relationship between fracturing effects and mineral characteristic parameters. This allows for dynamic inversion and determination of the appropriate brittle mineral weight coefficient (m) and mineral heterogeneity weight coefficient (n) for each well at different depths. These coefficients can be continuously iteratively updated as new well fracturing data are added, forming an adaptive learning mechanism.

[0077] Furthermore, based on conventional and specialized logging data, such as elemental capture spectrum logging and lithology scanning logging, the total brittle mineral content, total mineral content, and content of each individual mineral component at each depth point are calculated using lithology spectral analysis models and mineral inversion algorithms. Finally, the optimized dynamic coefficients m and n, along with the total brittle mineral content data, total mineral content data, and individual mineral component data sets, are used to calculate the second fracturing index.

[0078] Mineral heterogeneity manifests as the spatial inhomogeneity of brittle minerals (such as quartz and feldspar) and ductile minerals (such as clay). As heterogeneity intensifies, the mechanical properties of rocks exhibit significant anisotropy. For example, when a crack tip extends from a high Young's modulus, highly brittle mineral to a low Young's modulus, highly ductile mineral, a significant abrupt change in mechanical properties occurs at the interface. Cracks tend to propagate along the path of least resistance, leading to directional deflection or bifurcation, forming secondary cracks. This continuous deflection and bifurcation contributes to the formation of complex three-dimensional fracture networks. Conversely, a highly homogeneous rock (such as pure quartz sandstone) is more prone to forming a single, straight primary crack, with limited alteration volume.

[0079] By employing machine learning frameworks such as Bayesian optimization or genetic algorithms, a nonlinear mapping relationship between fracturing effects and mineral characteristics can be established, overcoming the subjectivity and experience-dependent nature of traditional manually set coefficients. The coefficients can be continuously updated iteratively with new data, forming a self-optimizing learning mechanism that enables the model to adapt to different geological blocks, significantly improving the scientific rigor and reliability of coefficient values. Furthermore, by utilizing special logging data such as elemental capture spectrum logging and lithology scanning logging, combined with lithology spectral analysis and mineral inversion algorithms, accurate calculations of the total brittle mineral content and the content of each individual mineral component can be achieved. This overcomes the limitations of insufficient mineral identification accuracy in conventional logging, providing high-precision input data for fracturability evaluation. In addition, through the fusion calculation of dynamically optimized coefficients and high-precision mineral parameters, the second fracturability index reflects both the macroscopic characteristics of mineral composition and the heterogeneous details of mineral distribution. The index calculation results show a higher degree of matching with actual geological conditions and stronger adaptability to different types of reservoirs.

[0080] In some embodiments, the calculation of the second fracturing index at each depth point may specifically include:

[0081] Based on the brittle mineral coefficient, mineral heterogeneity coefficient, total brittle mineral content, total mineral content, and individual mineral content data of the rock at each depth point, the second fracturing index at each depth point is calculated using the following formula:

[0082] ;

[0083] In the formula, is the second compressibility index at each depth point, dimensionless; m is the brittle mineral coefficient of the rock at each depth point, dimensionless; n is the mineral heterogeneity coefficient of the rock at each depth point, dimensionless. The total brittle mineral content of the rock at each depth point, % denoted as the total mineral content of the rock at each depth point, %; N represents the total mineral types in the rock at each depth point, dimensionless. Let be the content of the i-th mineral in the rock at each depth point.

[0084] By the absolute content of brittle minerals ( ) and the heterogeneity of mineral distribution ( This approach combines the traditional method with the weighted fusion of m and n coefficients to simultaneously evaluate both "mineral content" and "mineral distribution," thus providing a more comprehensive characterization of the control effect of mineral composition on fracture network complexity.

[0085] in the formula This method quantifies the degree of deviation of mineral composition from a completely homogeneous state, incorporating the uniformity of mineral distribution into the fracturability evaluation system. This helps explain why some high-brittle mineral content zones have poor fracturing performance (uneven mineral distribution), while some medium-content zones can form complex fracture networks (uniform mineral distribution).

[0086] Furthermore, by flexibly adjusting the coefficients m and n, the above formula can be adapted to the geological characteristics of different blocks and strata. The coefficient values ​​can be dynamically optimized based on feedback from actual fracturing effects. For example, the value of n can be increased for highly heterogeneous reservoirs to emphasize the influence of mineral distribution; while the value of m can be increased for homogeneous reservoirs to highlight the role of brittle mineral content.

[0087] Compared to a single brittleness index, the above formula can more accurately predict the complexity of the fracture network. When the mineral distribution is uniform, the fracture propagation is less affected by the interface effect, and it is easy to form a main fracture accompanied by complex branches; when the mineral distribution is uneven, the fracture is more likely to deflect or terminate at the soft-hard interface, reducing the complexity of the fracture network.

[0088] Evaluation results based on the second fracturing index can provide detailed guidance for fracturing design. For example, high... In high-value areas, i.e., regions with high and uniform content, large-scale fracturing can be used to form complex fracture networks; low-value areas... In areas with high potential for growth, techniques such as temporary plugging and diversion can be used to overcome the effects of heterogeneity and improve reservoir stimulation.

[0089] In some embodiments, determining the brittle mineral coefficient and mineral heterogeneity coefficient of the rock at each depth point of each gas production well in the reservoir based on the mineral composition data and fracturing effect data of each gas production well in the reservoir includes: acquiring the mineral composition data and corresponding fracturing effect data at each depth point of each gas production well in the reservoir; calculating the brittle mineral combination factor and mineral spatial configuration factor at each depth point based on the mineral composition data at each depth point of each gas production well; the brittle mineral combination factor characterizes the ability of brittle minerals in the rock to promote the formation of a three-dimensional fracture network; the brittle mineral combination factor is positively correlated with the brittle mineral coefficient; The mineral spatial configuration factor characterizes the ability of mineral distribution in the rock to promote the formation of a three-dimensional fracture network; the mineral spatial configuration factor is positively correlated with the mineral heterogeneity coefficient; based on the brittle mineral assemblage factor and mineral spatial configuration factor at each depth point, the brittle mineral coefficient and mineral heterogeneity coefficient of the rock at each depth point are determined respectively, and the second compressibility index at each depth point is calculated; taking the similarity between the compressibility effect data and the second compressibility index data at multiple depth points in each layer as the optimization objective, the brittle mineral coefficient and mineral heterogeneity coefficient at multiple depth points in each layer are obtained by inversion calculation using a multi-objective optimization algorithm.

[0090] Mineral composition data for each segment of each gas production well in the reservoir can be obtained, including but not limited to elemental capture spectrum logging data, core analysis data, and X-ray diffraction data. Simultaneously, quantitative data on fracturing effects corresponding to the aforementioned well depths are collected, such as the fracture network complexity index obtained based on microseismic monitoring interpretation, characteristic parameters of the fracturing operation curve, and normalized gas production data. Based on the mineral composition data at different depths of each gas production well, a weighted efficiency model is used to calculate the brittle mineral assemblage factor at each depth. This factor comprehensively characterizes the potential of brittle mineral assemblages in the rock to promote the formation of complex three-dimensional fracture networks by considering the differences in the promoting effect of different brittle minerals on fracture propagation. The brittle mineral assemblage factor is positively correlated with the brittle mineral coefficient; that is, the higher the factor value, the larger the brittle mineral coefficient value. Meanwhile, based on electrical imaging logging or core scanning images, mineral spatial configuration factors at each depth point are calculated using geostatistical methods and image texture analysis. These factors quantify the disorder and structural complexity of mineral phase spatial distribution, characterizing the ability of mineral spatial distribution patterns to promote the formation of three-dimensional fracture networks. The mineral spatial configuration factor is positively correlated with the mineral heterogeneity coefficient, meaning that the higher the factor value, the larger the mineral heterogeneity coefficient.

[0091] Based on the brittle mineral assemblage factor and mineral spatial configuration factor calculated at each depth point, a pre-defined dynamic weight allocation model is used to determine the brittle mineral coefficient and mineral heterogeneity coefficient of the rock at that depth point. These are then substituted into the calculation formula for the second fracturing index to obtain a preliminary second fracturing index. With the optimization objective of maximizing the overall similarity between the measured fracturing effect data and the calculated second fracturing index data at multiple depth points within the layer, a multi-objective optimization algorithm is used for inversion calculation, simultaneously optimizing to obtain the optimal combination of brittle mineral coefficient and mineral heterogeneity coefficient at multiple depth points within the layer.

[0092] In some embodiments, the above-mentioned calculation of the brittle mineral combination factor at each depth point based on the mineral composition data at each depth point of each gas production well includes: determining the efficiency weight coefficient of each mineral; the efficiency weight coefficient characterizing the ability of the mineral to promote the formation of a three-dimensional fracture network; determining the efficiency weighted component data of all brittle minerals and the efficiency weighted component data of all minerals at each depth point of the rock based on the mineral composition data at each depth point of each gas production well and the efficiency weight coefficient of each mineral; and determining the brittle mineral combination factor at each depth point based on the comparison result of the efficiency weighted component data of all brittle minerals and the efficiency weighted component data of all minerals at each depth point.

[0093] The effectiveness weighting coefficients of each mineral can be determined. These coefficients are dimensionless parameters pre-defined based on the rock mechanical properties of the minerals, used to quantitatively characterize the ability of a mineral to promote the formation of a complex three-dimensional fracture network during fracturing. Highly effective brittle minerals are assigned higher weights, while ductile minerals are assigned lower weights. Based on the mineral composition data and effectiveness weighting coefficients at each depth point of each gas production well, the effectiveness weighted sum of all brittle minerals at each depth point (i.e., the sum of the products of each brittle mineral content and its weighting coefficient) and the effectiveness weighted sum of all minerals (including brittle and ductile minerals) are calculated. The effectiveness weighted sum of brittle minerals at each depth point is compared with the overall effectiveness weighted sum of all minerals, and this ratio is used as the brittle mineral combination factor at that depth point. This factor comprehensively reflects the promoting effect of mineral composition on fracture propagation.

[0094] By introducing a mineral efficacy weighting coefficient based on rock mechanical properties, the limitations of traditional mineral content calculations, which rely solely on content, are overcome. This approach accurately distinguishes the actual differences in the contributions of different minerals to fracture propagation. The weighted calculation method ensures that the brittle mineral assemblage factor not only reflects the quantity of minerals but also their quality, providing a more scientific compositional basis for fracturability evaluation.

[0095] In some embodiments, the above-mentioned calculation of the mineral spatial configuration factor at each depth point based on the mineral composition data at each depth point of each gas production well includes: acquiring a mineral spatial distribution image at each depth point; performing mineral phase segmentation processing on the image to identify and distinguish regions of different mineral phases; performing connected component analysis on the segmented image to identify and statistically analyze the area of ​​independent continuous regions of each mineral phase; calculating the ratio of the standard deviation to the mean of the area of ​​all mineral phase regions to obtain the structural dispersion; calculating the variance of the variogram based on the mineral spatial distribution image; and calculating the mineral spatial configuration factor at each depth point based on the structural dispersion and the variance of the variogram.

[0096] This system can acquire high-resolution mineral spatial distribution image data at various depth points, and preprocess the image data and perform mineral facies segmentation to accurately identify and distinguish different mineral facies regions in the image. Connected component analysis is performed on the segmented mineral facies images to identify each independent mineral facies patch and calculate its pixel area. Based on the area data of all mineral facies patches, the ratio of their standard deviation to mean is calculated to obtain the structural dispersion, which characterizes the uniformity of patch size. Simultaneously, based on the mineral spatial distribution image, the variogram values ​​at different depth lengths are calculated using geostatistical methods, and their variances are obtained to obtain the variogram variance, which characterizes the intensity of spatial correlation changes. The structural dispersion and variogram variance are normalized, and a mineral spatial allocation factor at each depth point is calculated comprehensively. This factor is used to quantify the heterogeneity and complexity of mineral spatial distribution. For example, the mineral spatial allocation factor can be expressed by the formula SCF = 1 - [(D_norm / VV_norm)]. 1 / 2 The calculation is performed, where D_norm / VV_norm represent the structural dispersion and variance of the variogram, respectively. The closer the SCF is to 1, the better the mineral spatial configuration, meaning less contact with ductile minerals, a homogeneous structure, and greater facilitating crack propagation; the closer it is to 0, the worse the mineral spatial configuration, meaning more contact with ductile minerals, a heterogeneous structure, and less facilitating crack formation.

[0097] This study employs a method combining image processing and geostatistics to comprehensively quantify the spatial distribution characteristics of minerals through two dimensions: structural dispersion and variance of the variogram. Structural dispersion characterizes the uniformity of mineral patch size, while variance characterizes the stability of spatial correlation. This multi-dimensional analysis method enables a quantitative description of the complexity of mineral spatial configuration.

[0098] In some embodiments, calculating the third fracturing index at each depth point in step S10 above includes: determining the minimum horizontal principal stress data of the rock at each depth point based on the logging data at each depth point; determining the maximum minimum horizontal principal stress data and the minimum horizontal principal stress data of the rock in the layer to which each depth point belongs based on the logging data at each depth point; and calculating the third fracturing index at each depth point based on the minimum horizontal principal stress data of the rock at each depth point and the maximum minimum horizontal principal stress data and the minimum horizontal principal stress data of the rock in the layer to which each depth point belongs.

[0099] By comprehensively utilizing well logging data such as sonic transit time, bulk density, neutron porosity, and resistivity at various depth points, combined with rock mechanics models based on elastic constitutive relations (such as transverse isotropic models) and geostress interpretation methods, the minimum horizontal principal stress value of the rock at each depth point can be accurately calculated through multi-parameter inversion algorithms, thus establishing a continuous geostress profile.

[0100] Furthermore, by statistically analyzing the minimum horizontal principal stress data at all depth points within the target layer, the maximum and minimum values ​​of the minimum horizontal principal stress in the layer are determined, thereby quantifying the stress distribution range and heterogeneous characteristics within the layer.

[0101] Based on the minimum horizontal principal stress at each depth point and the extreme value of the minimum horizontal principal stress of the corresponding layer, the third fracturing index at each depth point can be obtained by calculating the relative stress difference.

[0102] By comprehensively utilizing multiple logging parameters such as acoustic waves, density, neutrons, and resistivity, combined with a transverse isotropic model and porosity elasticity theory, the minimum horizontal principal stress can be accurately calculated using a multi-parameter inversion algorithm. This overcomes the limitations of traditional single-model estimation, significantly improves the accuracy and reliability of geostress profile calculation, and provides high-precision input data for fracturability evaluation.

[0103] By statistically analyzing the extreme values ​​of the minimum horizontal principal stress within a fracturing segment, the range of stress distribution and degree of heterogeneity can be quantified, effectively identifying stress barrier layers and vulnerable layers, clarifying the controlling effect of stress differences within the segment on crack propagation, and providing a basis for the optimal selection of fracturing segments.

[0104] In some embodiments, the calculation of the third fracturing index at each depth point may specifically include: calculating the third fracturing index at each depth point using the following formula based on the minimum horizontal principal stress data of the rock at each depth point and the maximum and minimum horizontal principal stress data of the rock in the stratum to which each depth point belongs:

[0105] ;

[0106] In the formula, is the third fracturing index at each depth point, dimensionless; The maximum and minimum horizontal principal stress in the rock within the layer at each depth point, in Pa; The minimum horizontal principal stress, in Pa, is the minimum stress of the rock in the layer at each depth point. Minimum horizontal principal stress of rock at each depth point, in Pa.

[0107] Traditional methods rely on absolute stress values, which lack lateral and longitudinal comparability. This is addressed by normalizing the relative stress coefficient F. σ Converting absolute stress values ​​into dimensionless indices eliminates the differences in stress background between different layers or blocks, making the evaluation results comparable across layers and regions, and greatly improving the engineering applicability of the evaluation results.

[0108] The above formula quantifies the stress distribution range within a segment by normalizing the range and characterizes the stress dominance at a specific point by the relative difference. This allows for a quantitative evaluation of the impact of stress heterogeneity on the ease of fracturing, providing a basis for optimizing perforation location and fracturing design.

[0109] The closer the value is to 1, the closer the stress at that point is to the lowest level within the segment, indicating the lowest relative fracturing difficulty; the closer it is to 0, the closer the stress at that point is to the highest level within the segment, indicating the highest relative fracturing difficulty. This grading evaluation method can accurately identify easily fracturable and difficult-to-fracturable zones within a segment, guiding the optimal selection of targets.

[0110] based on The spatial distribution of the index can optimize the location of perforation clusters (preferred selection). High value area), number of clusters within the adjustment segment (high) Differential segment reduces cluster number) and customized construction parameters (low) (Increase pumping energy in the area) to achieve personalized fracturing design and improve reservoir stimulation efficiency.

[0111] In some embodiments, the calculation of the third fracturing index at each depth point may specifically include: calculating the third fracturing index at each depth point using the following formula based on the minimum horizontal principal stress data of the rock at each depth point and the maximum and minimum horizontal principal stress data of the rock in the stratum to which each depth point belongs:

[0112] ;

[0113] In the formula, is the third fracturing index at each depth point, dimensionless; The maximum and minimum horizontal principal stress in the rock within the layer at each depth point, in Pa; The minimum horizontal principal stress, in Pa, is the minimum stress of the rock in the layer at each depth point. Minimum horizontal principal stress of rock at each depth point, in Pa; The mean of the minimum horizontal principal stresses corresponding to all depth points within a preset-sized window centered at each depth point; This represents the stress contrast amplification factor of the rock at each depth point; This is the preset base magnification factor; This represents the overall coefficient of variation for all depth points within the layer segment to which each depth point belongs; The coefficient of variation is the local variation of all depth points within a preset-sized window centered on each depth point.

[0114] For the basic magnification factor This can be set based on the regional geomechanical characteristics and engineering experience. For example, for shale reservoirs known to have well-developed fractures and prone to forming complex fracture networks, a specific setting can be established. =1.5; For relatively homogeneous and brittle tight sandstone reservoirs, a value of 1.5 can be set. =0.8.

[0115] For the overall coefficient of variation Specifically, it can be calculated by the ratio of the standard deviation to the mean of the minimum horizontal principal stress corresponding to all depth points within the layer segment to which each depth point belongs; similarly, for the local coefficient of variation... Specifically, it can be calculated by the ratio of the standard deviation to the mean of the minimum horizontal principal stress corresponding to all depth points within a preset-sized window centered on each depth point.

[0116] ( This is merely a linearly normalized localization of the stress at each depth point within the stress range of the layer segment. Its physical meaning is limited to the fracturing difficulty of that point relative to the most difficult and easiest points. However, by introducing a nonlinear weighting term that characterizes the difference between the current point stress and the local neighborhood average stress, This allows evaluation metrics to be viewed in a spatial context, rather than in isolation. The fracturability of a depth point depends not only on its absolute value but also on whether it is a local stress sweet spot, i.e., its stress is significantly lower than that of the surrounding rock.

[0117] During fracturing, when such localized low-stress points are surrounded by high stress, fracturing energy is more likely to concentrate, forming dominant fractures and connecting to more distant areas. Therefore, the third fracturing index calculated using the above formula can not only identify vulnerable fracturing points, but also prioritize the identification of those "engineering sweet spots" that are likely to become the starting point and dominant channel for fracture propagation.

[0118] Meanwhile, by introducing an adaptive stress contrast amplification factor This allows the calculation of the third fracturing index to be more robust against interference. The value is no longer a fixed constant, but is determined by the overall heterogeneity ( ) and local heterogeneity ( The relative relationship between local and global features is dynamically determined. When the local features deviate significantly from the global features ( (High value) The value will automatically decrease, thereby suppressing the over-amplification of local differences that may be caused by noise or minor geological anomalies.

[0119] This mechanism enhances the robustness and resistance to interference in the calculation of the third fracturing index. It automatically identifies and smooths out local anomalies that do not conform to the overall pattern, preventing unreasonable and drastic jumps in the assessment results and making the identified "sweet spots" more reliable. In generally homogeneous formations, a small local difference may be significant; however, in formations that are extremely heterogeneous overall, local differences are the norm, and their importance is relatively reduced. This makes the assessment results more consistent with geological reality.

[0120] Furthermore, the coefficient of variation was used as a measure of heterogeneity, enabling reliable comparisons between different strata. Specifically, the coefficient of variation (...) , The coefficient of variation (COP) is the ratio of the standard deviation to the mean, rather than using the standard deviation directly. The COP is a dimensionless standardized indicator. It eliminates the influence of absolute numerical values ​​and dimensions, allowing for direct and fair comparisons of the strength of heterogeneity between different stress levels and different layers. This establishes a unified standard for evaluating fracturability across layers and even across work areas, greatly enhancing the model's universality and comparability.

[0121] In some embodiments, the calculation of the weighting coefficients of the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well in step S20 above may specifically include: calculating the weighting coefficients of the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well using the following formula:

[0122] ;

[0123] In the formula, a, b, and c represent the first fracturing index, respectively. Second fracturing index Third fracturing index The normalized weighting coefficients are dimensionless; , , These represent the fracturing index at each depth point. , , The standard deviation of the set of values ​​at all depth points in the corresponding layer.

[0124] The above formula overcomes the subjectivity and experience-dependent nature of traditional manually set weights by using the standard deviation of each index within a segment as the basis for weight calculation. The weight coefficients are entirely determined by the discrete characteristics of the data itself, significantly improving the objectivity and reliability of weight allocation.

[0125] By quantifying the variation of each index using standard deviation, higher weights are automatically assigned to indices with drastic changes (large standard deviations) within a stratigraphic interval, amplifying the contribution of dominant factors to the overall evaluation. For example, in stratigraphic intervals with homogeneous lithology and stable mechanical properties, To become dominant The weight 'a' automatically increases; in layers with complex and varied mineral compositions, Enlargement leads to The increased weight of b highlights the controlling role of mineral heterogeneity; in stratigraphic intervals with drastic changes in geostress, Enlargement leads to The weight 'c' is increased, emphasizing the dominant role of geostress. This dynamic adjustment mechanism gives the evaluation model good geological adaptability. By objectively reflecting the variation characteristics of each index within the stratigraphic interval, the weight allocation is more in line with actual geological laws, enabling the comprehensive evaluation results to more accurately characterize the differences in reservoir fracturing capability and providing a more reliable basis for fracturing design.

[0126] In some embodiments, the calculation of the fourth fracturing index at each depth point based on the first fracturing index, the second fracturing index, the third fracturing index, and the corresponding weighting coefficients in step S20 above may specifically include: calculating the fourth fracturing index at each depth point using the following formula based on the first fracturing index, the second fracturing index, the third fracturing index, and the corresponding weighting coefficients at each depth point:

[0127] ;

[0128] In the formula, F is the fourth fracturing index at each depth point, which is dimensionless; a, b, and c represent the first fracturing index, respectively. Second fracturing index Third fracturing index The normalized weighting coefficients are dimensionless.

[0129] By introducing weighting coefficients a, b, and c, three independent evaluation indicators reflecting fracturing fluid action, mineral characteristics, and geostress conditions are organically integrated, overcoming the limitations of single-indicator evaluation and establishing a comprehensive evaluation system that fully reflects geomechanical characteristics and engineering parameters.

[0130] Furthermore, the fourth fracturing index generated through weighted fusion retains the physical meaning of each sub-index and reflects the differences in importance of each factor under different geological conditions through weight adjustment, making the evaluation results more valuable for engineering guidance and providing a more reliable basis for the selection of fracturing sections and the optimization of construction parameters.

[0131] Furthermore, the dynamic weighting mechanism enables the formula to adapt to changes in geological characteristics of different blocks and strata, and obtain reasonable comprehensive evaluation results without the need for manual weight adjustment, thereby improving the applicability and promotion value of the method under different geological conditions.

[0132] As can be seen from the three-dimensional fracturing capability modeling method for tight gas reservoirs provided in the embodiments of this specification above, the embodiments of this specification can calculate the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well in the reservoir based on the logging data at each depth point. The first fracturing index characterizes the crack propagation capability of the rock under the action of fracturing fluid, the second fracturing index characterizes the ability of the rock to form a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index characterizes the ability of the rock to fracture under the drive of heterogeneity of the geostress field. Based on the first fracturing index, the second fracturing index, and the third fracturing index at each depth point, the first rock mechanical distribution data at each depth point are clustered. Based on the clustering results and the second rock mechanical distribution data of the reservoir, a three-dimensional fracturing capability model of the reservoir is constructed. By integrating three key factors—fracturing fluid action, mineral composition heterogeneity, and geostress field heterogeneity—a first, second, and third fracturability index were established, forming a multi-mechanism synergistic fracturability evaluation system. This fundamentally overcomes the limitations of single-index evaluation and significantly improves the comprehensiveness and scientific rigor of the assessment. Based on this, cluster analysis of rock mechanics data using the three fracturability indices establishes a nonlinear correlation between a fourth fracturability index and multi-dimensional rock mechanics parameters, laying the foundation for subsequent accurate predictions. Furthermore, by combining wellpoint clustering patterns with three-dimensional rock mechanics data, a leap from discrete wellpoint evaluation to continuous three-dimensional spatial prediction was achieved, thereby constructing a three-dimensional fracturability model covering the entire reservoir. Based on the fracturability prediction results across the entire reservoir, sweet spots and difficult zones within the reservoir can be accurately identified, guiding the optimal selection of perforation locations, fracturing section length design, and optimization of construction parameters, significantly improving the targeting and effectiveness of fracturing stimulation.

[0133] The following is a specific embodiment of this specification:

[0134] Taking the fracturing capability evaluation scenario of a fractured well in a tight gas reservoir in an onshore LX well area as an example.

[0135] The LX well area is a developed block with generally stable tectonics and no large-scale faults in the surrounding area. The target strata are H4, H6, and H7, with no natural fractures around the wellbore. Three wells, LX1, LX2, and LX3, which have been fractured and developed, were selected as evaluation targets. Based on the logging data and core test results from these three wells, the mechanical properties (elastic modulus, Poisson's ratio, tangential modulus) of the target strata were obtained. Fracture toughness parameters (stress intensity factor) characterizing the ability of the target strata to prevent crack instability and propagation were obtained based on fracture mechanics experiments. The required fracturing data (injection rate and fracturing fluid viscosity) were obtained based on the hydraulic fracturing parameters. Mineral content was interpreted from the logging data. The minimum horizontal in-situ stress of the target strata was calculated using the logging data and tectonic field data. [Following the above steps...] Figure 2 The three-dimensional fracturing capability evaluation method for tight gas reservoirs shown is used to evaluate the fracturing capability of target intervals in three wells. Specifically:

[0136] Step S1: Calculate the fracturing index, which characterizes the ability of fracturing to generate crack area. The calculation formula is as follows:

[0137] ;

[0138] In the formula, The fracturing index represents the ability of fracturing to generate crack area, and is dimensionless. This represents Poisson's ratio, which is dimensionless. Represents the stress intensity factor, Pa·m 0.5 E represents the elastic modulus, Pa; G represents the shear modulus, Pa. The value of Q represents the fracturing fluid viscosity, Pa·s; Q represents the fracturing fluid injection rate, m. 3 / s.

[0139] Step S2: Calculate the fracturing index, which characterizes the complexity of the fracturing network. The calculation formula is as follows:

[0140] ;

[0141] In the formula, The fracturing index represents the ability to characterize the complexity of the fracturing network and is dimensionless; m represents the brittle mineral coefficient and is dimensionless; n represents the mineral heterogeneity coefficient and is dimensionless; m=0.5 and n=0.5 are taken. Indicates the content of brittle minerals in reservoir rocks, % . The value represents the total mineral content of the reservoir rock, expressed as %; N represents the total mineral types, dimensionless. This represents the content of the i-th mineral in the reservoir rock.

[0142] Step S3: Calculate the fracturing index, which characterizes the ease with which the rock fractures. The calculation formula is as follows:

[0143] ;

[0144] In the formula, The fracturing index represents the degree of ease with which rocks can fracture; it is dimensionless. This represents the maximum value among the minimum horizontal force values ​​for the target reservoir segment. This represents the minimum value among the minimum horizontal force values ​​of the target reservoir segment. This represents the minimum horizontal force at the calculation point of the target layer.

[0145] Step S4: Calculate the normalized weighting coefficients of the three fracturing indices. The calculation formula is as follows:

[0146] ;

[0147] In the formula, a, b, and c represent the fracturing index, respectively. , , The normalized weighting coefficients are dimensionless; , , These represent the fracturing index, respectively. , , The standard deviation of the set of numerical values ​​at each calculation point in the target reservoir segment.

[0148] Step S5: Calculate the comprehensive fracturing index, which takes into account the fracturing capacity to generate crack area, the complexity of the fracturing network, and the ease of rock fracturing. The calculation formula is as follows:

[0149] ;

[0150] In the formula, F represents the comprehensive fracturing index, which takes into account the ability to generate crack area, the complexity of the fracturing network, and the ease of rock fracturing.

[0151] The fracturing capability evaluation results of the three wells LX1, LX2, and LX3 are as follows: Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, the average composite fracturing indexes of the target formations in the three wells were 0.714, 0.482, and 0.296, respectively. The post-fracturing gas production of wells LX1, LX2, and LX3 was 28392 m³. 3 / d、7009 m 3 / d、3229 m 3 / d. The compressibility calculation results showed a significant consistency with the actual yield, demonstrating the reliability and effectiveness of the proposed method.

[0152] Step S6: A comprehensive data table is generated, showing the XYZ matching between well logging data, stress data, elastic modulus data, rock strength data, and comprehensive compressibility index data. The clustering analysis relationship between the comprehensive compressibility index data and the aforementioned data is obtained. This clustering analysis relationship is then applied to the three-dimensional stress data volume, three-dimensional elastic modulus data volume, and three-dimensional rock strength data volume of the reservoir to establish a three-dimensional compressibility model of the tight gas reservoir. Figure 9 and Figure 10 As shown. The fracturing index of a three-dimensional multi-directional profile horizontal well is as follows. Figure 11 Display. From Figure 9 , Figure 10 and Figure 11 The results of the spatial three-dimensional fracturing capability demonstration can help optimize well placement and fracturing intervals.

[0153] Based on the above-described method for modeling the three-dimensional fracturability of tight gas reservoirs, this specification also provides embodiments of a three-dimensional fracturability modeling device for tight gas reservoirs. For example... Figure 12 As shown, the tight gas reservoir three-dimensional fracturing capability modeling device 1200 may specifically include the following modules:

[0154] The calculation module 1201 is used to calculate the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well in the reservoir based on the logging data at each depth point. The first fracturing index characterizes the crack propagation ability of the rock under the action of fracturing fluid, the second fracturing index characterizes the ability of the rock to form a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index characterizes the ability of the rock to fracture under the drive of heterogeneity of the geostress field.

[0155] Clustering module 1202 is used to cluster the first rock mechanics distribution data at each depth point based on the first fracturing index, the second fracturing index and the third fracturing index at each depth point.

[0156] Module 1203 is used to construct a three-dimensional fracturing model of the reservoir based on the clustering results and the second rock mechanics distribution data of the reservoir.

[0157] In some embodiments, the above-mentioned calculation module 1201 can be specifically used for:

[0158] Acquire fracturing fluid viscosity data and fracturing fluid injection rate data at each depth point;

[0159] Based on the logging data at each depth point, determine the elastic modulus, Poisson's ratio, and stress intensity data of the rock at each depth point;

[0160] Based on the elastic modulus data, Poisson's ratio data, stress intensity data, fracturing fluid viscosity data, and fracturing fluid injection rate data at each depth point, the first fracturing index at each depth point is calculated; the first fracturing index is negatively correlated with stress intensity, fracturing fluid viscosity, and fracturing fluid injection rate.

[0161] In some embodiments, the above-described calculation module 1201 can also be used for:

[0162] Based on the elastic modulus, Poisson's ratio, stress intensity, fracturing fluid viscosity, and fracturing fluid injection rate data at each depth point, the first fracturing index at each depth point is calculated using the following formula:

[0163] ;

[0164] In the formula, is the first compressibility index at each depth point, dimensionless; Let be the Poisson's ratio of the rock at each depth point, which is dimensionless; Let be the stress intensity factor of the rock at each depth point, in Pa·m. 0.5 G is the shear modulus of the rock at each depth point, Pa; E is the elastic modulus of the rock at each depth point, Pa. ρ is the fracturing fluid viscosity at each depth point, Pa·s; Q is the fracturing fluid injection rate at each depth point, m. 3 / s.

[0165] In some embodiments, the above-described calculation module 1201 can also be used for:

[0166] Based on the mineral composition data and fracturing effect data of each gas production well in the reservoir, the brittle mineral coefficient and mineral heterogeneity coefficient of the rock at each depth point of each gas production well in the reservoir are determined.

[0167] Based on the logging data at each depth point, determine the total brittle mineral content, total mineral content, and individual mineral content of the rock at each depth point;

[0168] Based on the brittle mineral coefficient, mineral heterogeneity coefficient, total brittle mineral content data, total mineral content data, and individual mineral content data of the rock at each depth point, a second fracturing index is calculated at each depth point; the second fracturing index is positively correlated with mineral heterogeneity.

[0169] In some embodiments, the above-described calculation module 1201 can also be used for:

[0170] Based on the brittle mineral coefficient, mineral heterogeneity coefficient, total brittle mineral content, total mineral content, and individual mineral content data of the rock at each depth point, the second fracturing index at each depth point is calculated using the following formula:

[0171] ;

[0172] In the formula, is the second compressibility index at each depth point, dimensionless; m is the brittle mineral coefficient of the rock at each depth point, dimensionless; n is the mineral heterogeneity coefficient of the rock at each depth point, dimensionless. The total brittle mineral content of the rock at each depth point, % denoted as the total mineral content of the rock at each depth point, %; N represents the total mineral types in the rock at each depth point, dimensionless. Let be the content of the i-th mineral in the rock at each depth point.

[0173] In some embodiments, the above-described calculation module 1201 can also be used for:

[0174] Based on the logging data at each depth point, determine the minimum horizontal principal stress data of the rock at each depth point;

[0175] Based on the logging data at each depth point, determine the maximum and minimum horizontal principal stress data and the minimum horizontal principal stress data of the rock in the layer to which each depth point belongs;

[0176] Based on the minimum horizontal principal stress data of the rock at each depth point and the maximum and minimum horizontal principal stress data of the rock in the strata to which each depth point belongs, the third fracturing index at each depth point is calculated using the following formula:

[0177] ;

[0178] In the formula, is the third fracturing index at each depth point, dimensionless; The maximum and minimum horizontal principal stress in the rock within the layer at each depth point, in Pa; The minimum horizontal principal stress, in Pa, is the minimum stress of the rock in the layer at each depth point. Minimum horizontal principal stress of rock at each depth point, in Pa.

[0179] In some embodiments, the clustering module 1202 described above can be specifically used for:

[0180] Calculate the weighting coefficients of the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well; the weighting coefficients represent the dispersion of the fracturing index of all depth points in the layer to which each depth point belongs;

[0181] Based on the first, second, and third fracturing indices and their corresponding weighting coefficients at each depth point, the fourth fracturing index at each depth point is calculated.

[0182] Based on the fourth fracturing index at each depth point, the first rock mechanics distribution data at each depth point are clustered.

[0183] In some embodiments, the above-mentioned building module 1203 can be specifically used for:

[0184] Based on the clustering results, a fracturing index calculation model is constructed; the fracturing index calculation model is coupled with the mapping relationship between the fourth fracturing index of rock and the rock mechanical distribution;

[0185] Based on the second rock mechanics distribution data of the reservoir and the fracturing index calculation model, a three-dimensional fracturing model of the reservoir is constructed.

[0186] As can be seen from the tight gas reservoir three-dimensional fracturing capability modeling device provided in the above embodiments of this specification, the embodiments of this specification can calculate the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well based on the logging data at each depth point of each gas production well in the reservoir. The first fracturing index characterizes the crack propagation capability of the rock under the action of fracturing fluid, the second fracturing index characterizes the ability of the rock to form a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index characterizes the ability of the rock to fracture under the drive of heterogeneity of the geostress field. Based on the first fracturing index, the second fracturing index, and the third fracturing index at each depth point, the first rock mechanical distribution data at each depth point are clustered. Based on the clustering results and the second rock mechanical distribution data of the reservoir, a three-dimensional fracturing capability model of the reservoir is constructed. By integrating three key factors—fracturing fluid action, mineral composition heterogeneity, and geostress field heterogeneity—a first, second, and third fracturability index were established, forming a multi-mechanism synergistic fracturability evaluation system. This fundamentally overcomes the limitations of single-index evaluation and significantly improves the comprehensiveness and scientific rigor of the assessment. Based on this, cluster analysis of rock mechanics data using the three fracturability indices establishes a nonlinear correlation between a fourth fracturability index and multi-dimensional rock mechanics parameters, laying the foundation for subsequent accurate predictions. Furthermore, by combining wellpoint clustering patterns with three-dimensional rock mechanics data, a leap from discrete wellpoint evaluation to continuous three-dimensional spatial prediction was achieved, thereby constructing a three-dimensional fracturability model covering the entire reservoir. Based on the fracturability prediction results across the entire reservoir, sweet spots and difficult zones within the reservoir can be accurately identified, guiding the optimal selection of perforation locations, fracturing section length design, and optimization of construction parameters, significantly improving the targeting and effectiveness of fracturing stimulation.

[0187] This specification also provides a computer device for a three-dimensional fracturability modeling method for tight gas reservoirs, including a processor and a memory for storing processor-executable instructions. Specifically, the processor can perform the following tasks according to the instructions: Based on logging data at each depth point of each gas production well in the reservoir, calculate a first fracturability index, a second fracturability index, and a third fracturability index at each depth point of each gas production well. The first fracturability index characterizes the crack propagation capability of the rock under the action of fracturing fluid; the second fracturability index characterizes the ability of the rock to form a three-dimensional fracture network under conditions of heterogeneous mineral composition distribution; and the third fracturability index characterizes the ability of the rock to fracture under the drive of heterogeneous geostress field. Based on the first fracturability index, the second fracturability index, and the third fracturability index at each depth point, cluster the first rock mechanical distribution data at each depth point; and based on the clustering results and the second rock mechanical distribution data of the reservoir, construct a three-dimensional fracturability model of the reservoir.

[0188] To execute the above instructions more accurately, please refer to... Figure 13 As shown in the embodiments of this specification, another specific computer device 1300 is also provided, wherein the computer device 1300 includes a network communication port 1301, a processor 1302 and a memory 1303, and the above structures are connected by internal cables so that the various structures can perform specific data interaction.

[0189] The processor 1302 can specifically be used to: calculate a first fracturing index, a second fracturing index, and a third fracturing index at each depth point of each gas production well in the reservoir based on logging data at each depth point. The first fracturing index characterizes the crack propagation ability of the rock under the action of fracturing fluid, the second fracturing index characterizes the ability of the rock to form a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index characterizes the ability of the rock to fracture under the drive of heterogeneity of the geostress field; cluster the first rock mechanical distribution data at each depth point based on the first fracturing index, the second fracturing index, and the third fracturing index; and construct a three-dimensional fracturing model of the reservoir based on the clustering results and the second rock mechanical distribution data of the reservoir.

[0190] The memory 1303 can be used to store the corresponding instruction program.

[0191] In this embodiment, the network communication port 1301 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0192] In this embodiment, the processor 1302 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0193] In this embodiment, the memory 1303 includes volatile memory and non-volatile memory. The memory 1303 can include multiple layers. In digital systems, anything that can store binary data can be a memory; in integrated circuits, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0194] This specification also provides a computer program product, including at least one instruction or at least one program segment, wherein the at least one instruction or the at least one program segment is loaded and executed by a processor to achieve the following: Figure 1 The method shown.

[0195] It should be understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0196] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this specification generally indicates that the preceding and following related objects have an "or" relationship.

[0197] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0198] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0199] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0200] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational tasks to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The task is a function specified in one or more boxes.

[0201] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for three-dimensional fracturing modeling of tight gas reservoirs, characterized in that, include: Based on the logging data at each depth point of each gas production well in the reservoir, the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well are calculated. The first fracturing index characterizes the crack propagation ability of the rock under the action of fracturing fluid, the second fracturing index characterizes the ability of the rock to form a three-dimensional fracture network under the condition of heterogeneous distribution of mineral components, and the third fracturing index characterizes the ability of the rock to fracture under the drive of heterogeneity of the geostress field. Based on the first fracturing index, the second fracturing index, and the third fracturing index at each depth point, the first rock mechanics distribution data at each depth point are clustered. Based on the clustering results and the second rock mechanics distribution data of the reservoir, a three-dimensional fracturing model of the reservoir is constructed.

2. The method according to claim 1, characterized in that, The calculation of the first fracturing index at each depth point includes: Acquire fracturing fluid viscosity data and fracturing fluid injection rate data at each depth point; Based on the logging data at each depth point, determine the elastic modulus, Poisson's ratio, and stress intensity data of the rock at each depth point; Based on the elastic modulus data, Poisson's ratio data, stress intensity data, fracturing fluid viscosity data, and fracturing fluid injection rate data at each depth point, the first fracturing index at each depth point is calculated; the first fracturing index is negatively correlated with stress intensity, fracturing fluid viscosity, and fracturing fluid injection rate.

3. The method according to claim 2, characterized in that, The calculation of the first fracturing index at each depth point includes: Based on the elastic modulus, Poisson's ratio, stress intensity, fracturing fluid viscosity, and fracturing fluid injection rate data at each depth point, the first fracturing index at each depth point is calculated using the following formula: ； In the formula, is the first compressibility index at each depth point, dimensionless; Let be the Poisson's ratio of the rock at each depth point, which is dimensionless; Let be the stress intensity factor of the rock at each depth point, in Pa·m. 0.5 G is the shear modulus of the rock at each depth point, Pa; E is the elastic modulus of the rock at each depth point, Pa. ρ is the fracturing fluid viscosity at each depth point, Pa·s; Q is the fracturing fluid injection rate at each depth point, m. 3 / s.

4. The method according to claim 1, characterized in that, The calculation of the second fracturing index at each depth point includes: Based on the mineral composition data and fracturing effect data of each gas production well in the reservoir, the brittle mineral coefficient and mineral heterogeneity coefficient of the rock at each depth point of each gas production well in the reservoir are determined. Based on the logging data at each depth point, determine the total brittle mineral content, total mineral content, and individual mineral content of the rock at each depth point; Based on the brittle mineral coefficient, mineral heterogeneity coefficient, total brittle mineral content data, total mineral content data, and individual mineral content data of the rock at each depth point, a second compressibility index is calculated at each depth point; the second compressibility index is positively correlated with mineral heterogeneity.

5. The method according to claim 4, characterized in that, The calculation of the second fracturing index at each depth point includes: Based on the brittle mineral coefficient, mineral heterogeneity coefficient, total brittle mineral content, total mineral content, and individual mineral content data of the rock at each depth point, the second fracturing index at each depth point is calculated using the following formula: ； In the formula, is the second compressibility index at each depth point, dimensionless; m is the brittle mineral coefficient of the rock at each depth point, dimensionless; n is the mineral heterogeneity coefficient of the rock at each depth point, dimensionless. The total brittle mineral content of the rock at each depth point, % denoted as the total mineral content of the rock at each depth point, %; N represents the total mineral types in the rock at each depth point, dimensionless. Let be the content of the i-th mineral in the rock at each depth point.

6. The method according to claim 1, characterized in that, The calculation of the third fracturing index at each depth point includes: Based on the logging data at each depth point, determine the minimum horizontal principal stress data of the rock at each depth point; Based on the logging data at each depth point, determine the maximum and minimum horizontal principal stress data and the minimum horizontal principal stress data of the rock in the layer to which each depth point belongs; Based on the minimum horizontal principal stress data of the rock at each depth point and the maximum and minimum horizontal principal stress data of the rock in the strata to which each depth point belongs, the third fracturing index at each depth point is calculated using the following formula: ； In the formula, is the third fracturing index at each depth point, dimensionless; The maximum and minimum horizontal principal stress in the rock within the layer at each depth point, in Pa; The minimum horizontal principal stress, in Pa, is the minimum stress of the rock in the layer at each depth point. Minimum horizontal principal stress of rock at each depth point, in Pa.

7. The method according to claim 1, characterized in that, The clustering of the first rock mechanics distribution data at each depth point based on the first fracturing index, the second fracturing index, and the third fracturing index includes: Calculate the weighting coefficients of the first fracturing index, the second fracturing index, and the third fracturing index at each depth point of each gas production well; the weighting coefficients represent the dispersion of the fracturing index of all depth points in the layer to which each depth point belongs; Based on the first, second, and third fracturing indices and their corresponding weighting coefficients at each depth point, the fourth fracturing index at each depth point is calculated. Based on the fourth fracturing index at each depth point, the first rock mechanics distribution data at each depth point are clustered.

8. The method according to claim 1, characterized in that, The construction of a three-dimensional fracturing model of the reservoir based on the clustering results and the second rock mechanical distribution data of the reservoir includes: Based on the clustering results, a fracturing index calculation model is constructed; the fracturing index calculation model is coupled with the mapping relationship between the fourth fracturing index of rock and the rock mechanical distribution; Based on the second rock mechanics distribution data of the reservoir and the fracturing index calculation model, a three-dimensional fracturing model of the reservoir is constructed.

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