Hydraulic fracturing network construction method and system based on geomechanical characteristics

Abstract

The application discloses a kind of based on geomechanics feature drive's hydraulic fracturing network construction method and system, belong to oil and gas reservoir well site fracturing technical field, this method includes: based on multiple-source geologic data reservoir geologic feature model is established;Based on the model calculates fracture controllability index (CFI), to quantitatively evaluate fracture predictability and controllability;Based on CFI and development demand definition with geometric form and conductivity as quantization target target fracture network structure;Geomechanics-fluid-particle three-field coupling model is constructed, and fracture and proppant collaborative evolution numerical simulation is carried out;With target network as constraint to simulation result inversion optimization, obtain optimal injection parameter sequence.The application realizes that fracturing design is from experience drive to geologic feature quantitative drive, from passive prediction to active design, from parameter trial and error to intelligent optimization radical change, significantly improves the controllability of fracture network, reconstruction volume and proppant laying effect.

Description

Technical Field

[0001] This invention relates to the field of well site fracturing technology, and in particular to a method and system for constructing a hydraulic fracturing network based on geomechanical characteristics. Background Technology

[0002] Traditional fracturing construction methods often rely solely on simplified geological models and two-dimensional or weakly coupled simulation frameworks, which are insufficient to reflect the combined control of multi-scale heterogeneity, stress disturbance, and natural fracture systems on fracture propagation in ultra-large reservoirs. This results in insufficient fracture connectivity, uneven proppant placement, and low effective stimulation volume.

[0003] Furthermore, most current fracturing methods do not establish quantitative evaluation indicators for the differences in reservoir geological structure, and lack a systematic design framework for "controllable fracture network structure". Without the identification of geological characteristic driving mechanisms, relying solely on pumping parameters or a single simulation strategy often leads to problems such as fracture deflection, flow imbalance, and cross-layer crosstalk, further reducing the effectiveness of the fracturing network.

[0004] The geological characteristics of ultra-large reservoirs are complex, including lithological heterogeneity, differences in the directionality of natural fracture density, the degree of development of mechanically weak surfaces within the layers, and variations in regional stress gradients. These factors not only determine the fracture initiation location and propagation path, but also significantly affect the depositional morphology of proppant along the fracture system, sandbank stability, and far-end capping capacity.

[0005] Traditional fracture propagation simulations have significant limitations in handling such complex coupled problems: two-dimensional models cannot represent spatial fracture interactions, the assumption of local homogeneity leads to prediction bias, and the proppant placement process is often weakened or simplified. Furthermore, most injection strategy designs do not establish a connection with geological control factors and lack a design concept of "target fracture network morphology," resulting in delayed injection program updates and the inability to differentiate construction parameters for different geological patterns.

[0006] In view of this, the present invention is hereby proposed. Summary of the Invention

[0007] This invention proposes a method and system for constructing hydraulic fracturing networks based on geomechanical characteristics. It can quantitatively identify the controllability of fractures based on reservoir geological characteristics and establish a mapping relationship between the target fracture network structure and the injection strategy, thereby achieving overall optimization of fracture geometry, connectivity and proppant placement behavior in ultra-large fracturing scenarios.

[0008] Specifically, the following technical solution was adopted:

[0009] A method for constructing hydraulic fracturing networks based on geomechanical characteristics includes:

[0010] Based on multi-source geological data of the target reservoir, a reservoir geological characteristic model is established to characterize its heterogeneity;

[0011] Based on the reservoir geological characteristic model, the fracture controllability index is calculated to quantitatively evaluate the predictability and controllability of fractures in the target reservoir.

[0012] Based on the crack controllability index and the preset development requirement indicators, a target crack network structure is defined with crack network geometry and flow conduction capacity as quantitative objectives.

[0013] Based on the reservoir geological feature model and the target fracture network structure, a numerical model dynamically coupled with the geomechanical field, fluid flow field and proppant particle field is constructed to perform numerical simulation of the co-evolution of fracture propagation process and proppant migration process, and to predict fracture morphology and proppant distribution under different injection parameters.

[0014] Using the target crack network structure as the desired output and the injection parameters as optimization variables, the results of the co-evolution numerical simulation are inverted and optimized. Through iterative optimization, the optimal injection parameter sequence that makes the simulation results approximate the target crack network structure within a preset tolerance range is obtained.

[0015] As an optional embodiment of the present invention, in the method for constructing a hydraulic fracturing network driven by geomechanical characteristics, the step of establishing a reservoir geological characteristic model characterizing the heterogeneity of the target reservoir based on multi-source geological data includes:

[0016] Collect and process multi-source geological data of the target reservoir, wherein the multi-source geological data includes at least data reflecting lithological structure, natural fracture system, type of weak surface within the layer, regional stress distribution, porosity and permeability parameters and geological heterogeneity.

[0017] Based on the multi-source geological data, a unified, three-dimensional spatially distributed reservoir geological characteristic model is generated through geological modeling and data fusion methods. This model integrates lithological mechanical properties, natural fracture network, weak surface distribution, geostress field, and physical property parameter field.

[0018] As an optional embodiment of the present invention, in the method for constructing a hydraulic fracturing network based on geomechanical characteristics, the step of calculating the fracture controllability index based on the reservoir geological characteristic model includes:

[0019] Based on the reservoir geological characteristic model, several predefined geomechanical evaluation indicators are extracted;

[0020] The multiple geomechanical evaluation indicators are normalized and weighted and summed according to their respective contributions to the controllability of fracture propagation to calculate a fracture controllability index between 0 and 1. The higher the value of the fracture controllability index, the stronger the predictability and controllability of the fracture in the target reservoir.

[0021] The predefined geomechanical evaluation indicators include at least three of the following: lithological strength difference, natural fracture density, stress gradient, weak surface distribution density, and reservoir continuity index.

[0022] As an optional embodiment of the present invention, in the hydraulic fracturing network construction method driven by geomechanical characteristics, a target fracture network structure is defined based on the fracture controllability index and preset development demand indicators, with the fracture network geometry and conductivity as quantitative objectives, including:

[0023] A quantitative objective is defined for the geometry of the fracture network, wherein the quantitative objective includes at least one of the main fracture propagation direction, branch fracture density, and effective fracture propagation range;

[0024] Define a quantitative target for the flow guidance capability, wherein the quantitative target includes at least one of the inter-cluster connectivity index and the proppant reachability region;

[0025] The effective propagation range of the crack is defined by the combined distribution of the length of the main crack and the length of the branch crack; the proppant accessibility area is quantified by the proppant distal coverage rate or distal accessibility index.

[0026] As an optional embodiment of the present invention, in the hydraulic fracturing network construction method driven by geomechanical characteristics, the quantitative target of the target fracture network structure is differentially set based on the fracture controllability index:

[0027] Based on the preset low controllability threshold and high controllability threshold, the numerical range of the crack controllability index is divided into a low controllability interval that is less than or equal to the low controllability threshold, a medium controllability interval that is between the low controllability threshold and the high controllability threshold, and a high controllability interval that is greater than or equal to the high controllability threshold.

[0028] When the crack controllability index is in the low controllability range, the primary goal is to improve the basic connectivity and expansion stability of the crack network, and the inter-cluster connectivity index and the effective expansion range of the crack are set first.

[0029] When the crack controllability index is in the high controllability range, the primary goal is to optimize proppant placement efficiency and improve overall flowability, and the proppant accessibility area and branch crack density are preferentially set.

[0030] As an optional embodiment of the present invention, in the method for constructing a hydraulic fracturing network based on geomechanical characteristics, the construction and simulation process of the numerical model considers the following physical mechanisms:

[0031] Crack tip propagation criteria are used to determine crack initiation and dynamic propagation.

[0032] Inter-cluster stress disturbance is used to simulate the competitive propagation behavior among multiple crack clusters;

[0033] The mechanism of proppant retention point formation is used to predict proppant migration and local accumulation in fracture networks;

[0034] Sandbank stability is used to assess the proppant placement pattern and long-term conductivity in cracks.

[0035] The numerical simulation of the co-evolution of crack propagation and proppant migration is achieved by using coupled solution or strong sequential iteration within the framework of the numerical model to enable the interaction and dynamic feedback of four physical mechanisms: crack tip propagation criteria, inter-cluster stress interference, proppant retention point formation mechanism, and sandbank stability.

[0036] The output of the co-evolutionary numerical simulation can quantitatively characterize at least one of the following engineering indicators:

[0037] The non-uniform propagation length and spatial morphology of cracks in each cluster affected by inter-cluster stress interference;

[0038] The migration front and local concentration distribution of proppant in the fracture network controlled by the proppant retention point formation mechanism;

[0039] The effective support area and flow conduction capacity profile of the final proppant affected by the stability of the sand embankment.

[0040] As an optional embodiment of the present invention, in the hydraulic fracturing network construction method driven by geomechanical characteristics, the inversion optimization is achieved by constructing and solving an inversion problem with the quantified target of the target fracture network structure as the expected value and the simulation results as the predicted value, including:

[0041] Construct an objective function to quantify the comprehensive deviation between the crack morphology and proppant distribution predicted by the co-evolution numerical simulation and the quantification objective of the geometric morphology and flow carrying capacity of the target crack network structure;

[0042] Using injection displacement, fluid viscosity, sand addition procedure, and inter-cluster fracture initiation timing as optimization variables, an optimization algorithm is used to iteratively adjust the optimization variables to minimize the objective function, thereby obtaining the optimal injection parameter sequence.

[0043] The objective function is a multi-objective weighted function, which includes at least one of the following four sub-objective items: crack connectivity deviation, branch density deviation, proppant coverage deviation, and flow conduction capacity deviation.

[0044] As an optional embodiment of the present invention, in the hydraulic fracturing network construction method driven by geomechanical characteristics, the weight coefficients of each sub-objective item in the multi-objective weighting function are dynamically configured according to the fracture controllability index; wherein, when the fracture controllability index is low, the weight of the fracture connectivity deviation sub-objective item is increased; when the fracture controllability index is high, the weights of the proppant coverage deviation and conductivity deviation sub-objective items are increased.

[0045] As an optional embodiment of the present invention, in the hydraulic fracturing network construction method driven by geomechanical characteristics, the parameter combination in the optimal injection parameter sequence constitutes a control strategy to enhance fracture controllability. The control strategy includes at least one of segmented variable discharge rate, clustered delayed start-up, fluid viscosity step change and multi-stage sand addition procedure.

[0046] The ultimate goal of the inversion optimization is to maximize the reservoir stimulation volume, proppant far-end coverage, and network connectivity while satisfying the constraints of the target fracture network structure; the reservoir stimulation volume is calculated from the fracture network geometry predicted by the co-evolution numerical simulation.

[0047] This invention also provides a hydraulic fracturing network construction system driven by geomechanical characteristics, comprising:

[0048] The geological feature modeling module is used to establish reservoir geological feature models that characterize the heterogeneity of the target reservoir based on multi-source geological data.

[0049] The controllability evaluation module is used to calculate the fracture controllability index based on the reservoir geological characteristic model, so as to quantitatively evaluate the predictability and controllability of fractures in the target reservoir.

[0050] The target network construction module is used to define a target crack network structure with crack network geometry and flow conduction capacity as quantitative targets, based on the crack controllability index and preset development requirement indicators.

[0051] The co-evolution simulation module is used to construct a numerical model that dynamically couples the geomechanical field, fluid flow field, and proppant particle field based on the reservoir geological feature model and the target fracture network structure. It performs co-evolution numerical simulation of the fracture propagation process and the proppant migration process to predict the fracture morphology and proppant distribution under different injection parameters.

[0052] The inversion optimization module is used to perform inversion optimization on the results of the co-evolution numerical simulation with the target crack network structure as the desired output and the injection parameters as the optimization variables. Through iterative optimization, the optimal injection parameter sequence that makes the simulation results approximate the target crack network structure within a preset tolerance range is obtained.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] 1. It has achieved a fundamental shift from "experience-driven" to "quantitative geological feature-driven" approaches, enhancing the scientific rigor and relevance of the design.

[0055] By establishing a refined reservoir geological characteristic model and calculating the fracture controllability index (CFI), this study quantifies and indexes the complex geological factors affecting fracture propagation (such as heterogeneity, natural fractures, geostress, and weak surfaces) for the first time, providing an objective and unified evaluation benchmark for subsequent design. This overcomes the blind spots and inconsistencies caused by traditional design relying on simplified models and engineers' personal experience, ensuring that fracturing design is firmly rooted in the unique geomechanical "genes" of the target reservoir.

[0056] 2. It has achieved a leap from "passive prediction" to "active design", enhancing the controllability of crack network morphology.

[0057] This innovative approach proposes the concept of Targeted Fracture Network Structure (T-FNM), allowing engineers to proactively define desired fracture geometries (such as branch density and propagation range) and conductivity (such as connectivity and proppant accessibility) based on the Fracture Controllability Index (CFI) and development requirements. This overcomes the limitations of traditional methods that passively simulate fracture morphology under given parameters, transforming fracturing design into a proactive optimization process of "setting objectives first, then seeking implementation paths," significantly enhancing control over the final fracturing outcome.

[0058] 3. It has achieved "high-fidelity, fully coupled" simulation of the entire fracturing process, which has greatly improved the accuracy and reliability of prediction.

[0059] A geomechanical-fluid-particle three-field dynamic coupling model is used for co-evolution simulation, which can simultaneously and accurately characterize physical processes such as rock fracturing, fracturing fluid flow and loss, and proppant transport and sedimentation, as well as their real-time interactions. This model can effectively predict key complex phenomena such as inter-cluster stress interference and proppant retention, thereby obtaining more realistic predictions of fracture morphology and proppant distribution. This provides a reliable basis for optimization and reduces decision-making errors caused by simulation distortion.

[0060] 4. It has achieved an upgrade from "parameter trial and error" to "intelligent inversion optimization", ensuring the optimality and economy of the construction plan.

[0061] Using a proactively defined target fracture network structure (T-FNM) as the desired objective, an inversion optimization algorithm automatically searches for the optimal sequence of injection parameters (such as displacement and proppant addition procedures). This forms an intelligent closed loop of "target setting - simulation verification - inversion optimization," systematically seeking the optimal solution to achieve the intended modification effect, replacing the inefficient traditional model that relies on extensive trial calculations and manual adjustments. The inversion optimization process can accept manually set constraints or intervention adjustments to combine engineering experience and geological knowledge, ensuring the engineering feasibility of the optimization results. This method can quickly find globally or locally optimal construction schemes that balance fracture propagation, proppant placement, and cost-effectiveness in a complex parameter space.

[0062] 5. It comprehensively improves the overall effect and construction success rate of fracturing transformation, and has significant practical engineering value.

[0063] In summary, the hydraulic fracturing network construction method based on geomechanical characteristics of the present invention, through systematic optimization, can ultimately achieve:

[0064] Larger effective transformation volume (SRV): Through proactive design and optimization, a more complex and wider-coverage fracture network is formed.

[0065] Superior proppant placement and flowability: Ensures effective proppant transport to the target area, forming a high-flow-capacity channel and delaying crack closure.

[0066] Greater construction controllability and success rate: Reduce the risk of uncontrolled cracks, early sand blockage, or layer migration through pre-assessment and targeted design using the Crack Controllability Index (CFI).

[0067] Enhanced reservoir adaptability and decision-making efficiency: A single approach can adapt to different geological conditions (distinguished by the fracture controllability index CFI) and shorten the design cycle and improve decision-making efficiency through intelligent processes.

[0068] Therefore, this invention provides a hydraulic fracturing network construction method that combines theoretical advancement, predictive accuracy, and engineering practicality, which is of great significance for promoting the efficient development of unconventional oil and gas resources. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating the method for constructing a hydraulic fracturing network based on geomechanical characteristics, as described in an embodiment of the present invention.

[0070] Figure 2 This is a schematic diagram of the target crack network structure in an embodiment of the present invention. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0072] Therefore, the following detailed description of embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely illustrates some embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0073] It should be noted that, unless otherwise specified, the embodiments and features and technical solutions in the present invention can be combined with each other.

[0074] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0075] In the description of this invention, it should be noted that the terms "upper," "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. These terms are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. In addition, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0076] like Figure 1 and Figure 2 As shown, this embodiment of the invention provides a method for constructing a hydraulic fracturing network based on geomechanical characteristics, including:

[0077] Based on multi-source geological data of the target reservoir, a reservoir geological characteristic model is established to characterize its heterogeneity;

[0078] Based on the reservoir geological characteristic model, the fracture controllability index (CFI) is calculated to quantitatively evaluate the predictability and controllability of fractures in the target reservoir.

[0079] Based on the Crack Controllability Index (CFI) and the preset development requirement indicators, a target crack network structure (T-FNM) is defined with crack network geometry and flow conduction capacity as quantitative objectives.

[0080] Based on the reservoir geological feature model and the target fracture network structure (T-FNM), a numerical model dynamically coupled with the geomechanical field, fluid flow field and proppant particle field is constructed to perform numerical simulation of the co-evolution of fracture propagation process and proppant migration process, and to predict fracture morphology and proppant distribution under different injection parameters.

[0081] Using the target crack network structure as the desired output and the injection parameters as optimization variables, the results of the co-evolution numerical simulation are inverted and optimized. Through iterative optimization, the optimal injection parameter sequence that makes the simulation results approximate the target crack network structure within a preset tolerance range is obtained.

[0082] An embodiment of the present invention provides a method for constructing a hydraulic fracturing network based on geomechanical characteristics. By constructing a complete, closed-loop design process driven by geomechanical characteristics, it achieves the following significant technical effects:

[0083] 1. It has achieved a fundamental shift from "experience-driven" to "quantitative geological feature-driven" approaches, enhancing the scientific rigor and relevance of the design.

[0084] By establishing a refined reservoir geological characteristic model and calculating the fracture controllability index (CFI), this study quantifies and indexes the complex geological factors affecting fracture propagation (such as heterogeneity, natural fractures, geostress, and weak surfaces) for the first time, providing an objective and unified evaluation benchmark for subsequent design. This overcomes the blind spots and inconsistencies caused by traditional design relying on simplified models and engineers' personal experience, ensuring that fracturing design is firmly rooted in the unique geomechanical "genes" of the target reservoir.

[0085] 2. It has achieved a leap from "passive prediction" to "active design", enhancing the controllability of crack network morphology.

[0086] The innovative approach proposes the concept of Targeted Fracture Network Structure (T-FNM), allowing engineers to proactively define desired fracture geometries (such as branch density and propagation range) and conductivity (such as connectivity and proppant accessibility) based on the Fracture Controllability Index (CFI) and development requirements. This overcomes the limitations of traditional methods that can only passively simulate fracture morphology under given parameters, transforming fracturing design into a proactive optimization process of "setting goals first and then seeking implementation paths," significantly enhancing control over the final fracturing outcome.

[0087] 3. It has achieved "high-fidelity, fully coupled" simulation of the entire fracturing process, which has greatly improved the accuracy and reliability of prediction.

[0088] A geomechanical-fluid-particle three-field dynamic coupling model is used for co-evolution simulation, which can simultaneously and accurately characterize physical processes such as rock fracturing, fracturing fluid flow and loss, and proppant transport and sedimentation, as well as their real-time interactions. This model can effectively predict key complex phenomena such as inter-cluster stress interference and proppant retention, thereby obtaining more realistic predictions of fracture morphology and proppant distribution. This provides a reliable basis for optimization and reduces decision-making errors caused by simulation distortion.

[0089] 4. It has achieved an upgrade from "parameter trial and error" to "intelligent inversion optimization", ensuring the optimality and economy of the construction plan.

[0090] Using a proactively defined target fracture network structure (T-FNM) as the desired objective, an inversion optimization algorithm automatically searches for the optimal sequence of injection parameters (such as displacement and proppant addition procedures). This forms an intelligent closed loop of "target setting - simulation verification - inversion optimization," systematically seeking the optimal solution to achieve the intended modification effect, replacing the inefficient traditional model that relies on extensive trial calculations and manual adjustments. The inversion optimization process can accept manually set constraints or intervention adjustments to combine engineering experience and geological knowledge, ensuring the engineering feasibility of the optimization results. This method can quickly find globally or locally optimal construction schemes that balance fracture propagation, proppant placement, and cost-effectiveness in a complex parameter space.

[0091] 5. It comprehensively improves the overall effect and construction success rate of fracturing transformation, and has significant practical engineering value.

[0092] In summary, the hydraulic fracturing network construction method based on geomechanical characteristics, as described in this embodiment of the invention, through systematic optimization, can ultimately achieve:

[0093] Larger effective transformation volume (SRV): Through proactive design and optimization, a more complex and wider-coverage fracture network is formed.

[0094] Superior proppant placement and flowability: Ensures effective proppant transport to the target area, forming a high-flow-capacity channel and delaying crack closure.

[0095] Greater construction controllability and success rate: Reduce the risk of uncontrolled cracks, early sand blockage, or layer migration through pre-assessment and targeted design using the Crack Controllability Index (CFI).

[0096] Enhanced reservoir adaptability and decision-making efficiency: A single approach can adapt to different geological conditions (distinguished by the fracture controllability index CFI) and shorten the design cycle and improve decision-making efficiency through intelligent processes.

[0097] Therefore, the embodiments of the present invention provide a hydraulic fracturing network construction method that combines theoretical advancement, predictive accuracy, and engineering practicality, which is of great significance for promoting the efficient development of unconventional oil and gas resources.

[0098] In the method for constructing a hydraulic fracturing network based on geomechanical characteristics in this invention, the step of establishing a reservoir geological characteristic model characterizing the heterogeneity of the target reservoir based on multi-source geological data includes:

[0099] Collect and process multi-source geological data of the target reservoir, wherein the multi-source geological data includes at least data reflecting lithological structure, natural fracture system, type of weak surface within the layer, regional stress distribution, porosity and permeability parameters and geological heterogeneity.

[0100] Based on the multi-source geological data, a unified, three-dimensional spatially distributed reservoir geological characteristic model is generated through geological modeling and data fusion methods. This model integrates lithological mechanical properties, natural fracture network, weak surface distribution, geostress field, and physical property parameter field.

[0101] This invention, through high-level fusion and unified characterization of multi-source geological data, has for the first time constructed a three-dimensional, all-element reservoir geological characteristic model integrating lithology mechanics, natural fractures, geological weak points, geostress fields, and physical property parameters. This model completely overcomes the limitations of traditional modeling methods, which often suffer from fragmented data and simplified characterization. It achieves a high-fidelity, integrated depiction of reservoir heterogeneity and key structural factors, providing a unique and reliable geomechanical foundation for subsequent quantitative evaluation of fracture controllability, accurate simulation of complex fracture networks, and proactive optimization design. This represents a crucial breakthrough in improving the scientific rigor and accuracy of fracturing design from its inception.

[0102] Furthermore, the reservoir geological characteristic model is a unified three-dimensional data volume that integrates lithological mechanical fields, discrete fracture networks, geostress tensor fields, and weak surface attribute fields. These fields are spatially aligned and interconnected, including the simultaneous construction of the following coupled sub-models and attribute fields, unified within the same three-dimensional mesh system:

[0103] Based on the lithological structure data, a three-dimensional lithological mechanical parameter field characterizing the spatial distribution of lithology and the corresponding parameters is constructed.

[0104] Based on the data of the natural fracture system, a discrete fracture network model characterizing the geometry and connectivity of fractures is constructed.

[0105] Based on the stress distribution data of the region, a spatial tensor field characterizing the magnitude and direction of three-dimensional geostress is constructed.

[0106] Based on the data on weak surface types within the layer, a weak surface distribution attribute field is constructed to characterize the spatial attitude and mechanical properties of weak surfaces.

[0107] Specifically, the three-dimensional lithological mechanical parameter field includes at least the three-dimensional spatial distribution data of Young's modulus, Poisson's ratio, and uniaxial compressive strength parameters;

[0108] The three-dimensional geostress tensor field includes at least the three-dimensional spatial distribution data of the magnitude and direction of the maximum horizontal principal stress, the minimum horizontal principal stress, and the vertical stress.

[0109] In the geomechanical feature-driven hydraulic fracturing network construction method of this embodiment, the calculation of the fracture controllability index based on the reservoir geological feature model includes:

[0110] Based on the reservoir geological characteristic model, several predefined geomechanical evaluation indicators are extracted;

[0111] The multiple geomechanical evaluation indicators are normalized and weighted and summed according to their respective contributions to the controllability of fracture propagation to calculate a fracture controllability index between 0 and 1. The higher the value of the fracture controllability index, the stronger the predictability and controllability of the fracture in the target reservoir.

[0112] The predefined geomechanical evaluation indicators include at least three of the following: lithological strength difference, natural fracture density, stress gradient, weak surface distribution density, and reservoir continuity index.

[0113] Specifically, optionally, in the weighted summation, the weak surface distribution density index is given a higher weight for reservoirs with low controllability, and the stress gradient index is given a higher weight for reservoirs with high controllability.

[0114] This embodiment has made a breakthrough in the evaluation of fracture controllability, and has created the first fracture controllability index (CFI) based on the fusion of multiple geomechanical indicators and dynamic weights. For the first time, it has elevated the fracturing capability of complex reservoirs from a qualitative experience judgment to a standardized scientific evaluation that is quantifiable and comparable.

[0115] This method integrates key indicators from multiple dimensions, including lithology, fractures, stress, weak surfaces, and continuity. It innovatively introduces an adaptive mechanism that dynamically adjusts indicator weights based on formation "endowment" (low / high controllability). This allows the Fracture Controllability Index (CFI) to not only objectively quantify the reservoir's "friendliness" to fracture propagation but also accurately diagnose the core geological contradictions restricting controllability (such as weak surface dominance or stress dominance). This provides a precise decision-making basis for subsequently setting differentiated design objectives and optimization strategies "tailored to local conditions."

[0116] In the hydraulic fracturing network construction method driven by geomechanical characteristics in this invention embodiment, the preset development requirement indicators include, but are not limited to, at least one of: desired reservoir stimulation volume, single-well production capacity target, cost constraints, and construction risk threshold. Further, based on the fracture controllability index and the preset development requirement indicators, a target fracture network structure is defined, with fracture network geometry and conductivity as quantifiable objectives, including:

[0117] A quantitative objective is defined for the geometry of the fracture network, wherein the quantitative objective includes at least one of the main fracture propagation direction, branch fracture density, and effective fracture propagation range;

[0118] Define a quantitative target for the flow guidance capability, wherein the quantitative target includes at least one of the inter-cluster connectivity index and the proppant reachability region;

[0119] The effective propagation range of the crack is defined by the combined distribution of the length of the main crack and the length of the branch crack; the proppant accessibility area is quantified by the proppant distal coverage rate or distal accessibility index.

[0120] This embodiment has achieved a fundamental technological advancement in the target setting stage, transforming the abstract concept of "good fracturing effect" into a quantifiable, simulable, and verifiable "engineering blueprint"—the target fracture network structure (T-FNM).

[0121] This innovative method uses the Fracture Controllability Index (CFI) as the decision-making basis, specifically decomposing development requirements into two dimensions: "geometric morphology" and "conduction capacity," with refined quantitative targets (such as branch density and proppant accessibility). This achieves a historic shift in fracturing design from experience-based, vague "parameter adjustment" to a clear, goal-driven model based on geomechanical understanding. This transformation provides clear guidance and evaluation criteria for subsequent simulation and optimization processes, and is a core decision-making innovation that ensures the final modification results accurately match geological conditions and development needs.

[0122] Furthermore, in the hydraulic fracturing network construction method driven by geomechanical characteristics in this embodiment of the invention, the quantitative target of the target fracture network structure is differentiated based on the fracture controllability index:

[0123] Based on the preset low controllability threshold and high controllability threshold, the numerical range of the crack controllability index is divided into a low controllability interval that is less than or equal to the low controllability threshold, a medium controllability interval that is between the low controllability threshold and the high controllability threshold, and a high controllability interval that is greater than or equal to the high controllability threshold.

[0124] When the crack controllability index is in the low controllability range, the primary goal is to improve the basic connectivity and expansion stability of the crack network, and the inter-cluster connectivity index and the effective expansion range of the crack are set preferentially.

[0125] When the crack controllability index is in the high controllability range, the primary goal is to optimize proppant placement efficiency and improve overall flowability, and the proppant accessibility area and branch crack density are preferentially set.

[0126] When the fracture controllability index (CFI) is within the medium controllability range, the system employs a weighted combination and balanced optimization strategy. The core of this strategy is to dynamically weight and integrate the objectives of "basic connectivity stability" and "layout flow efficiency" based on the position of the CFI value within the range. For example, if the CFI value is close to the low controllability threshold, a more robust strategy for the low controllability range is adopted; if it is close to the high controllability threshold, a more enhanced strategy for the high controllability range is adopted. This allows for a smooth design transition, precisely adapting to the "transitional state" characteristics of the reservoir.

[0127] This embodiment clearly classifies reservoirs into three controllability levels—low, medium, and high—by pre-setting controllability thresholds, matching distinctly different core design objectives to reservoirs with different geological endowments. For low-controllability reservoirs, the objective focuses on establishing stable and controllable basic connectivity channels, prioritizing the avoidance of fracture runaway risks caused by weak surface development. For high-controllability reservoirs, the objective aims to maximize the effective proppant placement and conductivity, fully releasing their potential to form complex networks. An adaptively adjustable fracturing design objective decision logic based on fracture controllability index (CFI) zoning has been established, achieving a leap from "static general design" to "dynamic precise customization."

[0128] In the hydraulic fracturing network construction method driven by geomechanical characteristics in this embodiment, the construction and simulation process of the numerical model considers the following physical mechanisms:

[0129] Crack tip propagation criteria are used to determine crack initiation and dynamic propagation.

[0130] Inter-cluster stress disturbance is used to simulate the competitive propagation behavior among multiple crack clusters;

[0131] The mechanism of proppant retention point formation is used to predict proppant migration and local accumulation in fracture networks;

[0132] Sandbank stability is used to assess the proppant placement pattern in cracks and its long-term conductivity.

[0133] The numerical simulation of the co-evolution of crack propagation and proppant migration is achieved by using coupled solution or strong sequential iteration within the framework of the numerical model to enable the interaction and dynamic feedback of four physical mechanisms: crack tip propagation criteria, inter-cluster stress interference, proppant retention point formation mechanism, and sandbank stability.

[0134] The output of the co-evolutionary numerical simulation can quantitatively characterize at least one of the following engineering indicators:

[0135] The non-uniform propagation length and spatial morphology of cracks in each cluster affected by inter-cluster stress interference;

[0136] The migration front and local concentration distribution of proppant in the fracture network controlled by the proppant retention point formation mechanism;

[0137] The effective support area and flow conduction capacity profile of the final proppant affected by the stability of the sand embankment.

[0138] The numerical model for the dynamic coupling of the mass field, fluid flow field, and proppant particle field described in this embodiment refers to a numerical simulation framework based on multiphysics coupling theory, capable of simultaneously simulating rock fracture, fracturing fluid flow, and proppant migration. Its construction and solution process includes, but is not limited to, the following steps:

[0139] Geomechanical field modeling: Based on the lithological mechanical parameter field, geostress tensor field and weak surface property field in the reservoir geological characteristic model, constitutive models (such as linear elastic, elastoplastic or damage models) are used to describe the rock mechanical behavior, and fracture mechanics criteria (such as maximum circumferential stress criterion and energy release rate criterion) are used to determine fracture initiation and propagation.

[0140] Fluid flow field modeling: Based on the Navier-Stokes equations or their simplified forms in porous media / fractures (such as Darcy's law and Forchheimer's equations), the flow, filtration, and pressure transmission processes of fracturing fluids in fractures and reservoirs are described. Fluid properties (such as viscosity and density) can vary with temperature, pressure, and proppant concentration.

[0141] Proppant particle field modeling: Using particle dynamics methods (such as discrete element method DEM) or a continuous medium model based on concentration transport, the migration, sedimentation, aggregation and sand dam formation of proppant in fracturing fluid are simulated, taking into account mechanisms such as particle-fluid interaction and particle-fracture wall friction.

[0142] Inter-field coupling mechanism:

[0143] Mechanics-flow coupling: Crack width is affected by fluid pressure, which in turn affects the flow path;

[0144] Flow-particle coupling: fluid velocity affects proppant transport, and proppant concentration affects fluid viscosity and density;

[0145] Particle-mechanical coupling: proppant deposition affects fracture conductivity and closure stress.

[0146] Numerical solution methods: Spatial discretization can be performed using the finite element method, finite volume method, discrete element method, or a hybrid method thereof, followed by implicit or explicit time integration for transient solutions. Inter-field coupling can be achieved through strong coupling (monolithic) or staggered iteration strategies.

[0147] Available tools and platforms: This model can be implemented in existing commercial fracturing simulation software (such as GOHFER, StimPlan, ResFrac, Kinetix, etc.) through corresponding module configuration, or it can be built based on open source or multiphysics simulation platforms (such as COMSOL, OpenFOAM, FLAC3D, etc.).

[0148] It should be noted that this embodiment is not limited to a specific numerical software or code implementation. Its core lies in providing a methodological framework for fracturing design based on geomechanical characteristics, guided by the target fracture network structure, and achieved through multi-field coupled simulation and inversion optimization. Those skilled in the art can select or develop appropriate numerical tools for implementation based on the above modeling principles, combined with specific reservoir data and engineering requirements.

[0149] In this embodiment of the hydraulic fracturing network construction method driven by geomechanical characteristics, the inversion optimization is achieved by constructing and solving an inversion problem with the quantified target of the target fracture network structure as the expected value and the simulation results as the predicted value, including:

[0150] Construct an objective function to quantify the comprehensive deviation between the crack morphology and proppant distribution predicted by the co-evolution numerical simulation and the quantification objective of the geometric morphology and flow carrying capacity of the target crack network structure;

[0151] Using injection displacement, fluid viscosity, sand addition procedure, and inter-cluster fracture initiation timing as optimization variables, an optimization algorithm is used to iteratively adjust the optimization variables to minimize the objective function, thereby obtaining the optimal injection parameter sequence.

[0152] The objective function is a multi-objective weighted function, which includes at least one of the following four sub-objective items: crack connectivity deviation, branch density deviation, proppant coverage deviation, and flow conduction capacity deviation.

[0153] The inversion optimization in this embodiment, with the preset target crack network structure as the "navigation endpoint" intelligent inversion optimization mechanism, realizes a paradigm shift in construction parameter design from "forward trial simulation" to "reverse precise matching".

[0154] Goal-driven inverse problem-solving transforms the traditional forward simulation of "given parameters, predicting results" into an inverse optimization problem of "given ideal results (target network), finding the optimal parameters in reverse." This makes fracturing design a precise goal-achieving process, rather than a parametric experiment with uncontrollable results.

[0155] Multi-dimensional integrated cost function: The constructed objective function unifies and quantifies multiple engineering objectives, including crack morphology (connectivity, density) and flow conduction performance (proppane coverage, flow capacity), and performs a comprehensive trade-off through weighted summation. This enables the optimization solution to simultaneously consider network complexity and effective support, overcoming the one-sidedness of single-objective optimization.

[0156] A global optimization closed loop is formed: This inversion optimization is closely integrated with the aforementioned reservoir geological characteristic model, target fracture network structure (T-FNM), and high-fidelity simulation, forming a fully intelligent closed loop of "geological evaluation - target setting - simulation verification - parameter inversion". The system can automatically and efficiently search for the optimal construction scheme that best approximates the design blueprint within the preset tolerance range in the multi-dimensional parameter space (displacement, viscosity, sand addition program, etc.), thereby directly transforming geological knowledge and engineering wisdom into executable optimal operation instructions, greatly improving the scientific nature, efficiency, and reliability of the design.

[0157] Specifically, in the multi-objective weighting function, the weight coefficients of each sub-objective item are dynamically configured according to the crack controllability index; wherein, when the crack controllability index is low, the weight of the crack connectivity deviation sub-objective item is increased; when the crack controllability index is high, the weights of the proppant coverage deviation and flow conduction capacity deviation sub-objective items are increased.

[0158] Specifically, the parameter combinations in the optimal injection parameter sequence constitute a control strategy to enhance the controllability of the fracture. The control strategy includes at least one of segmented variable displacement, clustered delayed start-up, fluid viscosity step change, and multi-stage sand addition procedure.

[0159] The ultimate goal of the inversion optimization is to maximize the reservoir stimulation volume, proppant far-end coverage, and network connectivity while satisfying the constraints of the target fracture network structure; the reservoir stimulation volume is calculated from the fracture network geometry predicted by the co-evolution numerical simulation.

[0160] The optimization algorithm in this embodiment is an intelligent optimization algorithm that can automatically search for global or local optimal solutions that satisfy constraints in a multidimensional parameter space.

[0161] This embodiment also provides a hydraulic fracturing network construction system driven by geomechanical characteristics, including:

[0162] The geological feature modeling module is used to establish reservoir geological feature models that characterize the heterogeneity of the target reservoir based on multi-source geological data.

[0163] The controllability evaluation module is used to calculate the fracture controllability index based on the reservoir geological characteristic model, so as to quantitatively evaluate the predictability and controllability of fractures in the target reservoir.

[0164] The target network construction module is used to define a target crack network structure with crack network geometry and flow conduction capacity as quantitative targets, based on the crack controllability index and preset development requirement indicators.

[0165] The co-evolution simulation module is used to construct a numerical model that dynamically couples the geomechanical field, fluid flow field, and proppant particle field based on the reservoir geological feature model and the target fracture network structure. It performs co-evolution numerical simulation of the fracture propagation process and the proppant migration process to predict the fracture morphology and proppant distribution under different injection parameters.

[0166] The inversion optimization module is used to perform inversion optimization on the results of the co-evolution numerical simulation with the target crack network structure as the desired output and the injection parameters as the optimization variables. Through iterative optimization, the optimal injection parameter sequence that makes the simulation results approximate the target crack network structure within a preset tolerance range is obtained.

[0167] This invention discloses a method and system for constructing hydraulic fracturing networks based on geomechanical characteristics. By introducing a geological feature-driven mechanism, it achieves proactive design of fracture networks in ultra-large fracturing reservoirs. The proposed Fracture Controllability Index (CFI) and Target Fracture Network Structure (T-FNM) significantly improve fracture network connectivity and reduce the impact of stress interference. Furthermore, through collaborative simulation to optimize proppant placement, it enhances fracture conductivity and provides more comprehensive distal coverage. In addition, injection strategy inversion can proactively control fracture morphology, preventing over-expansion or early closure, thereby improving the consistency of fracturing operations and reservoir stimulation efficiency.

[0168] Example 1: Fracture network design based on low stress difference reservoirs.

[0169] This embodiment focuses on a super-large tight oil reservoir. Regional in-situ stress testing results show that the difference between the maximum and minimum horizontal principal stresses is only 3.1 MPa, classifying it as a typical low-stress-difference reservoir. The formation exhibits a complete internal structure, strong lateral continuity, and sparse, mostly closed natural fractures. Based on reservoir structure characteristics obtained from core triaxial experiments, imaging logging, and geostatistical model inversion, the calculated fracture controllability index (CFI) is 0.82, indicating that the reservoir possesses strong fracture controllability, relatively stable fracture propagation directions, and that branch density and connectivity can be directly controlled through injection parameters.

[0170] Based on this, and following the construction principle of the "Target Crack Network Structure (T-FNM)" proposed in this invention, the target network is set as follows:

[0171] (1) The main crack direction is stable and the length of the crack is 160-210m;

[0172] (2) Forming a dense branched crack structure with a branch length of 10-25m and a branch density of no less than 8-12 cracks / 100m;

[0173] (3) The flow channels between clusters should be kept balanced to avoid cluster expansion caused by excessive stress shadows;

[0174] (4) The far reachability (FRAI) of the proppant is improved by more than 25%.

[0175] Based on the target fracture network structure (T-FNM) requirements, this embodiment employs a high-displacement-medium-viscosity system (16-18 m³ / min and 45-70 mPa•s viscosity range) to enhance the dynamic driving capability along the main fracture direction and stabilize the generation of branch fractures. Simulations of the fracture-proppane co-evolution revealed that the number of branch fractures was insufficient when the displacement was below 14 m³ / min, and the proppant struggled to maintain high sand pile stability when the viscosity was below 40 mPa•s. Therefore, the final injection system was determined to be a high-displacement + medium-viscosity combination.

[0176] After completing the numerical simulation of the fracture network, the sand addition procedure was solved using an inversion optimization module based on the proppant deposition morphology, sand pile front advancement velocity, and sand loss risk calculation. The optimization results show that the optimal sand addition sequence is:

[0177] Pre-treatment: 10 min of clear liquid cleaning → staged sand addition of 120-350 kg / m³ → final sand flushing with 80 kg / m³ uniform sand.

[0178] The optimal inter-cluster splitting interval is 8-12 seconds;

[0179] The proppant ultimately improves distal coverage by 31.4% compared to traditional designs.

[0180] Construction simulation results show that the reservoir can achieve balanced inter-cluster flow splitting and dense branch fracture generation under a high displacement-medium viscosity system, and the proppant distal placement is significantly enhanced, meeting the design requirements of the target fracture network structure.

[0181] Example 2: Design of controllable fracture network based on strongly heterogeneous reservoir.

[0182] This example focuses on a highly heterogeneous conglomerate reservoir with significant lithological variations, including localized gravel content as high as 35-50%. Weak surfaces exhibit uneven density and marked directional changes. Regional stress testing results show lateral disturbances of 18°-25° along the principal stress direction. The reservoir's geological characteristic model reveals poor continuity and a complex distribution of weak surfaces, leading to easy deflection and irregular propagation of fracture paths. The reservoir's fracture controllability index (CFI) is evaluated at 0.37, classifying it as a typical low-controllability reservoir.

[0183] According to the target fracture network structure (T-FNM) construction method proposed in this invention, a strategy of strengthening "main fracture channel constraint + branch density regulation + sand pile stability control" needs to be adopted in low-controllability reservoirs. Therefore, the target fracture network structure is set as follows:

[0184] (1) Determine the direction of the main crack channel and limit the maximum deflection angle to no more than 15°;

[0185] (2) The density of branch cracks is low, but it is necessary to ensure that diversion channels are formed in key parts;

[0186] (3) There are many natural weak points, which can easily lead to the collapse of sand piles. Therefore, it is necessary to set up a stable zone for sand piles and improve the stability index of sand piles.

[0187] (4) Control the non-equivalent diversion to avoid premature leakage of cracks in the weak area.

[0188] To enhance crack controllability, this embodiment employs a segmented variable displacement strategy (8-12-16-12 m³ / min) and a fluid viscosity gradient system (35-90-50 mPa•s). The key mechanisms include:

[0189] An initial low discharge rate (8 m³ / min) is used to stabilize the crack initiation direction;

[0190] The high-displacement section (16 m³ / min) is used to traverse high-resistivity sections and improve crack penetration capability;

[0191] The final stage of the drop-off displacement (12m³ / min) avoids instability of the sand pile and reduces the risk of weak surface fracture.

[0192] A numerical model dynamically coupling the geomechanical field, fluid flow field, and proppant particle field was used to predict the behavior of fractures and proppant throughout the entire process. The results show that:

[0193] If a constant discharge rate is used, the crack deflection exceeds 22°, and the conduction between clusters is unbalanced;

[0194] Under the variable displacement strategy, the deflection angle can be controlled within 14°;

[0195] The stability index of the sand pile increased by 47%;

[0196] The coverage of distal propionate increased by 19%.

[0197] The optimal sand-addition procedure was finally obtained through the inversion optimization module:

[0198] “Low concentration at the beginning (80 kg / m³) → high concentration in the middle stage (260-320 kg / m³) → low concentration in the stable stage (120 kg / m³)”.

[0199] This procedure enables sand piles to remain stable and advance without collapsing near weak surfaces, significantly improving the connectivity and flow conduction capacity of the fracture network.

[0200] This embodiment verifies that in strongly heterogeneous reservoirs, a joint optimization strategy of "Controllability Index (CFI) classification + target fracture network structure (T-FNM) target constraint + segmented variable displacement + sand pile stability control" can significantly improve the controllability and stimulation effect of fracture network.

[0201] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described herein. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or equivalent substitutions to the present invention, as well as all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.

Claims

1. A method for constructing hydraulic fracturing networks based on geomechanical characteristics, characterized in that, include: Based on multi-source geological data of the target reservoir, the multi-source geological data includes at least data reflecting lithological structure, natural fracture system, intra-layer weak surface type, regional stress distribution, porosity and permeability parameters and geological heterogeneity, and a reservoir geological characteristic model characterizing its heterogeneity is established. Based on the reservoir geological characteristic model, the fracture controllability index is calculated to quantitatively evaluate the predictability and controllability of fractures in the target reservoir. Based on the crack controllability index and the preset development requirement indicators, a target crack network structure is defined with crack network geometry and flow conduction capacity as quantitative objectives. Based on the reservoir geological feature model and the target fracture network structure, a numerical model dynamically coupled with the geomechanical field, fluid flow field and proppant particle field is constructed to perform numerical simulation of the co-evolution of fracture propagation process and proppant migration process, and to predict fracture morphology and proppant distribution under different injection parameters. Using the target fracture network structure as the desired output and the injection parameters as optimization variables, the results of the co-evolution numerical simulation are inverted and optimized. The optimal injection parameter sequence that makes the simulation results approximate the target fracture network structure within a preset tolerance range is obtained through iterative optimization. The inversion optimization is achieved by constructing and solving an inversion problem with the quantized target of the target crack network structure as the expected value and the simulation results as the predicted value, including: Construct an objective function to quantify the comprehensive deviation between the crack morphology and proppant distribution predicted by the co-evolution numerical simulation and the quantification objective of the geometric morphology and flow carrying capacity of the target crack network structure; Using injection displacement, fluid viscosity, sand addition procedure, and inter-cluster fracture initiation timing as optimization variables, an optimization algorithm is used to iteratively adjust the optimization variables to minimize the objective function, thereby obtaining the optimal injection parameter sequence. The objective function is a multi-objective weighted function, which includes at least one of the following four sub-objective items: crack connectivity deviation, branch density deviation, proppant coverage deviation, and flow conduction capacity deviation.

2. The method for constructing a hydraulic fracturing network based on geomechanical characteristics according to claim 1, characterized in that, The reservoir geological characteristic model, based on multi-source geological data of the target reservoir, is established to characterize its heterogeneity, including: Collect and process multi-source geological data of the target reservoir; Based on the multi-source geological data, a unified, three-dimensional spatially distributed reservoir geological characteristic model is generated through geological modeling and data fusion methods. This model integrates lithological mechanical properties, natural fracture network, weak surface distribution, geostress field, and physical property parameter field.

3. The method for constructing a hydraulic fracturing network based on geomechanical characteristics according to claim 2, characterized in that, The calculation of the fracture controllability index based on the reservoir geological characteristic model includes: Based on the reservoir geological characteristic model, several predefined geomechanical evaluation indicators are extracted; The multiple geomechanical evaluation indicators are normalized and weighted and summed according to their respective contributions to the controllability of fracture propagation to calculate a fracture controllability index between 0 and 1. The higher the value of the fracture controllability index, the stronger the predictability and controllability of the fracture in the target reservoir. The predefined geomechanical evaluation indicators include at least three of the following: lithological strength difference, natural fracture density, stress gradient, weak surface distribution density, and reservoir continuity index.

4. The method for constructing a hydraulic fracturing network based on geomechanical characteristics according to claim 1, characterized in that, Based on the crack controllability index and preset development requirement indicators, a target crack network structure is defined with crack network geometry and flow conduction capacity as quantitative objectives, including: A quantitative objective is defined for the geometry of the fracture network, wherein the quantitative objective includes at least one of the main fracture propagation direction, branch fracture density, and effective fracture propagation range; Define a quantitative target for the flow guidance capability, wherein the quantitative target includes at least one of the inter-cluster connectivity index and the proppant reachability region; The effective propagation range of the crack is defined by the combined distribution of the length of the main crack and the length of the branch crack; the proppant accessibility area is quantified by the proppant distal coverage rate or distal accessibility index.

5. The method for constructing a hydraulic fracturing network based on geomechanical characteristics according to claim 4, characterized in that, Based on the crack controllability index, the quantification target of the target crack network structure is set differently: Based on the preset low controllability threshold and high controllability threshold, the numerical range of the crack controllability index is divided into a low controllability interval that is less than or equal to the low controllability threshold, a medium controllability interval that is between the low controllability threshold and the high controllability threshold, and a high controllability interval that is greater than or equal to the high controllability threshold. When the crack controllability index is in the low controllability range, the primary goal is to improve the basic connectivity and expansion stability of the crack network, and the inter-cluster connectivity index and the effective expansion range of the crack are set first. When the crack controllability index is in the high controllability range, the primary goal is to optimize proppant placement efficiency and improve overall flowability, and the proppant accessibility area and branch crack density are preferentially set.

6. The method for constructing a hydraulic fracturing network based on geomechanical characteristics according to claim 1, characterized in that, The construction and simulation process of the numerical model considers the following physical mechanisms: Crack tip propagation criteria are used to determine crack initiation and dynamic propagation. Inter-cluster stress disturbance is used to simulate the competitive propagation behavior among multiple crack clusters; The mechanism of proppant retention point formation is used to predict proppant migration and local accumulation in fracture networks; Sandbank stability is used to assess the proppant placement pattern and long-term conductivity in cracks. The numerical simulation of the co-evolution of crack propagation and proppant migration is achieved by using coupled solution or strong sequential iteration within the framework of the numerical model to enable the interaction and dynamic feedback of four physical mechanisms: crack tip propagation criteria, inter-cluster stress interference, proppant retention point formation mechanism, and sandbank stability. The output of the co-evolutionary numerical simulation can quantitatively characterize at least one of the following engineering indicators: The non-uniform propagation length and spatial morphology of cracks in each cluster affected by inter-cluster stress interference; The migration front and local concentration distribution of proppant in the fracture network controlled by the proppant retention point formation mechanism; The effective support area and flow conduction capacity profile of the final proppant affected by the stability of the sand embankment.

7. The method for constructing a hydraulic fracturing network based on geomechanical characteristics according to claim 1, characterized in that, In the multi-objective weighting function, the weight coefficients of each sub-objective item are dynamically configured according to the crack controllability index; wherein, when the crack controllability index is low, the weight of the crack connectivity deviation sub-objective item is increased; when the crack controllability index is high, the weights of the proppant coverage deviation and flow conduction capacity deviation sub-objective items are increased.

8. The method for constructing a hydraulic fracturing network based on geomechanical characteristics according to claim 1, characterized in that, The parameter combinations in the optimal injection parameter sequence constitute a control strategy to enhance the controllability of the fracture. The control strategy includes at least one of segmented variable displacement, clustered delayed start-up, fluid viscosity step change and multi-stage sand addition procedure. The ultimate goal of the inversion optimization is to maximize the reservoir stimulation volume, proppant far-end coverage, and network connectivity while satisfying the constraints of the target fracture network structure; the reservoir stimulation volume is calculated from the fracture network geometry predicted by the co-evolution numerical simulation.

9. A hydraulic fracturing network construction system driven by geomechanical characteristics, characterized in that, include: The geological feature modeling module is used to establish a reservoir geological feature model characterizing its heterogeneity based on multi-source geological data of the target reservoir. The multi-source geological data includes at least data reflecting lithological structure, natural fracture system, intra-layer weak surface type, regional stress distribution, porosity and permeability parameters and geological heterogeneity. The controllability evaluation module is used to calculate the fracture controllability index based on the reservoir geological characteristic model, so as to quantitatively evaluate the predictability and controllability of fractures in the target reservoir. The target network construction module is used to define a target crack network structure with crack network geometry and flow conduction capacity as quantitative targets, based on the crack controllability index and preset development requirement indicators. The co-evolution simulation module is used to construct a numerical model that dynamically couples the geomechanical field, fluid flow field, and proppant particle field based on the reservoir geological feature model and the target fracture network structure. It performs co-evolution numerical simulation of the fracture propagation process and the proppant migration process to predict the fracture morphology and proppant distribution under different injection parameters. The inversion optimization module is used to invert and optimize the results of the co-evolution numerical simulation with the target crack network structure as the desired output and the injection parameters as the optimization variables. Through iterative optimization, the optimal injection parameter sequence that makes the simulation results approximate the target crack network structure within a preset tolerance range is obtained. The inversion optimization is achieved by constructing and solving an inversion problem with the quantized target of the target crack network structure as the expected value and the simulation results as the predicted value, including: Construct an objective function to quantify the comprehensive deviation between the crack morphology and proppant distribution predicted by the co-evolution numerical simulation and the quantification objective of the geometric morphology and flow carrying capacity of the target crack network structure; Using injection displacement, fluid viscosity, sand addition procedure, and inter-cluster fracture initiation timing as optimization variables, an optimization algorithm is used to iteratively adjust the optimization variables to minimize the objective function, thereby obtaining the optimal injection parameter sequence. The objective function is a multi-objective weighted function, which includes at least one of the following four sub-objective items: crack connectivity deviation, branch density deviation, proppant coverage deviation, and flow conduction capacity deviation.

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