A fracturing channeling quantitative evaluation method based on multi-physical field coupling and pressure response characteristics

Abstract

This invention discloses a quantitative evaluation method for fracturing crosstalk based on multi-physics coupling and pressure response characteristics, relating to the field of unconventional oil and gas development technology. Addressing the problem that traditional fracturing crosstalk evaluation methods are prone to false crosstalk misjudgments due to the disruption of fluid-structure interaction, this method first extracts the inter-well pressure response time and rise; constructs a dynamic model of fluid crosstalk channel conductivity considering proppant viscosity correction, and introduces its first-order time derivative as a dynamic early warning indicator; utilizes well logging geological parameters to adaptively map formation dissipation and stress gain coefficients to establish a comprehensive reservoir stimulation efficiency model based on multi-physics coupling; and achieves real-time determination of stimulation modes based on a multi-dimensional threshold matrix, automatically triggering closed-loop quantitative intervention when malignant crosstalk is detected, calculating the amount of temporary plugging agent added and the reduction in discharge. This invention achieves a leap from qualitative analysis to quantitative real-time closed-loop control of fracturing crosstalk, significantly improving the efficiency and profitability of unconventional reservoir stimulation.

Description

Technical Field

[0001] This invention relates to the field of unconventional oil and gas field development and hydraulic fracturing engineering technology, specifically to a method for quantitatively evaluating the degree of fracturing crosstalk and dynamically optimizing reservoir stimulation efficiency by utilizing inter-well pressure response characteristics, fluid dynamic parameters, and geostress field evolution laws. Background Technology

[0002] In the large-scale development of unconventional oil and gas resources such as shale oil and gas and tight oil reservoirs, multi-well platform "zipper-style" fracturing has become the core technology for increasing reservoir stimulation volume (SRV), improving single-well production, and realizing commercial exploitation. In recent years, as unconventional oil and gas development has gradually moved towards deep, ultra-deep and complex structural areas, in order to maximize ultimate recovery rate (EUR) and development benefits, well density has gradually increased, and well spacing and cluster spacing have been continuously reduced [Zhao Jinzhou, Yong Rui, Hu Dongfeng, et al. Deep-ultra-deep shale gas fracturing in China: problems, challenges and development direction [J]. Acta Petrolei Sinica, 2024, 45(1):295-311.]. However, due to the development of natural weak surfaces and strong heterogeneity in unconventional reservoirs, and their long-term exposure to complex high-pressure geostress environment, during the process of dense well layout and large-scale hydraulic fracturing, high-pressure fracturing fluid and proppant are very likely to enter adjacent wells along artificial hydraulic fractures or activated natural bedding planes, resulting in serious inter-well interference [Wang Qiang, Wang Yufeng, Sun Ying et al. Zipper-type fracturing fracture flow law in deep shale gas wells under the influence of small faults [J]. Natural Gas Geoscience, 2025, 36(2): 342-353.].

[0003] If severe crossflow occurs after fracturing, it will lead to a series of catastrophic engineering and economic consequences. On the one hand, a large amount of fracturing fluid will leak into the existing high-speed channels, causing the reservoir of the activated well (post-killed well) to be ineffectively and repeatedly stimulated. This not only leads to a significant reduction in the effective fracture length and severe dissipation of formation energy [Wang Qiang, Yang Yu, Song Yi, et al. Shale gas zipper-type fracturing interference model affected by natural fracture-fault zone [J]. Chinese Journal of Rock Mechanics and Engineering, 2025, 44(201): 146-157.], but also greatly weakens the production enhancement effect of the well. On the other hand, the overflow of high-pressure fluid and strong proppant can easily cause abnormal surges in the wellhead pressure of the response well (pre-killed well), leading to serious accidents such as casing deformation, wellbore equipment damage, and proppant backflow, which greatly affects the overall oil and gas production capacity and economic life of the entire multi-well platform [Duan Guifu, Mou Jianye, Yan Xiaolun, et al. Main controlling factors and induction mechanism of fracturing interference in horizontal wells of deep shale gas in southern Sichuan [J]. China Petroleum Exploration, 2024, 29(3): [146-158.]. Therefore, real-time monitoring of parameters to determine the degree of crosstalk and dynamically optimizing the construction strategy during fracturing is a key bottleneck for the efficient and safe extraction of unconventional oil and gas.

[0004] Currently, numerous explorations and studies have been conducted both domestically and internationally regarding the monitoring and evaluation of hydraulic fracturing crossflow. Existing high-end monitoring methods largely rely on microseismic monitoring or distributed optical fiber sensing (DAS / DTS) monitoring. However, microseismic monitoring often only reflects the macroscopic envelope of rock fractures, making it difficult to accurately quantify the specific scale of fluid crossflow. While optical fiber monitoring technology offers high precision, it suffers from limitations such as complex downhole processes, low equipment survival rates under harsh conditions, and extremely high costs. Furthermore, the massive data processing of both technologies is typically severely delayed, making it difficult to provide real-time feedback and guidance in the critical fracturing environment [Liu Jiangbo, Wang Shangwei, Yang Yixing, et al. Progress in the Application of Optical Fiber Sensing Technology in Hydraulic Fracturing of Unconventional Gas Reservoirs [C] The 2nd China Natural Gas Development Technology Annual Conference. 2023.]. In light of this, some scholars and field engineers have attempted to use the relatively low-cost and easily accessible response well pressure changes for qualitative analysis of inter-well interference, generally believing that "an increase in pressure in adjacent wells indicates the occurrence of crossflow." However, most existing pressure assessment methods only scratch the surface, relying on simple static pressure difference comparisons or simple fracture length ratios [Wang Qiang, Wang Yufeng, Hu Yongquan, et al. Fracture propagation and cross-connection laws in deep shale gas wells using zipper-type fracturing [J]. Petroleum Exploration and Development, 2024, 51(5): 1141-1149.], severely lacking a dynamic early warning mechanism for the transient time derivative of channel conductivity. This assessment system has serious defects: it severely severs the complex multi-physics coupling relationship between the hydrodynamics of sand-bearing fracturing fluids (such as the influence of flow rate, pure fluid viscosity, and proppant phase volume concentration on the nonlinear frictional resistance of flow) and rock geomechanics (such as the fracture network deflection caused by geostress shadowing effect and the differentiated influence of logging heterogeneous properties on energy dissipation). Due to the lack of in-depth multi-field coupling, this static, single-dimensional assessment method is prone to "false cross-connection" misjudgments in the field. Due to the lack of in-depth multi-field coupling, this static, one-dimensional evaluation method easily leads to misjudgments of "false crosstalk" in the field. For example, the stress shadow effect of the response well forces the fractures in the excitation well to become tortuous and bifurcated. This is a "benign stress-induced gain" that can greatly increase the volume of complex fracture networks, but the resulting pore elastic waves can also cause an increase in pressure in the response well. If traditional methods are used, it is easy to confuse this with "malignant substantive fluid crosstalk," leading to erroneous decisions such as blindly reducing the discharge rate and prematurely stopping the pump. This not only misses the best opportunity for stimulation but also results in a huge waste of fracturing materials.

[0005] To date, the industry has yet to find a mathematical model that can comprehensively integrate formation energy dissipation mechanisms, the physical spatial conduction of non-Newtonian sand-bearing fluids, adaptive mapping of complex geological properties in well logging, and the dynamic evolution of the geostress field, and directly apply it to the dynamic quantitative evaluation and automated quantitative closed-loop intervention parameter optimization in fracturing operations. To accurately predict and analyze the microscopic physical nature of inter-well interference during fracturing, break through the technical barriers of single-parameter qualitative evaluation, and fill the gap in fully coupled dynamic evaluation technology for complex fracture networks, a quantitative evaluation method adapted to these complex geological engineering conditions is urgently needed. This invention aims to achieve a historic leap from "qualitative post-hoc analysis" to "quantitative real-time closed-loop control" of fracturing crosstalk, thereby providing theoretical support for the efficient and intelligent development of deep and unconventional oil and gas resources. Summary of the Invention

[0006] This invention aims to overcome the shortcomings of existing technologies by proposing a quantitative evaluation method for fracturing channeling based on multi-physics coupling and pressure response characteristics. This method considers non-Newtonian fluid conduction, proppant resistance, formation fracturing energy dissipation mechanisms, well logging geological properties, and formation stress deflection characteristics during fracturing. Based on seepage mechanics, rock mechanics, and multi-field coupling theory, a comprehensive evaluation model suitable for zipper fracturing is established. Using this model, the strength of the channeling physical pathways and the actual efficiency of reservoir stimulation can be predicted in real time based on construction and geological parameters, and a quantitative closed-loop intervention strategy can be automatically calculated.

[0007] The technical solution provided by this invention to solve the above-mentioned technical problems is: a quantitative evaluation method for fracturing crosstalk based on multi-physics coupling and pressure response characteristics, comprising the following steps: S1. Based on the geological and engineering characteristics of the target reservoir, obtain the basic parameters in the zipper fracturing process and determine the initial geological conditions and construction boundary conditions; S2. Based on the inter-well pressure monitoring system, the wellhead pressure data of the response well is collected in real time during the fracturing operation. Combined with the fracturing operation time axis of the excitation well, the pressure response time and the pressure increase of the response well are extracted and calculated. S3. Based on Darcy's law of seepage and the theory of non-Newtonian fluid dynamics in sand-containing fluids, a fluid channel conductivity considering the volume concentration of the proppant phase is constructed ( C D A dynamic calculation model is used, and the first time derivative of the transmissibility is introduced as a dynamic early warning indicator. S4. Based on rock mechanics theory and energy dissipation principle, by dynamically decoupling the formation dissipation coefficient and fracture network complexity gain coefficient through logging parameters, and coupling the stress shadowing effect generated after well fracturing and the energy penalty caused by fluid channeling, a comprehensive reservoir stimulation efficiency is established. or c Evaluation model; S5. Based on the calculated fluid channel conductivity and its time derivative, and the overall efficiency of reservoir stimulation, the automated quantitative closed-loop control and dynamic solution of the fracturing strategy are realized using a multidimensional threshold matrix and intervention control model.

[0008] A further technical solution is that the specific process of step S1 is as follows: during the zipper-type fracturing operation, the construction displacement of the excitation well is obtained in real time. Q Viscosity of fracturing fluid system Total fracturing time in the current stage T frac Simultaneously, the initial formation fracture pressure of the response well was obtained using a high-frequency pressure gauge at the wellhead. P f and real-time pressure monitoring curve P ( t ).

[0009] A further technical solution is that the specific process of step S2 is as follows: Time-frequency domain analysis was performed on the response well pressure curve to extract the initial pressure rise time and the fracturing termination time, and the pressure response time was calculated respectively. T res This refers to the difference between the moment when the pressure in the response well begins to rise and the moment when fracturing begins in the excitation well; the pressure increase in the response well. That is, the difference between the pressure at the end of the well fracturing and the pressure at the initial pressure rise: ; ; in, T res The pressure response time is in minutes. T initial To respond to the initial rise in well pressure, min; T strat The time (min) is the start time of fracturing in the excited well. In response to the increase in well pressure, MPa; P initial The pressure value at the moment of response to well pressure rise, in MPa; P end The pressure value, in MPa, corresponds to the moment when fracturing of the excitation well ends.

[0010] A further technical solution is that the specific process of step S3 is as follows: Real-time proppant volume concentration in the carrier fluid of the excitation well is obtained, and a proppant viscosity correction factor is constructed: ; in, f ( C propSupport phase viscosity correction factor, dimensionless; C prop The volume concentration of the proppant in the carrier fluid is dimensionless. The maximum close-packed volume fraction of the proppant is dimensionless. The intrinsic viscosity factor is dimensionless. The viscosity correction factor is introduced into the fluid conductivity model to obtain the corrected conductivity of the inter-well crossflow channels. C D Dynamic calculation formula: ; in, C D The conductivity of the inter-well crossflow channel is dimensionless. In response to the increase in well pressure, MPa; The viscosity of the fracturing fluid system is expressed in mPa·s. Q The displacement during well construction is expressed in m³ / min. T res The pressure response time is expressed in minutes; within the fracturing operation cycle, the calculated conductivity is monitored in real time. C D Taking the first derivative over time yields the dynamic rate of change of the conductivity; when > ( When the preset critical rate threshold for accelerated channel development is reached, the system can determine that a large-scale malignant fluid crossflow channel between wells is accelerating its development.

[0011] A further technical solution is that the specific process of step S4 is as follows: Obtain well logging geological data of the target formation and extract the natural fracture development density index. I frac With brittleness index B I Using well logging mapping functions to analyze the dimensionless formation dissipation coefficient l Gain coefficient for complexity of stress joint mesh c Perform adaptive correction: ; ; In the formula, The formation dissipation coefficient, after dynamic correction of logging parameters, represents the penalty weight for ineffective energy dissipation and is dimensionless. The gain coefficient for the complexity of the stress joint network, after dynamic correction, represents the weight of the complex joint network caused by the geostress shadow, and is dimensionless. , where is the baseline formation dissipation coefficient, and is the basic dissipation weight calibrated by experimental and historical fitting, dimensionless; The reference stress gain coefficient is the experimentally calibrated base stress deflection gain weight, which is dimensionless. a , b , c It is a dimensionless constant used as a geological adaptive adjustment factor to correlate heterogeneity with dissipation / gain properties; The revised and Substituting these values, we obtain the final equation for the overall efficiency evolution of reservoir stimulation: ; in, or c The overall efficiency of reservoir stimulation is dimensionless. L 1 represents the length of the fracture in the excitation well, in meters (m). L 2 represents the length of the fracture in the response well, in meters. P f The initial formation fracture pressure of the response well is measured in MPa. T frac The total fracturing time for this section of the well is expressed in minutes.

[0012] A further technical solution is that the specific process of step S5 is as follows: the system determines the current construction step length based on the current construction step length. C D , or c as well as Real-time determination of reservoir stimulation modes and execution of closed-loop control strategies: when or c ≥1.05 and ≤ When this is determined to be a stress-induced benign expansion of the seam mesh, the construction discharge rate should be maintained or appropriately increased. When 0.85≤ or c When the value is less than 1.05, it is determined to be in the steady-state micro-interference zone, and the original design displacement is maintained to ensure smooth progress. when or c <0.85 and > The system triggers a real-time quantitative intervention mechanism, automatically calculating the intelligent dosing quality and displacement of the steering temporary plugging agent based on the current efficiency loss, and automatically adjusting the dosage accordingly. ; ; In the formula, M The mass of the temporary plugging agent for intelligent dosing is measured in kg. k The temporary blocking quantitative decision coefficient includes empirical constants for the dimensional conversion equivalents between emission volume, concentration, and mass. To automatically adjust the displacement, m 3 / min; Q design The original design displacement is m³ / min; the system issues a pumping order for the specified mass. M The temporary plugging agent and the construction instructions to reduce the discharge volume, until the requirements are met again. The convergence condition for <0.

[0013] The beneficial effects of this invention are: This method can change the previous reliance on a single pressure difference value for qualitative estimation, by introducing… C D and or c This invention transforms a simple pressure increase into a physically meaningful energy dissipation index. It innovatively introduces a proppant concentration correction factor and a dynamic mapping relationship between well logging geological properties, enabling the model to perfectly match the actual working conditions of sand-bearing fluids and reservoir heterogeneity. Furthermore, it introduces the first-order time derivative of conductivity as a warning threshold, effectively avoiding false crosstalk caused by fluctuations in normal operating conditions. Most importantly, this invention establishes an automated quantitative intervention closed-loop model that can accurately calculate the required reduction in the mass and displacement of temporary plugging agent based on the efficiency deficit difference. All input parameters required for this method can be collected in real-time by conventional surface instruments, eliminating the need for expensive downhole monitoring equipment. This provides a reliable, economical, and efficient analysis and decision-making technology for the stimulation of unconventional oil and gas reservoirs. Attached Figure Description

[0014] Figure 1 Here is a logical flowchart of the model calculation and optimization evaluation method; Figure 2 The transient pressure waveform and characteristic parameter extraction curves of the response well are shown in the figure. Figure 3 Overall efficiency of reservoir stimulation ( or c A surface plot showing the relationship between pressure response time and pressure response time; Figure 4 Inter-well stress disturbance and fracture propagation mode A-cloud map: benign stress disturbance ( or c ≥1.05); Figure 5 Inter-well stress disturbance and fracture propagation mode B-cloud map: Conventional equilibrium zone (0.85≤ or c <1.05); Figure 6 C-cloud map of inter-well stress disturbance and fracture propagation mode: malignant fluid channeling ( or c <0.85); Figure 7Cross-flow channel conductivity ( C D Dynamic evolution response characteristic map. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further illustrated below with reference to the accompanying drawings and embodiments.

[0016] This invention provides a quantitative evaluation method for fracturing crosstalk based on multi-physics coupling and pressure response characteristics. Its core lies in using energy dissipation and fluid conduction dimensionality reduction techniques to handle inter-well interference phenomena, and efficiently embedding it into a dynamic evaluation framework for real-time fracturing operations. The specific implementation steps are as follows: Figure 1 ): S1. Based on the geological and engineering characteristics of the target reservoir, obtain basic fracturing data and real-time monitoring parameters; S2. Based on the inter-well pressure curve, extract the nonlinear mapping pressure response time and the response well pressure increase. S3. Based on Darcy's law of seepage and the theory of non-Newtonian fluid dynamics in sand, a dynamic calculation model of the conductivity of fluid channel considering the volume concentration of the proppant phase is constructed, and the time derivative is introduced as an early warning indicator. S4. By dynamically decoupling the mapping of formation dissipation and stress gain coefficients through logging parameters, a reservoir stimulation comprehensive efficiency evaluation tensor that couples energy dissipation penalty and stress shadow gain is constructed. S5. Based on the multidimensional threshold matrix and intervention control model, realize the automated quantitative closed-loop control and dynamic solution of fracturing strategy.

[0017] In this invention, the specific acquisition requirements for step S1 are as follows: to obtain the geological characteristic parameters and fracturing construction parameters of the existing target unconventional oil and gas reservoir. The specific parameters include: initial formation fracturing pressure, rock elastic modulus, Poisson's ratio, dynamic viscosity of fracturing fluid system, construction displacement, total time of single-stage fracturing, reference value of hydraulic fracture length of response well, hydraulic fracture length of excitation well, formation dissipation coefficient and fracture network complexity coefficient. At the same time, the wellhead of the response well is acquired in real time using a high-frequency pressure gauge.

[0018] In this invention, the specific process of step S2 is as follows: Traditional fracturing interference monitoring typically extracts only a single final pressure difference value. This invention innovatively introduces time-frequency domain feature analysis. First, a first-order derivative analysis is performed on the real-time pressure curve of the response well to accurately capture weak pressure jump singularities. The pressure response time is defined as the difference between the initial pressure jump moment of the response well and the start moment of fracturing in the excitation well. The pressure increase of the response well is defined as the difference between the pressure at the end of fracturing and the pressure at the initial jump moment (feature parameter extraction curves are shown in the figure). Figure 2As shown in the figure, these two parameters not only reflect the apparent inter-well interference, but also serve as the spatiotemporal boundary conditions for subsequently constructing the fluid-structure interaction conductivity. ; ; in, T res For pressure response time; T initial To respond to the initial rise in well pressure, min; T strat The time (min) is the start time of fracturing in the excited well. In response to the increase in well pressure, MPa; P initial The pressure value at the moment of response to well pressure rise, in MPa; P end The pressure value, in MPa, corresponds to the moment when the fracturing of the excitation well ends.

[0019] In this invention, the specific process of step S3 is as follows: In order to quantitatively determine the macroscopic physical scale of inter-well fracture communication, this invention constructs a dynamic calculation model of fluid channel conductivity considering the volume concentration of proppant phase based on Darcy's law of seepage and the theory of non-Newtonian fluid dynamics in sand-bearing fluids; at the same time, the time derivative of conductivity is introduced as a transient dynamic early warning indicator for malignant inter-well flow. First, the real-time volume concentration of proppant in the carrier fluid of the excitation well is obtained, and a proppant viscosity correction factor is constructed: ; in, f ( C prop Support phase viscosity correction factor, dimensionless; C prop The volume concentration of the proppant in the carrier fluid is dimensionless. The maximum close-packed volume fraction of the proppant is dimensionless. The intrinsic viscosity factor is dimensionless. The viscosity correction factor is introduced into the fluid conductivity model to obtain the corrected conductivity of the inter-well crossflow channels. C D Dynamic calculation formula: ; in, C D The conductivity of the inter-well crossflow channel is dimensionless. In response to the increase in well pressure, MPa; The viscosity of the fracturing fluid system is expressed in mPa·s. Q The displacement during well construction is expressed in m³ / min.T res The pressure response time is in minutes. Furthermore, within the fracturing operation cycle, the calculated conductivity is monitored in real time. C D Taking the first derivative over time yields the dynamic rate of change of the conductivity; when > ( When the preset critical rate threshold for accelerated channel development is reached, the system can determine that a large-scale malignant fluid crossflow channel between wells is accelerating its development.

[0020] In this invention, the specific process of step S4 is as follows: acquiring well logging geological data of the target layer and extracting the natural fracture development density index. I frac With brittleness index B I Adaptive correction of the dimensionless formation dissipation coefficient and stress fracture network complexity gain coefficient is performed using well logging mapping functions. ; ; In the formula, The formation dissipation coefficient, after dynamic correction of logging parameters, represents the penalty weight for ineffective energy dissipation and is dimensionless. The gain coefficient for the complexity of the stress joint network, after dynamic correction, represents the weight of the complex joint network caused by the geostress shadow, and is dimensionless. , where is the baseline formation dissipation coefficient, and is the basic dissipation weight calibrated by experimental and historical fitting, dimensionless; The reference stress gain coefficient is the experimentally calibrated base stress deflection gain weight, which is dimensionless. a , b , c It is a dimensionless constant used as a geological adaptive adjustment factor to correlate heterogeneity with dissipation / gain properties; The revised and Substituting these values, we obtain the final equation for the overall efficiency evolution of reservoir stimulation: ; in, or c The overall efficiency of reservoir stimulation is dimensionless. L 1 represents the length of the fracture in the excitation well, in meters (m). L 2 represents the length of the fracture in the response well, in meters. P f The initial formation fracture pressure of the response well is measured in MPa. T frac The total fracturing time for this section of the well is given in minutes. This formula, for the first time, realizes the dynamic reduction correction and explicit nonlinear characterization of the macroscopic stimulation volumetric efficiency model using well logging micro-inhomogeneity parameters (such as three-dimensional spatial surface response characteristics). Figure 3 (As shown).

[0021] In this invention, the specific process of step S5 is as follows: Within the framework of full life-cycle mining and fracturing simulation, a multi-dimensional decision space for displacement mechanisms is constructed. Within each fracturing operation time step, a sequential iteration and dynamic evaluation mechanism is employed. First, real-time monitoring data is input to calculate the current time step. C D , or c as well as Real-time determination of reservoir stimulation modes and execution of the following automated quantitative closed-loop control strategy: like or c ≥1.05 and ≤ When the crack is determined to be a benign crack propagation induced by stress, the system outputs a command to maintain or moderately increase the construction flow rate to promote crack propagation. If 0.85≤ or c When the value is less than 1.05, it is determined to be a steady-state micro-interference zone. At this point, slight porosity elastic conduction occurs between wells, there are no macroscopic fluid leakage channels, the activated well achieves equal-length expansion, and it advances smoothly according to the original design parameters, with increased monitoring. C D The rate of change of the derivative; If discovered within the time step or c <0.85 and > At that time, the system triggers a real-time quantitative intervention mechanism, automatically calculating the intelligent dosing quality of the temporary plugging agent based on the current efficiency deficit. M And the automatic reduction range of displacement: ; ; In the formula, M The mass of the temporary plugging agent for intelligent dosing is measured in kg. k The temporary blocking quantitative decision coefficient includes empirical constants for the dimensional conversion equivalents between emission volume, concentration, and mass. The automatic reduction range for displacement is measured in m³ / min. Q design The original design displacement was m³ / min; the system issued a construction instruction to pump in a specified mass of temporary plugging agent and reduce the displacement until the requirement was met again. The convergence condition for <0.

[0022] Example 1: Geological data of a deep shale gas well was obtained through field logging. This well suffered from severe fracturing and channeling, and lacked scientific and reasonable technical guidance. This well is typical of deep shale gas reservoirs, with a burial depth exceeding 3500m, well-developed natural fracture zones, and high formation pressure. This well serves as a good demonstration case for this method. The specific simulation method steps are as follows ( Figure 1 ): 1. Parameter Acquisition: Based on previous geological studies and well logging data of the target block, the geological characteristic parameters of a shale gas platform were obtained, including the initial formation fracture pressure. P f =45MPa, rock elastic modulus 35GPa, Poisson's ratio 0.22; through previous core mechanics experiments and microseismic history fitting, the dimensionless formation dissipation coefficient was calibrated. =2.5, Gain coefficient for mesh complexity =0.4; Based on the analysis of previous block fracturing tests and historical crosstalk data, the preset channel accelerated development warning threshold α=0.01min is used in the system. -1 The length of the hydraulic fracture in the foundation, calculated after pressure testing of the response well (Well No. 1). L 2 = 150m; 2. Real-time monitoring and feature extraction: During fracturing in the excitation well (well No. 2), the real-time fracturing parameters were: constant construction flow rate. Q =14 m³ / min, fracturing fluid viscosity =3mPa·s; Total design time for single-section construction T frac =120 min, during the fracturing process, the ground high-frequency data acquisition system recorded the wellhead pressure of well No. 1 in real time; 3. Implementation Scenario 1 (Verification of Stress-Induced Enhancement Zone): When Well No. 2 reached the 5th stage of construction, the volume concentration of proppant pumped in the early stage of this stage was low. Real-time monitoring... C prop The value was 5%; the natural fracture density index extracted from the well logging in this section was... I frac The brittleness index is 0.45. B I The value is 0.55; the system adaptively decouples and calculates the corrected formation dissipation coefficient λ for this section as 2.3 and the fracture network complexity gain coefficient γ as 0.48 based on the well logging mapping function; the data acquisition system monitors that 80 minutes after the pump is started, the pressure in well No. 1 only shows a slight increase deviating from the baseline (i.e., T res =80min); at the end of the construction, the pressure in Well No. 1 increased by 2MPa from the initial pressure (i.e., =2MPa); According to microseismic inversion, the actual fracture length in this segment is...L 1 = 160m; The formula for calculating the conductivity of fluid crossflow channels is obtained. C D The value is 0.0061; the conductivity is extremely low, and the system detected its time derivative. The value is only 0.001 min⁻¹ (far below the warning threshold α), indicating that no macroscopic physical leakage channel has been formed. Substituting the parameters into the formula for the comprehensive efficiency of reservoir stimulation yields... or c The score is 1.2; a comprehensive evaluation yields... or c ≥1.05, the system determines that this segment belongs to the "stress-induced complex fracture network high-efficiency zone" (inter-well stress field and fracture deflection mode, such as...). Figure 4 As shown in the figure, due to the stress shadow effect of Well 1, the fractures in Well 2 became tortuous and branched, and the actual volume of the fracturing far exceeded that of a single main fracture. Based on this result, the original design displacement of 14 m³ / min was maintained and construction continued (the dynamic evolution trajectory of conductivity throughout the fracturing process is shown in the figure). Figure 7 (As shown by the green line).

[0023] 4. Implementation Scenario 2 (Verification of Conventional Balance Zone): When Well No. 2 is constructed to the 6th stage, the volumetric concentration of proppant is monitored in real time. C prop The natural fracture density index extracted from the well logging in this section rose to 10%. I frac The brittleness index is 0.58. B I The value is 0.6; the system adaptively decouples and calculates the corrected formation dissipation coefficient λ for this section as 2.7, and the fracture network complexity gain coefficient γ as 0.5 based on the well logging mapping function; the data acquisition system monitors that 40 minutes after the pump is started, the pressure in well No. 1 slowly increases ( T res =40min); pressure increase at the end of fracturing =4MPa, this section calculates the actual joint length. L 1 = 148m; Calculate channel conductivity C D The value is 0.028, and the system detected its time derivative. The value is 0.004 min⁻¹ (still below the warning threshold α), indicating a slow, extremely low-amplitude climb; calculate the overall efficiency. or c The value is 0.89; a comprehensive evaluation yields... or c Falling into 0.85≤ or c In the range <1.05, the system determines that this segment belongs to the "conventional stimulation equilibrium zone" (inter-well stress field and fracture deflection mode, such as...). Figure 5 (As shown in the image). This indicates a slight pressure sensing between the wells, but no substantial fluid leakage channel has formed. The formation energy dissipation and weak stress gain are in a dynamic equilibrium. Based on this result, no aggressive intervention measures are needed on-site. The original design discharge and fluid volume should be maintained for stable construction. At the same time, this section should be marked as a "key observation section" and entered into further investigation. The intensified monitoring phase of evolutionary trends (comparison of dynamic evolution trajectory of conductivity throughout fracturing process, as shown in the figure) Figure 7 (As shown by the blue line).

[0024] 5. Implementation Scenario 3 (Verification of dynamic derivative early warning and closed-loop quantitative intervention in the malignant fluid crossflow zone): When well No. 2 was constructed to the 8th stage, the volumetric concentration of proppant was monitored in real time. C prop The natural fracture density index extracted from this section of the well logging was 12%. I frac The brittleness index is 0.78. B I The value was 0.65; the system adaptively decoupled and calculated the corrected formation dissipation coefficient λ for this section as 3.2, and the fracture network complexity gain coefficient γ as 0.52, based on the well logging mapping function; only 15 minutes after the pump was started, the high-frequency pressure gauge of well No. 1 detected a violent jump in wellhead pressure, with the pressure increase instantaneously reaching 0.65. =12MPa, this section calculates the actual joint length. L 1 = 140m; Calculated C D The value is 0.239, and the system captures the time derivative of the conductivity. =0.038 min⁻¹, far exceeding the preset alarm threshold α=0.010 min⁻¹; Calculate the overall efficiency or c The value is 0.42; a comprehensive evaluation yields... or c If the value is less than 0.85, the system alarm determines that the segment has encountered a "fluid channel type malignant interference zone" (abnormal high pressure field of fluid and through-flow mode, such as...). Figure 6 (As shown); at this point, the system immediately activates the closed-loop quantitative intervention control model, automatically calculating the required mass of multi-stage steering temporary plugging agent to be pumped in. M ≈145kg, and calculate the automatic displacement reduction range. ≈7.08 m³ / min; On-site, the sand-adding truck and pump truck were immediately coordinated to precisely pump 145 kg of multi-stage temporary plugging particles into the wellbore, and the operation flow rate was reduced from 14 m³ / min to approximately 7 m³ / min to resume fracturing; after the temporary plugging agent entered the main flow channel and forced it to change direction, the monitoring system showed... It quickly dropped below 0, and during subsequent construction... or cGradual recovery (comparison of the dynamic evolution trajectory of conductivity throughout the fracturing process, as shown in the figure) Figure 7 (As shown by the red line).

[0025] Based on the quantitative evaluation method described in this invention, firstly, by accurately capturing the fluid-structure interaction physical mechanism of hydrodynamic transmission of sand-bearing fracturing fluid and dynamic deflection of rock stress field during zipper fracturing, a solid theoretical support is provided for scientifically assessing the intensity of inter-well crossflow and accurately predicting the effective reservoir stimulation volume (SRV). Secondly, by innovatively introducing dynamic mapping of logging parameters and a first-order time derivative early warning index, the traditional static geometric evaluation model is reconstructed through multi-physics nonlinear coupling, completely breaking the technical bottleneck of relying solely on single-point pressure difference to qualitatively judge the degree of crossflow at the fracturing site, which is prone to "false crossflow" misjudgment. Finally, this invention not only constructs an automated quantitative intervention model for temporary plugging agents based on comprehensive efficiency deficit, significantly improving the accuracy of dynamic evaluation of inter-well interference and real-time closed-loop control capabilities, but also provides highly valuable technical guidance for the efficient development of multi-well platforms for unconventional oil and gas resources, intelligent control of fracturing construction parameters, and the maximization of the final single-well recovery rate (EUR).

[0026] The above description is not intended to limit the present invention in any way. Although the present invention has been disclosed through the above embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A quantitative evaluation method for fracturing crosstalk based on multi-physics coupling and pressure response characteristics, characterized in that, Includes the following steps: S1. Based on the geological and engineering characteristics of the target reservoir, obtain basic fracturing data and real-time monitoring parameters; S2. Based on the real-time wellhead pressure data of the response well collected by the inter-well pressure monitoring system, and combined with the time axis of the fracturing operation of the excitation well, extract the nonlinear mapping pressure response time and the pressure increase of the response well. S3. Obtain the real-time volume concentration of proppant in the fracturing fluid of the excitation well. Based on Darcy's law of seepage and the theory of non-Newtonian fluid dynamics in sand-bearing wells, construct a dynamic calculation model of fluid crossflow channel conductivity considering the correction of proppant phase viscosity, and calculate the first time derivative of the conductivity in real time as a transient dynamic early warning indicator of malignant crossflow between wells. S4. Obtain well logging geological data of the target layer, extract the natural fracture development density index and brittleness index, dynamically decouple the formation dissipation coefficient and fracture network complexity gain coefficient through well logging parameters, and then couple the energy dissipation penalty and stress shadow gain to establish a comprehensive reservoir stimulation efficiency evaluation model. S5. Based on the fluid crossflow channel conductivity, conductivity time derivative and reservoir stimulation efficiency calculated within the current construction step, the reservoir stimulation mode is determined based on a multi-dimensional threshold matrix. When a malignant crossflow zone is determined, an automated quantitative intervention mechanism is triggered. Based on the current efficiency deficit, the intelligent addition quality and displacement reduction of the temporary plugging agent are automatically calculated to achieve quantitative closed-loop control of the fracturing strategy.

2. The quantitative evaluation method for fracturing crosstalk based on multi-physics coupling and pressure response characteristics according to claim 1, characterized in that, The specific process of step S1 is as follows: during the zipper-type fracturing operation, the construction displacement of the excitation well is obtained in real time. Q fracturing fluid viscosity Total fracturing time in the current stage T frac Simultaneously, the initial formation fracture pressure of the response well was obtained using a high-frequency pressure gauge at the wellhead. P f and real-time pressure monitoring curve P ( t ).

3. The method according to claim 1, characterized in that, The specific steps of S2 include: performing time-frequency domain feature analysis on the real-time pressure curve of the response well and extracting the initial pressure jump moment of the response well. T initial ; Calculate the pressure response time: ; Response well pressure increase: ; in, T res The pressure response time is in minutes. T initial To respond to the initial rise in well pressure, min; T start The starting time of fracturing in the excited well is min; In response to the increase in well pressure, MPa; P initial The pressure value at the moment of response to well pressure rise, in MPa; P end The pressure value, in MPa, corresponds to the moment when the fracturing of the excitation well ends.

4. The method according to claim 1, characterized in that, The specific steps of S3 include: Obtain the real-time volume concentration of proppant in the carrying fluid of the excitation well. C prop Construct a proppant viscosity correction factor: ； in, f ( C prop Support phase viscosity correction factor, dimensionless; C prop The volume concentration of the proppant in the carrier fluid is dimensionless. The maximum close-packed volume fraction of the proppant is dimensionless. The intrinsic viscosity factor is dimensionless. The viscosity correction factor is introduced into the fluid conductivity model to obtain the corrected conductivity of the inter-well crossflow channels. C D Dynamic calculation formula: ； in, C D The conductivity of the inter-well crossflow channel is dimensionless. In response to the increase in well pressure, MPa; The viscosity of the fracturing fluid is expressed in mPa·s. Q The displacement during well construction is expressed in m³ / min. T res The pressure response time is expressed in minutes; within the fracturing operation cycle, the calculated conductivity is monitored in real time. C D Taking the first derivative over time yields the dynamic variation of conductivity; when the dynamic variation of conductivity... > At that time, it was determined that large-scale malignant fluid leakage channels between wells were developing rapidly, among which... The preset critical rate threshold for accelerated channel development.

5. The method according to claim 1, characterized in that, The specific steps of step S4 include: Extracting the density index of natural crack development I frac With brittleness index B I Using well logging mapping functions to analyze the dimensionless formation dissipation coefficient λ Gain coefficient for complexity of stress joint mesh γ Perform adaptive correction: ； ； In the formula, The formation dissipation coefficient, after dynamic correction of logging parameters, represents the penalty weight for ineffective energy dissipation and is dimensionless. The stress joint network complexity gain coefficient, after dynamic correction, characterizes the weight of the ground stress shadow contributing to the complex joint network, and is dimensionless. , where is the baseline formation dissipation coefficient, and is the basic dissipation weight calibrated by experimental and historical fitting, dimensionless; The reference stress gain coefficient is the experimentally calibrated base stress deflection gain weight, which is dimensionless. a , b , c It is a dimensionless constant used as a geological adaptive adjustment factor to correlate heterogeneity with dissipation / gain properties; The revised and Substituting these values, we obtain the final equation for the overall efficiency evolution of reservoir stimulation: ； in, η c The overall efficiency of reservoir stimulation is dimensionless. L 1 represents the length of the fracture in the excitation well, in meters; L 2 represents the length of the fracture in the response well, in meters. P f The initial formation fracture pressure of the response well is measured in MPa. T frac The total fracturing time for this section of the well is expressed in minutes.

6. The method according to claim 1, characterized in that, The specific steps of step S5 include: The system is based on the current construction step length. C D , η c as well as Real-time determination of reservoir stimulation modes and execution of closed-loop control strategies: when η c ≥1.05 and ≤ When this is determined to be stress-induced benign expansion of the seam mesh, the construction flow rate should be maintained or moderately increased; if 0.85 ≤ η c When the value is less than 1.05, it is determined to be in the steady-state micro-interference zone, and the original design displacement is maintained to ensure smooth progress. η c <0.85 and > At this time, the system triggers a real-time quantitative intervention mechanism, automatically calculating the intelligent dosing quality and displacement of the steering temporary plugging agent based on the current efficiency loss, and automatically adjusting the dosage accordingly. ； ； In the formula, M The mass of the temporary plugging agent for intelligent dosing is measured in kg. k The temporary blocking quantitative decision coefficient includes empirical constants for the dimensional conversion equivalents between emission volume, concentration, and mass. The automatic reduction range for displacement is measured in m³ / min. Q design The original design displacement is m³ / min; the system issues a pumping order for the specified mass. M The temporary plugging agent and the construction instructions to reduce the discharge volume, until the requirements are met again. The convergence condition for <0.

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