Fracturing crack propagation dynamic measurement and control method based on mutual feedback of microseism and numerical simulation

Abstract

The invention discloses a micro-seismic and numerical simulation mutual feedback fracturing crack propagation dynamic measurement and control method. The method comprises the following steps: constructing a fracturing crack propagation simulation calculation model; monitoring a micro-seismic waveform, obtaining spatial distribution of a seismic source and micro-seismic characteristic parameters according to the micro-seismic waveform, and generating a distribution rule; a crack area is screened out according to the distribution rule; determining spatial distribution of rock fracture mechanical parameters of the fracture area according to a correlation model of the micro-seismic characteristic parameters of the fracture area and rock nonlinear fracture characteristics; and mechanical parameters are substituted into the simulation calculation model for fracturing crack propagation prediction. By means of the method, dynamic updating of parameters such as physical mechanics of the numerical simulation model along with micro-seismic monitoring can be achieved; through mutual feedback dynamic of microseism and numerical simulation, real-time prediction and control of the expansion form and track of the fracturing crack in engineering can be finally realized.

Description

Technical Field

[0001] The present invention relates to the exploitation of underground geological new energy, and in particular to a dynamic measurement and control method for fracturing crack expansion with mutual feedback of microseismic and numerical simulation. Background Art

[0002] Hot dry rock geothermal energy, unconventional oil and gas, and coalbed methane are important strategic replacement geological new energy sources in my country. Among them, hot dry rock geothermal energy has the dual advantages of huge reserves and clean and low-carbon.

[0003] Fracturing and creating fracture networks to improve reservoir permeability is the core technology for exploiting hot dry rock geothermal energy, unconventional oil and gas, and coalbed methane. Hot dry rocks, mainly granite, are very dense. If heat is circulated through injection wells (injecting cold water) and extraction wells (extracting hot water for heat exchange), it is necessary to use a fracturing method that uses high-pressure fluid to break the rock to form an artificial fracture network in the reservoir, so that the fluid flows in the artificial fracture network, thereby improving the heat exchange efficiency of the injected fluid in the artificial fracture network; and unconventional oil and gas resources and coalbed methane, due to the low reservoir permeability, oil and gas are difficult to extract, and it is also necessary to form an artificial fracture network in the reservoir to improve the reservoir permeability, thereby increasing the recovery rate of oil and gas in the reservoir. In addition, fracturing is also a key technology for controlling the surrounding rock of well mining and increasing the permeability of coalbed gas.

[0004] Since the cracks driven by fracturing expand inside the rock formation, high-precision monitoring of cracks and real-time control of crack expansion will greatly improve the efficiency of reservoir permeability transformation. However, fracturing cracks have always faced the problem of being difficult to measure and control, because the correlation between microseismic monitoring characteristic parameters and numerical simulation theory and numerical simulation models has not yet been established, microseismic monitoring of fracturing cracks and numerical simulation prediction of fracturing crack expansion are independent of each other, and fracturing crack monitoring and numerical simulation are difficult to feedback each other in real time, so it is difficult to control the crack morphology and expansion trajectory by dynamically adjusting process parameters. Summary of the invention

[0005] Purpose of the invention: The purpose of the invention is to provide a dynamic measurement and control method for fracturing crack expansion with mutual feedback of microseismic and numerical simulation, so as to realize the prediction and control of the morphology and trajectory of fracturing crack expansion in engineering.

[0006] To solve the above technical problems, an embodiment of the present invention provides a dynamic measurement and control method for fracturing crack expansion with mutual feedback of microseismic and numerical simulation, including constructing a simulation calculation model for fracturing crack expansion; monitoring microseismic waveforms, obtaining the spatial distribution of earthquake sources and microseismic characteristic parameters according to the microseismic waveforms and generating a distribution law; screening out fracture zones according to the distribution law; determining the spatial distribution of rock fracture mechanics parameters in the fracture zone according to a correlation model between the microseismic characteristic parameters of the fracture zone and the nonlinear fracture characteristics of the rock; and substituting the rock fracture mechanics parameters into the simulation calculation model to predict fracturing crack expansion.

[0007] In addition, the simulation calculation model includes engineering conditions, physical and mechanical parameters and a calculation function; the calculation function calculates the physical and mechanical parameters according to the engineering conditions.

[0008] In addition, the spatial distribution of the earthquake source and microseismic characteristic parameters is obtained based on the microseismic waveform and a distribution law is generated, including: locating the earthquake source using the acoustic emission and microseismic positioning method of full waveform analysis; analyzing and calculating the waveform and event evolution characteristics of the acoustic emission, the waveform and event evolution characteristics of the microseismic based on the spatial coordinates of the earthquake source positioning point, and obtaining microseismic characteristic parameters that characterize the acoustic emission and microseismic characteristics, wherein the microseismic characteristic parameters include earthquake source event density and earthquake source energy; identifying areas where the density of earthquake source event density and earthquake source energy distributed in space is greater than a preset threshold, and obtaining the distribution law based on the areas.

[0009] In addition, the regions where the density of source events and the density of source energy distributed in space are greater than a preset threshold are identified, and the distribution law is obtained according to the regions, including: constructing spatial units in the microseismic space; counting the accumulated source energy and the accumulated source events in each unit in turn; taking the peak point of the sum of the accumulated source energy and the accumulated source events as the central source point, and extending the statistics of source energy and source events to the surrounding of the microseismic space; comparing the sum of the accumulated source energy and the accumulated source events in each unit with a preset threshold, and selecting the region where the unit with a value greater than the preset threshold is located; and obtaining the distribution law of source energy and source events according to the region.

[0010] In addition, according to the correlation model between the microseismic characteristic parameters of the fracture zone and the nonlinear fracture characteristics of the rock, the spatial distribution of the rock fracture mechanics parameters in the fracture zone is determined; including: constructing a correlation model between the microseismic characteristic parameters of the fracture zone and the fracture and rupture characteristics of the rock; using acoustic emission and microseismic full waveform analysis methods and full waveform microseismic analysis methods to analyze and monitor the microseismic results of the fracture zone in real time to obtain the microseismic characteristic parameters of the fracture zone; inputting the microseismic characteristic parameters into the correlation model to obtain the spatial distribution of the rock fracture mechanics parameters in the fracture zone; wherein the correlation model includes a rock fracture model, a fracturing crack extension physical model, a time-dependent rock subcritical crack extension model, a unit damage mechanics model, and a spatial material point mechanics characteristic determination model.

[0011] In addition, after the prediction of the expansion of the fracturing crack, it also includes: judging whether the simulation results of the numerical prediction of the expansion of the fracturing crack meet the requirements of the fracturing design; when it does not meet the requirements of the fracturing design, adjusting the engineering condition parameters until the simulation results of the numerical prediction of the expansion of the fracturing crack meet the requirements of the fracturing design.

[0012] In addition, in the n+1th prediction, the fracture area screened out for the nth time is used as the encrypted trial calculation area and fine partitioning area for the n+1th earthquake source location initial value, where n is an integer ≥1.

[0013] Compared with the prior art, the present invention has the following significant effects: in the present invention, by constructing a correlation model between microseismic characteristic parameters and physical and mechanical parameters, the physical and mechanical parameters of the numerical simulation model can be dynamically updated with microseismic monitoring; the fracture area can be more accurately determined and the distribution law can be identified through the combined analysis of the source energy and the spatial distribution of the source event; by predicting the subsequent expansion law and trajectory of the fracturing cracks under the current process and engineering parameters, mutual feedback between microseismic and numerical simulation can be achieved, and finally the real-time prediction and control of the expansion morphology and trajectory of the fracturing cracks in the project can be realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a method for dynamic measurement and control of fracturing crack expansion with mutual feedback of microseismic and numerical simulation according to an embodiment of the present invention;

[0015] Figure 2 A diagram showing a correlation between microseismic characteristic parameters and mechanical parameters according to an embodiment of the present invention;

[0016] Figure 3 A mutual feedback schematic diagram of an embodiment of the present invention;

[0017] Figure 4 The diagram is a mutual feedback flow diagram of an implementation mode of the present invention. DETAILED DESCRIPTION

[0018] To make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present invention, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can be implemented. The division of the following embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined and referenced with each other without contradiction.

[0019] One embodiment of the present invention relates to a dynamic measurement and control method for fracturing crack expansion with mutual feedback of microseismic and numerical simulation, which can be applied in a fracturing measurement and control system. In the embodiment of the present invention, the measurement and control method includes: constructing a simulation calculation model for fracturing crack expansion; monitoring microseismic waveforms, obtaining the spatial distribution of earthquake sources and microseismic characteristic parameters according to the microseismic waveforms and generating distribution laws; screening out fracture areas according to the distribution laws; determining the spatial distribution of rock fracture mechanics parameters in the fracture area according to the correlation model between the microseismic characteristic parameters of the fracture area and the nonlinear fracture characteristics of the rock; substituting the mechanical parameters into the simulation calculation model to predict the expansion of fracturing cracks. The correlation model between microseismic characteristic parameters and physical and mechanical parameters is constructed by the above method, and the physical and mechanical parameters of the numerical simulation model are dynamically updated with microseismic monitoring; by predicting the subsequent expansion law and trajectory of the fracturing crack under the current process and engineering parameters, the mutual feedback dynamics of microseismic and numerical simulation are realized, and finally the real-time prediction and control of the expansion morphology and trajectory of the fracturing crack can be realized in engineering. The following is a detailed description of the implementation details of the method for dynamic monitoring and control of fracturing crack extension with mutual feedback of microseismic and numerical simulation according to one embodiment of the present invention. The following content is only provided for easy understanding of the implementation details and is not necessary for the implementation of this solution.

[0020] In one embodiment of the present invention, the process of the dynamic monitoring and control method of fracturing crack extension with mutual feedback of microseismic and numerical simulation is as follows: Figure 1 In step 101, a simulation calculation model for fracturing crack extension is constructed.

[0021] Specifically, the simulation calculation model includes engineering conditions, physical and mechanical parameters and calculation functions; the calculation function calculates the physical and mechanical parameters according to the engineering conditions. In one example, the numerical simulation calculation model can be used for hydraulic fracturing scenarios such as deep high-temperature oil and gas reservoirs, geothermal heat storage such as dry hot rocks, and oil shale and coal seams that require high-temperature modification. The numerical simulation calculation model includes but is not limited to the compression-shear thermoplastic constitutive model and the thermoplastic fracture model:

[0022] (1) The compression-shear thermoplastic constitutive model includes: a1, a plastic modulus that characterizes the strengthening and softening caused by stress changes; a2, a temperature-sensitive modulus that characterizes the strengthening and softening caused by temperature changes; a3, an elastic-plastic stiffness matrix; and a4, a temperature conversion tensor. The elastic-plastic stiffness matrix and the temperature conversion tensor can be updated in real time according to the thermoplastic constitutive relation to obtain the stress-strain-temperature constitutive response relation under the latest state.

[0023] (2) The thermoplastic fracture model includes the following: b1 cohesive tensile strength; b2 critical crack opening displacement; b3 fracture process zone length; b4 fracture energy per unit length; b5 accumulated dissipated energy; b6 temperature-sensitive modulus of strengthening (softening) of the process zone caused directly by temperature. When using different processes (including but not limited to carbon dioxide and supercritical carbon dioxide fracturing, hydraulic fracturing, circulating pump injection and pulse fracturing, temporary plugging fracturing, segmented and multi-cluster fracturing within a segment, combined and layered fracturing, and fracturing of different targets), the engineering conditions include but are not limited to: displacement, viscosity, fracturing fluid type, liquid volume, proppant scale type; solid mechanical parameters such as elastic parameters, plasticity, strength, viscosity, fracture, and permeability, porosity, filtration rate, and other physical parameters related to fluid seepage flow.

[0024] It should be pointed out that the microseismicity mentioned in the present invention refers to elastic waves in a broad sense, that is, elastic waves released by material rupture, and also includes microseismicity and acoustic emission; this method can be applied to all microseismic monitoring and acoustic emission monitoring methods, covering all field projects and laboratory tests, and can be applied to all types of microseismic sensors, such as optical fiber type, piezoelectric type, vibration type, and three-component type.

[0025] In step 102, the microseismic waveform is monitored, the spatial distribution of the earthquake source and microseismic characteristic parameters is obtained according to the microseismic waveform, and the distribution law is generated, and the fracture area is screened out according to the distribution law.

[0026] Specifically, step 102 includes locating the earthquake source by using the acoustic emission and microseismic full waveform analysis method and the full waveform microseismic analysis method; based on the spatial coordinates of the earthquake source positioning point, analyzing and calculating the waveform and event evolution characteristics of the acoustic emission, the waveform and event evolution characteristics of the microseismic, and obtaining microseismic characteristic parameters that characterize the acoustic emission and microseismic characteristics, wherein the microseismic characteristic parameters include earthquake source event density and earthquake source energy; identifying areas where the density of earthquake source event density and earthquake source energy distributed in space is greater than a preset threshold, and obtaining the distribution law according to the area.

[0027] In one example, locating the earthquake source includes locating the earthquake source using acoustic emission and microseismic positioning methods using full waveform analysis: first, determining microseismic sampling parameters and high-quality waveform characteristics, and then obtaining elastic waves corresponding to crack expansion and rock rupture. According to the frequency from low to high, the elastic waves may include microseismic and acoustic emission to obtain high-quality waveforms. Further, based on the acquisition of high-quality original waveforms, a group of waveforms corresponding to the same earthquake source are determined, and the arrival time of each waveform is obtained; considering the prominent characteristics of the attenuation of wave velocity due to the large-scale development of microcracks when rock materials fracture, the change of wave velocity due to rock deformation, and the difference in wave velocity in different formations, it is preferred to use dynamic wave velocity for earthquake source positioning. If the formation homogeneity is good and the microcrack zone has little effect on the wave velocity, constant wave velocity can also be used for earthquake source positioning. Based on the spatial coordinates of the earthquake source point accurately located, the waveforms and event evolution characteristics of acoustic emission and microseismic events are further analyzed and calculated to obtain multiple microseismic characteristic parameters that characterize the characteristics of acoustic emission and microseismic events, such as earthquake source event density, earthquake source energy, earthquake source wave velocity, earthquake source frequency spatial distribution, earthquake source mechanism, b-value evolution and all other waveform and evolution characteristics.

[0028] Further, by analyzing the microseismic characteristic parameters, the spatial distribution law is obtained, and the fracture zone (fracture zone includes macro-fracturing fractures and secondary fractures or micro-fracture zones around the fractures) is identified. The microseismic characteristic parameters and their corresponding mechanical characteristics are as follows: the spatial aggregation area of ​​microseismic events characterizes the macro-hydraulic fractures and the secondary fracture zones around them (including micro-fracture zones); the spatial distribution of microseismic energy characterizes the energy dissipation distribution of hydraulic fractures and the secondary fracture zones around them; the spatial distribution of microseismic wave velocity characterizes the microfracture damage characteristics of hydraulic fractures and the secondary fracture zones around them; the spatial distribution of microseismic frequency characterizes the scale of hydraulic fractures and the secondary fracture zones around them, and the source mechanism characterizes the tensile, shear and tension-shear composite fracture mechanisms in the hydraulic microfracture zone. Specifically, the corresponding relationship between microseismic characteristic parameters and mechanical characteristics such as rock fractures is as follows: Figure 2 The method for identifying macroscopic fracturing cracks mainly involves identifying areas where the spatial density of earthquake source energy and earthquake source events is greater than a preset threshold, and analyzing their distribution patterns.

[0029] Specifically, the combined analysis of the spatial distribution of source energy and source events is mainly used to eliminate the potential risk of non-continuous distribution restricting crack identification under the condition of separate analysis of the two. During the analysis, the parameter with the best spatial distribution continuity of source energy and source events is used to determine the location of crack formation. In addition, the analysis of the distribution law includes first constructing a spatial unit body in the microseismic space, and successively counting the cumulative amount of source energy and source events in each unit body; further, taking the peak point of the cumulative amount of source energy and source events as the central source point, extending the source energy and source events around the microseismic space, and then comparing the cumulative amount of source energy and source events in each unit body with the preset threshold (70% of the source energy and source event density of the central source point), selecting the area exceeding the preset threshold as the macro-fracture formation area, and obtaining the distribution law of source energy and source events. The setting of the preset threshold can be adjusted according to the actual production scenario.

[0030] Based on the above distribution law, we can introduce digital twin technology, specifically artificial intelligence algorithms, to intelligently identify macro fractures and secondary fracture areas around fractures, and determine the spatial distribution law of characteristic parameters. Specifically, after obtaining the macro fracture formation area and distribution law, linear regression models, logistic regression models, decision tree models, support vector machine (SVM) models, neural network models, and ensemble learning models are used to fit and optimize the spatial distribution of microseismic characteristic parameters, and discrete points of parameters are picked up for regression processing, thereby improving the accuracy of spatial distribution of microseismic characteristic parameters and accurately characterizing and identifying macro fractures and secondary fracture areas around fractures.

[0031] In order to avoid the singleness and limitations of the microseismic characteristic parameters in characterizing and identifying macro-cracks and secondary fracture zones around cracks, models such as (including but not limited to) correlation analysis model, regression analysis model, neural network model, ensemble learning model, support vector machine (SVM) model, Bayesian network model and hidden Markov model (HMM) are used to analyze the correlation between microseismic characteristic parameters (including macro-hydraulic fractures and their surrounding secondary fracture zones (including micro-crack zones) characterized by the spatial clustering areas of microseismic events, energy dissipation distribution of hydraulic fractures and their surrounding secondary fracture zones characterized by the spatial distribution of microseismic energy, micro-fracture damage characteristics of hydraulic fractures and their surrounding secondary fracture zones characterized by the spatial distribution of microseismic wave velocity, scales of hydraulic fractures and their surrounding secondary fracture zones characterized by the spatial distribution of microseismic frequencies, and correlation between tensile, shear and tensile-shear composite fracture mechanisms in hydraulic micro-crack zones characterized by source mechanisms), and the spatial distribution of microseismic characteristic parameters is fitted and optimized to further obtain macro-cracks and secondary fracture zones around cracks characterized by the combination of multiple parameters.

[0032] In step 103, the spatial distribution of rock fracture mechanics parameters in the fracture zone is determined based on a correlation model between microseismic characteristic parameters in the fracture zone and nonlinear fracture characteristics of rock.

[0033] Specifically, it includes constructing a correlation model between microseismic characteristic parameters and rock fracture and rupture characteristics; using acoustic emission and microseismic full waveform analysis methods and full waveform microseismic analysis methods to analyze and monitor the microseismic results of the fracture zone in real time to obtain the microseismic characteristic parameters of the fracture zone; inputting the microseismic characteristic parameters into the correlation model to obtain the spatial distribution of rock fracture mechanics parameters in the fracture zone; wherein the correlation model includes a fracture model suitable for rock, a physical model of fracturing crack extension, a time-dependent rock subcritical crack extension model, a unit damage mechanics model, and a spatial material point mechanics characteristic determination model.

[0034] In one example, constructing a correlation model between microseismic characteristic parameters and rock fracture and rupture characteristics includes:

[0035] c1 Fracture model applicable to rock, obtain the constitutive relation and parameters of the fracture model: carry out three-point bending fracture test in the laboratory, record various macroscopic corresponding data such as loading force, displacement, deformation, crack opening, etc., use various monitoring methods such as acoustic emission (indoor microseismic), digital image, fiber Bragg grating, etc., to determine the constitutive relation and model parameters of the fracture model applicable to rock. Typical parameters are as follows (not limited to): the correlation between the softening function of cohesion-crack opening of the cohesive fracture model and the spatial distribution of acoustic emission and microseismic characteristic parameters; determine the correlation between "the spatial accumulation of acoustic emission energy, events, etc., as well as the spatial distribution of frequency, wave velocity, different types of fracture ratios, etc." and rock elasticity, viscosity, strength and fracture parameters;

[0036] c2 Fracturing crack extension physical model: Based on the correlation between the acoustic emission and microseismic energy distribution characteristics monitored in the fracture experiment and the phase field model, the acoustic and optical joint monitoring method is adopted to characterize the distribution characteristics of parameters such as dissipated energy, fracture mechanism, displacement field, strain field, crack opening, stress field, etc. in the cross-sectional crack extension direction in the fracture test, which are used to construct the phase field model and then obtain the fracturing crack extension physical model.

[0037] c3 Time-dependent rock subcritical crack growth model: Based on the correlation between the size of the microcrack zone and the characteristics of acoustic emission, microseismic energy and event density: Acoustic emission, microseismic energy and event density are used to characterize the spatial evolution of the microcrack zone, and to determine the energy dissipation in the microcrack zone, and then infer the local stress response, thereby constructing a time-dependent rock subcritical crack growth model. This model is particularly suitable for liquid and supercritical carbon dioxide fracturing and well sealing fracture technology.

[0038] c4 unit damage mechanics model: adopts the microseismic characteristic parameters and fracturing crack correlation discrimination method of "microseismic parameter evolution-macro-micro crack generation", captures the evolution characteristics of microseismic characteristic parameters such as wave velocity attenuation, energy accumulation, and frequency increase, and then judges the initiation and development of macro- and micro-cracks, and finally uses the above mechanism to determine the unit damage mechanics model parameters.

[0039] c5 Spatial material point mechanical characteristics determination model: The initial polarity method and moment tensor analysis method (including but not limited to) are used to identify and determine the fracture mechanism (tension, shear, compression) of microcracks (acoustic emission sources), and are further integrated into the discrete element model to characterize the particle-particle mesoscopic failure mode in the discrete element model. In addition, based on the spatial distribution characteristics of characteristic parameters including but not limited to dissipated energy, displacement field, strain field, crack opening, stress field, etc., the material point method is integrated to obtain the spatial material point mechanical characteristics based on the material point method.

[0040] In step 104, the rock fracture mechanics parameters are substituted into the simulation calculation model to predict the expansion of the fracturing crack. Specifically, it includes using acoustic emission and microseismic full waveform analysis methods and full waveform microseismic analysis methods to analyze and monitor the microseismic results in real time, obtain the macroscopic crack trajectory and morphology of the fracturing crack, as well as the range and distribution morphology of the secondary cracks around the cracks, convert the microseismic characteristic parameters of the secondary micro-crack area into physical and mechanical parameters, and according to the engineering geological conditions, comprehensively use the finite element method, discrete element method, boundary element method, material point method and other numerical simulation calculation methods, dynamically update the physical and mechanical parameters and engineering conditions in the numerical simulation, perform numerical calculations, and predict the next expansion trajectory and macro and microscopic morphology of the fracturing crack under the current fracturing process engineering parameters. The specific mutual feedback schematic diagram is shown below. Figure 3 As shown, the physical and mechanical parameters are obtained by monitoring the hydraulic fractures, and the engineering parameters output by the numerical simulation model are updated through the physical and mechanical parameters to control the fracture expansion of the hydraulic fractures.

[0041] In step 105, it is determined whether the simulation result of the predicted value of the fracturing crack extension meets the requirements of the fracturing design; if it does not meet the requirements of the fracturing design, the engineering condition parameters are adjusted until the simulation result of the predicted value of the fracturing crack extension meets the requirements of the fracturing design.

[0042] Specifically, it is determined whether the dynamically updated numerical simulation results of fracturing crack expansion meet the requirements of fracturing design. When they do not meet the requirements, a deep learning method is used to train engineering parameters corresponding to different processes, including but not limited to displacement, viscosity, fracturing fluid type, liquid volume, proppant scale type; elastic parameters, plasticity, strength, viscosity, fracture and other solid mechanics parameters, permeability, porosity, filtration and other physical parameters related to fluid seepage flow, and other engineering parameters to obtain engineering conditions that are conducive to meeting the requirements of fracturing crack expansion design. The numerical simulation model after the current physical and mechanical parameters are dynamically updated is further used to calculate the expansion law of fracturing cracks under multiple sets of engineering conditions, select the engineering conditions corresponding to the optimal results, and make dynamic, real-time and intelligent adjustments.

[0043] In one embodiment, in the n+1th prediction, the fracture area screened out for the nth time is used as the encrypted trial calculation area and fine partition of the initial value of the source location for the n+1th time, wherein n is an integer ≥ 1. The accuracy of the source inversion and the richness of the microseismic characteristic parameters can be improved. In addition, in the whole process of the expansion of the fracturing crack, the minimum analysis step length of the dynamic expansion of the fracturing crack is determined based on the minimum number of sources required to obtain the correlation between the microseismic characteristic parameters and the geomechanical parameters, and the above steps 101-104 are repeated until the reservoir fracturing transformation is completed according to the engineering requirements in step 105, so as to achieve more accurate prediction and monitoring. Specifically, the negative feedback link in this embodiment is as follows: Figure 4 As shown, Figure 4 The dynamic, real-time and intelligent adjustment process can be clearly reflected through a judgment process and two negative feedback substitution processes.

[0044] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited thereto. Various changes that can be made within the knowledge scope of technicians in the relevant technical field without departing from the spirit of the present invention are all within the protection scope of the claims of the present invention.

[0045] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for dynamic measurement and control of fracturing crack expansion with mutual feedback of microseismic and numerical simulation, characterized in that: include: Construct a simulation calculation model for fracturing crack expansion; Monitoring microseismic and acoustic emission waveforms, obtaining spatial distribution of earthquake sources and microseismic characteristic parameters according to the microseismic waveforms and generating distribution laws, and screening out fracture areas according to the distribution laws; According to the correlation model between the microseismic characteristic parameters of the fracture zone and the nonlinear fracture characteristics of the rock, the spatial distribution of the rock fracture mechanics parameters in the fracture zone is determined; Substituting the rock fracture mechanics parameters into the simulation calculation model to predict the expansion of the fracturing crack.

2. The method for dynamic monitoring and control of fracturing crack expansion with mutual feedback of microseismic and numerical simulation according to claim 1 is characterized in that: The simulation calculation model includes engineering conditions, physical and mechanical parameters and a calculation function; the calculation function calculates the physical and mechanical parameters according to the engineering conditions.

3. The method for dynamic monitoring and control of fracturing crack expansion with mutual feedback of microseismic and numerical simulation according to claim 1 is characterized in that: The spatial distribution of the earthquake source and microseismic characteristic parameters is obtained according to the microseismic and acoustic emission waveforms, and the distribution law is generated, including: Locating the earthquake source using acoustic emission and microseismic location methods based on full waveform analysis; Based on the spatial coordinates of the location point of the earthquake source, the waveform and event evolution characteristics of the acoustic emission, the waveform and event evolution characteristics of the microseismic are analyzed and calculated to obtain microseismic characteristic parameters characterizing the characteristics of the acoustic emission and the microseismic, wherein the microseismic characteristic parameters include the density of earthquake source events and the energy of the earthquake source; Identify areas where the density of earthquake source events and the density of earthquake source energy distributed in space are greater than a preset threshold, and obtain the distribution law based on the areas.

4. The method for dynamic monitoring and control of fracturing crack expansion with mutual feedback of microseismic and numerical simulation according to claim 3 is characterized in that: The identifying of an area where the density of earthquake source events and the density of earthquake source energy distribution in space are greater than a preset threshold, and obtaining the distribution law according to the area, includes: Constructing a spatial unit in the microseismic space; The accumulated amount of earthquake source energy and accumulated amount of earthquake source events in each unit are counted in turn; The peak point of the sum of the accumulated source energy and the accumulated source events is taken as the central source point, and the source energy and source events are counted around the microseismic space. Compare the sum of the accumulated amount of earthquake source energy and the accumulated amount of earthquake source events in each unit with a preset threshold, and select the area where the unit with a value greater than the preset threshold is located; The distribution law of earthquake source energy and earthquake source events is obtained according to the area.

5. The method for dynamic monitoring and control of fracturing crack extension with mutual feedback of microseismic and numerical simulation according to claim 1 is characterized in that: The method of determining the spatial distribution of rock fracture mechanics parameters in the fracture zone according to the correlation model between the microseismic characteristic parameters of the fracture zone and the nonlinear fracture characteristics of the rock comprises: Construct a correlation model between microseismic characteristic parameters in the fracture zone and rock fracture and rupture characteristics; Using acoustic emission and microseismic full waveform analysis methods to analyze and monitor the microseismic results of the fracture zone in real time, and obtain microseismic characteristic parameters of the fracture zone; Inputting the microseismic characteristic parameters into the correlation model to obtain the spatial distribution of rock fracture mechanics parameters in the fracture zone; The correlation model includes a rock fracture model, a fracturing crack extension physical model, a time-dependent rock subcritical crack extension model, a unit damage mechanics model, and a spatial material point mechanical characteristics determination model.

6. The method for dynamic monitoring and control of fracturing crack expansion with mutual feedback of microseismic and numerical simulation according to claim 1 is characterized in that: After the prediction of the expansion of the hydraulic fracture, the following is also included: Determining whether the simulation result of the predicted numerical value of the fracturing crack extension meets the requirements of the fracturing design; When the fracturing design requirements are not met, the engineering condition parameters are adjusted until the simulation result of the predicted numerical value of the fracturing crack extension meets the fracturing design requirements.

7. The method for dynamic monitoring and control of fracturing crack extension with mutual feedback of microseismic and numerical simulation according to claim 1 is characterized in that: Also includes: In the n+1th prediction, the fracture area screened out for the nth time is used as the encrypted trial calculation area and fine partitioning area for the n+1th earthquake source location initial value, where n is an integer ≥1.

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