Method and system for real-time evaluation of potential catastrophic risks to optimize fracturing operation parameters

Abstract

The present invention discloses a method and system for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters, which relates to the field of shale gas exploitation. The method comprises: obtaining seismic data in the process of shale gas exploitation in the target area in real time through near-field monitoring; locating each seismic event according to the seismic data; obtaining production factor data of the construction unit in the process of shale gas exploitation in the target area and casing deformation data occurring in the process of single well fracturing in real time; screening seismic events that meet the set conditions based on a spatiotemporal relationship fitter; calculating the linear correlation coefficient, nonlinear correlation coefficient and regression coefficient of each production factor data; calculating the importance weight of each production factor data; determining the main control production factor data; determining the risk threshold based on a multi-field coupling numerical model; and evaluating the potential seismic risk in real time based on the main control production factor data. The present invention can reduce the potential earthquake and casing deformation risks in the process of shale gas exploitation.

Description

Technical Field

[0001] The present invention relates to the field of shale gas exploitation, and in particular to a method and system for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters. Background Art

[0002] Hydraulic fracturing technology is one of the most critical technologies in the development of strategic clean energy shale gas. However, in recent years, with the rapid increase in the scale of shale gas extraction by fracturing, many earthquake events and casing deformation induced by hydraulic fracturing have been widely observed. Some large-magnitude events (greater than magnitude 3) and frequent casing deformation phenomena have caused huge economic losses and social impacts, attracting great attention from the scientific and industrial communities. How to reduce the potential risks of induced earthquakes and casing deformation, and how to reduce this risk by adjusting industrial production strategies in real time and optimizing fracturing construction parameters have become issues that need to be urgently addressed. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters, which can reduce the potential earthquake and casing change risks in the shale gas production process.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for real-time evaluation of potential catastrophic risks to optimize fracturing operation parameters, the method comprising:

[0006] Taking the target shale gas development platform as the center, the seismic data of the shale gas exploitation process in the target area is obtained in real time through near-field monitoring; the seismic data of the target area is the three-component continuous waveform data monitored by the seismic monitoring equipment;

[0007] According to the seismic data, each seismic event is located to obtain cluster information of each seismic event, and the spatial distribution form and geometric size of the fracture and the spatial distribution form and geometric size of the fault are inferred according to the cluster information;

[0008] Real-time acquisition of production factor data of the construction unit during shale gas exploitation in the target area and casing deformation data occurring during single well fracturing; the production factor data include fluid injection volume, fluid injection rate, real-time bottom hole pressure, fluid return volume and injected fluid properties; the casing deformation data include casing change time, casing change position, casing deformation amount, casing inner diameter and maximum diameter of mill shoe;

[0009] Constructing a time-space relationship fitter, and based on the cluster information of each earthquake event, screening earthquake events that meet the set conditions based on the time-space relationship fitter to obtain an induced earthquake time series data set;

[0010] Calculating a first linear correlation coefficient and a first nonlinear correlation coefficient between each of the production factor data and the induced seismic time series data set, and calculating a second linear correlation coefficient and a second nonlinear correlation coefficient between each of the production factor data and the casing deformation data;

[0011] Calculating the first regression coefficient of each of the production factor data and the induced earthquake time series data set and calculating the second regression coefficient of each of the production factor data and the casing deformation data using a multivariate regression model;

[0012] Calculate the first importance weight of each of the production factor data to the induced earthquake time series data set by weighted average according to the first linear correlation coefficient, the first nonlinear correlation coefficient and the first regression coefficient;

[0013] Calculating the second importance weight of each of the production factor data to the casing deformation data by weighted average according to the second linear correlation coefficient, the second nonlinear correlation coefficient and the second regression coefficient;

[0014] Determining the master-controlled production factor data according to the first importance weight and the second importance weight;

[0015] Constructing a multi-field coupling numerical model, and determining the risk threshold of the main production factor in the target area based on the multi-field coupling numerical model; the multi-field coupling numerical model includes a three-dimensional geological structure model containing fault risk parameters and a multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field;

[0016] A real-time assessment of potential earthquake risks is performed based on the risk threshold of the main controlled production factors.

[0017] Optionally, locating each seismic event according to the seismic data of the target area to obtain cluster information of each seismic event, and inferring the spatial distribution form and geometric dimensions of the fractures and the spatial distribution form and geometric dimensions of the faults according to the cluster information, specifically includes:

[0018] Using bandpass filtering and wavelet transform methods to perform denoising on the three-component continuous waveform data to obtain a high signal-to-noise ratio continuous signal;

[0019] Extracting three-component waveform signals of each earthquake event according to the high signal-to-noise ratio continuous signal;

[0020] According to the three-component waveform signal of each seismic event, based on a grid search method, determining the absolute location of each seismic event;

[0021] According to the absolute positioning, based on a double-difference positioning algorithm, the relative positioning of each earthquake event is determined;

[0022] According to the relative positioning of each earthquake event, based on the cluster distribution of earthquakes, cluster information of each earthquake event is determined;

[0023] The spatial distribution morphology and geometric dimensions of the cracks and the spatial distribution morphology and geometric dimensions of the faults are inferred based on the cluster information.

[0024] Optionally, the calculating of the first linear correlation coefficient and the first nonlinear correlation coefficient between each of the production factor data and the induced seismic time series data set and the calculating of the second linear correlation coefficient and the second nonlinear correlation coefficient between each of the production factor data and the casing deformation data specifically includes:

[0025] Using the Pearson correlation coefficient method, a first global synchronous Pearson correlation coefficient between each of the production factor data and the induced seismic time series data set and a second global synchronous Pearson correlation coefficient between each of the production factor data and the casing deformation data are calculated;

[0026] According to the first global synchronous Pearson correlation coefficient, a first set time window is selected, and a local synchronous Pearson correlation coefficient is calculated to obtain a first linear correlation coefficient;

[0027] According to the second global synchronous Pearson correlation coefficient, a second set time window is selected, and a local synchronous Pearson correlation coefficient is calculated to obtain a second linear correlation coefficient;

[0028] Calculating a first penalty function value according to each of the production factor data and the induced earthquake time series data set, and calculating a second penalty function value according to each of the production factor data and the casing deformation data;

[0029] According to the first penalty function value, applying an adaptive dynamic time bending algorithm, calculating a first nonlinear correlation coefficient between each of the production factor data and the induced earthquake time series data set;

[0030] According to the second penalty function value, an adaptive dynamic time warping algorithm is applied to calculate a second nonlinear correlation coefficient between each of the production factor data and the casing deformation data.

[0031] Optionally, the constructing of a multi-field coupling numerical model to determine the risk threshold of the main controlled production factor in the target area specifically includes:

[0032] Obtaining surface structural information of geological surveys in the target area, geophysical exploration information in the target area, and drilling information in the target area;

[0033] Constructing a three-dimensional seismic structural model of the target area according to the cluster information of each seismic event, the surface structural information, the geophysical exploration information of the target area and the drilling information of the target area; the three-dimensional seismic structural model of the target area includes a three-dimensional geological model of an activated fault and a three-dimensional geological model of a fault to be activated;

[0034] According to the geometrical morphology of the three-dimensional geological model of the activated fault and the three-dimensional geological model of the fault to be activated, the slip trend and risk value of each fault are calculated by applying principal stress parameters and quantitative risk assessment method;

[0035] According to the slip trend and risk value of each fault and the three-dimensional seismic structural model of the target area, a three-dimensional geological structural model including fault risk parameters is obtained;

[0036] Inputting the main control production factor data as a disturbance item of the regional stress field into the three-dimensional geological structure model containing fault risk parameters to obtain the inverted stress field distribution of the earthquake source area;

[0037] Using elastic equations, a hydraulic fracture propagation model based on the main controlling production factors is constructed;

[0038] Calculating the flow field in the hydraulic fracture after the hydraulic fracture is initiated according to the main control production factor data and the hydraulic fracture expansion model based on the main control production factor;

[0039] Construct the multi-field coupled constitutive equation of hydraulic fracturing flow field-fault slip force field;

[0040] According to the multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field, the inversion source area stress field distribution and the flow field in the fracture after the hydraulic fracture is initiated, the risk threshold of the main controlled production factor in the target area is determined through forward simulation.

[0041] Optionally, the risk threshold of the main controlled production factor in the target area is determined by forward simulation based on the multi-field coupled constitutive equation of hydraulic fracturing flow field-fault slip force field, the inverted stress field distribution in the source area and the flow field in the fracture after the hydraulic fracture is initiated, specifically including:

[0042] According to the multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field, the inverted stress field distribution of the source area, the flow field in the fracture after the hydraulic fracture is initiated, and the three-dimensional geological structure model containing fault risk parameters, the theoretical main control production factors are determined through forward simulation;

[0043] When the theoretical master-controlled production factor and the master-controlled production factor are inconsistent, the master-controlled production factor and the theoretical master-controlled production factor are updated according to the current target area seismic data and production factor data to obtain updated master-controlled production factors and updated theoretical master-controlled production factors;

[0044] When the theoretical main controlled production factor is consistent with the main controlled production factor, the risk threshold of the main controlled production factor in the target area is determined.

[0045] Optionally, the method further comprises:

[0046] According to the risk threshold of the main controlled production factor, the main controlled production factor data is adjusted so that the main controlled production factor data is less than the risk threshold.

[0047] Optionally, the real-time assessment of potential earthquake risks based on the master-controlled production factor data specifically includes:

[0048] When the main controlled production factor data is less than the risk threshold, there is no potential earthquake risk during shale gas exploitation in the target area;

[0049] When the main controlled production factor data is not less than the risk threshold, there is a potential earthquake risk in the shale gas exploitation process in the target area.

[0050] A system for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters is applied to the above-mentioned method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters, and the system comprises:

[0051] The first acquisition module is used to acquire the seismic data of the shale gas exploitation process in the target area in real time through near-field monitoring, with the target shale gas development platform as the center; the seismic data of the target area is the three-component continuous waveform data monitored by the seismic monitoring equipment;

[0052] A positioning module, used to locate each seismic event according to the seismic data, obtain cluster information of each seismic event, and infer the spatial distribution form and geometric size of the cracks and the spatial distribution form and geometric size of the faults according to the cluster information;

[0053] The second acquisition module is used to obtain in real time the production factor data of the construction unit during the shale gas exploitation in the target area and the casing deformation data occurring during the single well fracturing process; the production factor data include the fluid injection volume, fluid injection rate, real-time bottom hole pressure, fluid return volume and injected fluid properties; the casing deformation data include casing change time, casing change position, casing deformation amount, casing inner diameter and maximum diameter of the mill shoe;

[0054] A screening module is used to construct a time-space relationship fitter, and based on the cluster information of each earthquake event, screen earthquake events that meet the set conditions based on the time-space relationship fitter to obtain an induced earthquake time series data set;

[0055] A first calculation module, used for calculating a first linear correlation coefficient and a first nonlinear correlation coefficient between each of the production factor data and the induced seismic time series data set, and calculating a second linear correlation coefficient and a second nonlinear correlation coefficient between each of the production factor data and the casing deformation data;

[0056] A second calculation module, for calculating the first regression coefficient of each of the production factor data and the induced earthquake time series data set and calculating the second regression coefficient of each of the production factor data and the casing deformation data by using a multivariate regression model;

[0057] A third calculation module, configured to calculate a first importance weight of each of the production factor data to the induced earthquake time series data set by weighted average according to the first linear correlation coefficient, the first nonlinear correlation coefficient and the first regression coefficient;

[0058] a fourth calculation module, configured to calculate, according to the second linear correlation coefficient, the second nonlinear correlation coefficient and the second regression coefficient, a second importance weight of each of the production factor data to the casing deformation data by weighted average;

[0059] A main control production factor determination module, used to determine the main control production factor data according to the first importance weight and the second importance weight;

[0060] A risk threshold determination module is used to construct a multi-field coupling numerical model and determine the risk threshold of the main control production factor in the target area based on the multi-field coupling numerical model; the multi-field coupling numerical model includes a three-dimensional geological structure model containing fault risk parameters and a multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field;

[0061] An assessment module is used to conduct real-time assessment of potential earthquake risks based on the risk threshold of the main controlled production factors.

[0062] An electronic device includes a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-mentioned method of real-time assessment of potential catastrophic risks to optimize fracturing construction parameters.

[0063] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters.

[0064] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0065] The present invention provides a method for real-time assessment of potential disaster risks to optimize fracturing construction parameters, comprising: taking a target shale gas development platform as the center, obtaining real-time seismic data in the shale gas production process in the target area through near-field monitoring; locating each seismic event according to the seismic data, obtaining cluster information of each seismic event, and inferring the spatial distribution morphology and geometric dimensions of the cracks and the spatial distribution morphology and geometric dimensions of the faults according to the cluster information; obtaining real-time production factor data of the construction unit in the shale gas production process in the target area and casing deformation data occurring during the single well fracturing process; constructing a spatiotemporal relationship fitter, and based on the evolution information of each seismic event, screening seismic events that meet the set conditions based on the spatiotemporal relationship fitter to obtain an induced seismic time series data set; calculating the first linear correlation coefficient and the first nonlinear correlation coefficient of each production factor data and the induced seismic time series data set, and calculating the first linear correlation coefficient and the first nonlinear correlation coefficient of each production factor data and the induced seismic time series data set. The second linear correlation coefficient and the second nonlinear correlation coefficient of the casing deformation data; the first regression coefficient of each production factor data and the induced earthquake time series data set is calculated by using a multivariate regression model, and the second regression coefficient of each production factor data and the casing deformation data is calculated; according to the first linear correlation coefficient, the first nonlinear correlation coefficient and the first regression coefficient, the first importance weight of each production factor data to the induced earthquake time series data set is calculated by weighted average; according to the second linear correlation coefficient, the second nonlinear correlation coefficient and the second regression coefficient, the second importance weight of each production factor data to the casing deformation data is calculated by weighted average; according to the first importance weight and the second importance weight, the main control production factor data is determined; a multi-field coupling numerical model is constructed, and the risk threshold of the main control production factor of the target area is determined based on the multi-field coupling numerical model; based on the main control production factor data, the potential earthquake risk is evaluated in real time. The present invention determines the main control production factor data, determines the risk threshold of the main control production factor data, and performs real-time regulation on the main control production factor data according to the risk threshold of the main control production factor data, reduces the potential risk of earthquake and casing deformation, and provides a key scientific basis for the safe and green development of the shale gas industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0067] Figure 1 A flow chart of a method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters provided by the present invention;

[0068] Figure 2 A technical roadmap provided for the present invention;

[0069] Figure 3 A spatial constraint diagram provided for the present invention;

[0070] Figure 4 A real-time evolution diagram of the Pearson correlation coefficient provided by the present invention;

[0071] Figure 5 A system module diagram for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters provided by the present invention.

[0072] Explanation of symbols:

[0073] 1-first acquisition module, 2-positioning module, 3-second acquisition module, 4-screening module, 5-first calculation module, 6-second calculation module, 7-third calculation module, 8-fourth calculation module, 9-main control production factor determination module, 10-risk threshold determination module, 11-evaluation module. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] The purpose of the present invention is to provide a method and system for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters, which can reduce the potential earthquake and casing change risks in the shale gas production process.

[0076] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0077] Embodiment 1

[0078] like Figure 1 and Figure 2 As shown, the present invention provides a method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters, the method comprising:

[0079] Step S1: With the target shale gas development platform as the center, the seismic data of the shale gas exploitation process in the target area is acquired in real time through near-field monitoring; the seismic data of the target area is three-component continuous waveform data monitored by the seismic monitoring equipment.

[0080] In actual application, with the target shale gas development platform as the center, 12-15 short-period seismic monitoring devices are deployed with a radius of 2 km, 5 km and 10 km to monitor the micro-earthquakes and moderate-strong earthquakes during the entire hydraulic fracturing process in real time. The seismic data obtained by monitoring specifically refers to the three-component continuous waveform data in the horizontal direction (NS and WE direction) and vertical direction (Z direction) received by each monitoring station, that is, time series data containing continuous amplitude information and velocity information, and its frequency range is generally within 10-100Hz.

[0081] Step S2: locate each seismic event according to the seismic data, obtain cluster information of each seismic event, and infer the spatial distribution morphology and geometric dimensions of the cracks and the spatial distribution morphology and geometric dimensions of the faults according to the cluster information.

[0082] S2 specifically includes:

[0083] Step S21: using a bandpass filtering method and a wavelet transform method to perform denoising on the three-component continuous waveform data to obtain a high signal-to-noise ratio continuous signal.

[0084] Specifically, the frequency characteristics of the bandpass filter are:

[0085] Among them, ω 1 is the lower cutoff frequency, ω 2 is the upper cutoff frequency, ω is the cutoff frequency, H d is the frequency response function.

[0086] Step S22: extracting the three-component waveform signal of each earthquake event according to the high signal-to-noise ratio continuous signal. In practical applications, the long-short time window method is used to extract the three-component waveform signal of a single earthquake event.

[0087] Step S23: Determine the absolute location of each seismic event based on the three-component waveform signal of each seismic event and the grid search method.

[0088] Step S24: According to the absolute positioning, based on a double-difference positioning algorithm, the relative positioning of each earthquake event is determined.

[0089] Step S25: According to the relative positioning of each earthquake event and based on the cluster distribution of earthquakes, cluster information of each earthquake event is determined.

[0090] Step S26: Inferring the spatial distribution morphology and geometric dimensions of cracks and the spatial distribution morphology and geometric dimensions of faults based on the cluster information.

[0091] In practical applications, the spatial distribution morphology and geometric dimensions of cracks can be inferred based on the clustered distribution of earthquakes. The mainstream earthquake positioning methods include absolute positioning and relative positioning algorithms. The following method is used to accurately locate earthquakes: first, absolute positioning based on the grid search method is performed, and then relative positioning based on the double difference positioning algorithm is performed. For traditional earthquake positioning, there are two main factors that affect the accuracy of earthquake positioning, one is the error in picking up the earthquake phase arrival time, and the other is the error in the velocity model. The present invention uses a double difference positioning method for accurate earthquake positioning. Double difference earthquake relative positioning can overcome these two shortcomings of traditional earthquake positioning to some extent. Double difference positioning can directly use the waveform cross-correlation method to obtain accurate arrival time difference, and for adjacent earthquakes, the propagation paths almost overlap outside the source area, so the influence of model errors outside the source area can be partially eliminated through the "differential" process.

[0092] Step S3: Real-time acquisition of production factor data of the construction unit during shale gas exploitation in the target area and casing deformation data occurring during single well fracturing; the production factor data include fluid injection volume, fluid injection rate, real-time bottom hole pressure, fluid return volume and injected fluid properties; the casing deformation data include casing change time, casing change position, casing deformation amount, casing inner diameter and maximum diameter of mill shoe.

[0093] In actual applications, the production factor data of the construction unit in the process of shale gas exploitation in the target area is the shale gas fracturing construction data, that is, collecting and organizing various real-time industrial parameters in the shale gas fracturing exploitation process, including but not limited to the accurate spatial position of the fracturing platform, the precise fracturing time of each well section, the volume and rate of single-stage injected fluid, wellhead pressure, pump pressure and backflow volume, etc.

[0094] Step S4: construct a spatiotemporal relationship fitter, and according to the cluster information of each earthquake event, screen earthquake events that meet the set conditions based on the spatiotemporal relationship fitter to obtain an induced earthquake time series data set.

[0095] Specifically, Figure 3 As shown in the figure, a spatiotemporal relationship fitter for screening induced earthquakes was designed: in terms of space, the horizontal direction is constrained by 2000m from the fracturing section, and the vertical direction is constrained by 10000m from the sea level; in terms of time, the first fracturing is used as the starting point, and the end point is 10 days after the end of the final fracturing. Figure 3 (a) is a three-dimensional schematic diagram of the spatial range of the fitter on one side of the platform. Figure 3 (b) is the corresponding top view. Figure 3 (c) is a three-dimensional schematic diagram of the spatial range of the fitter for a single horizontal well. Figure 3(d) in the figure is the corresponding top view. The induced earthquake time series data set x corresponding to each fracturing platform, each horizontal well, and each fracturing section is screened out according to the above fitter. The induced earthquake time series data set x includes time, location, and magnitude.

[0096] Step S5: Calculate the first linear correlation coefficient and the first nonlinear correlation coefficient between each of the production factor data and the induced seismic time series data set, and calculate the second linear correlation coefficient and the second nonlinear correlation coefficient between each of the production factor data and the casing deformation data.

[0097] S5 specifically includes:

[0098] Step S51: using the Pearson correlation coefficient method, calculate the first global synchronous Pearson correlation coefficient between each of the production factor data and the induced seismic time series data set and the second global synchronous Pearson correlation coefficient between each of the production factor data and the casing deformation data.

[0099] Step S52: According to the first global synchronous Pearson correlation coefficient, a first set time window is selected, and a local synchronous Pearson correlation coefficient is calculated to obtain a first linear correlation coefficient.

[0100] Step S53: According to the second global synchronous Pearson correlation coefficient, a second set time window is selected, and a local synchronous Pearson correlation coefficient is calculated to obtain a second linear correlation coefficient.

[0101] In practical applications, the Pearson correlation coefficient method is used to analyze the linear correlation between all shale gas fracturing production factor time series and induced earthquake time series. The Pearson coefficient is used to describe the degree and direction of linear correlation between two continuous series, and the value range is [-1,1]. The Pearson correlation coefficient of two time series samples is the product of their covariance divided by the standard deviation, which is calculated as follows:

[0102]

[0103] Where x and y represent two sets of time series data, r(x, y) is the Pearson correlation coefficient, cov(x, y) is the standard deviation, E is the mathematical expectation, and σ is the variance. In the present invention, x represents the earthquake event or earthquake energy sequence selected by the spatiotemporal relationship fitter, y i Represents the construction elements of shale gas fracturing, where i=1,2,…5 correspond to fluid injection volume, injection rate, real-time bottom hole pressure / pump pressure, fluid return volume, and injected fluid properties (quartz sand content, viscosity, density, etc.).

[0104] First, the global synchronization Pearson correlation coefficient is calculated, and then an appropriate time window (such as 1 minute) is selected to calculate the local synchronization Pearson correlation coefficient, and the linear correlation characteristics that change over time are obtained, such as Figure 4 As shown in the figure, the Pearson correlation coefficient r(x,y) between the earthquake sequence and the time series of multiple shale gas construction elements is obtained. i ).

[0105] Step S54: Calculate a first penalty function value according to each of the production factor data and the induced earthquake time series data set, and calculate a second penalty function value according to each of the production factor data and the casing deformation data.

[0106] Step S55: According to the first penalty function value, an adaptive dynamic time warping algorithm is applied to calculate a first nonlinear correlation coefficient between each of the production factor data and the induced earthquake time series data set.

[0107] Step S56: according to the second penalty function value, applying an adaptive dynamic time warping algorithm, calculating a second nonlinear correlation coefficient between each of the production factor data and the casing deformation data.

[0108] In practical applications, the adaptive dynamic time warping algorithm (ACDTW) is used to calculate the similarity / correlation of two nonlinear time series variables.

[0109] The first step is to limit the number of repetitions of sequence points by constructing a penalty function. This step includes two cases: the first is that the lengths of the two nonlinear time series variables are equal; the second is that the lengths of the two nonlinear time series variables are not equal.

[0110] For the first case: First penalty function (for time series of equal length):

[0111] Among them, m, n represent the sequence length, N(x i,j ) is the number of times the sequence point is used.

[0112] For the second case: Second penalty function (for time series of unequal lengths):

[0113] in,

[0114] m, n represents the sequence length, N(x i,j ) is the number of times the sequence point is used.

[0115] The second step is to calculate the ACDTW value based on the penalty function value:

[0116] ACDTW(1,1)=d 1,1

[0117]

[0118] Among them, d i,j Indicates the Euclidean distance between point i and point j. This step obtains the ACDTW similarity between the earthquake sequence and the time series of multiple shale gas construction elements (ACDTW(x,y i ).

[0119] Step S6: using a multivariate regression model to calculate the first regression coefficient of each of the production factor data and the induced earthquake time series data set, and to calculate the second regression coefficient of each of the production factor data and the casing deformation data.

[0120] In practical applications, linear / nonlinear regression analysis is used to quantify the importance weight of the seismic activity sequence corresponding to the fracturing production indicator sequence.

[0121] The first step is to build a multiple regression model:

[0122] Where x is the earthquake sequence dataset, y i are multiple fracturing production factor indicators, β 0 ,β 1 ,…β m is the regression coefficient. Get n independent observation data [b i ,a i1 ,…,a im ], where b i is the observed value of x, a i1 ,…,a im They are y 1 ,…,y m Observed values, i = 1, 2, ..., m, we get:

[0123]

[0124] ε=[ε 1 , …, ε n ] T , β=[β 0 , β 1 , …, β m ] T ;

[0125] The multiple regression model can be simplified as: Where E n is the n-th order identity matrix.

[0126] The parameter β in the regression model is estimated using the least squares method, that is, the estimated value is selected when When , the error square sum Q:

[0127]

[0128] Q reaches the minimum, let get:

[0129]

[0130] After sorting, we get the normal equation system:

[0131]

[0132] Its matrix form and the solution of β are: and

[0133] Thus, the regression coefficient β between the earthquake sequence and the time series of multiple shale gas construction elements is obtained. i and a second regression coefficient of each of the production factor data and the casing deformation data.

[0134] Step S7: Calculate the first importance weight of each of the production factor data to the induced earthquake time series data set by weighted average according to the first linear correlation coefficient, the first nonlinear correlation coefficient and the first regression coefficient.

[0135] Step S8: Calculate the second importance weight of each of the production factor data to the casing deformation data by weighted average according to the second linear correlation coefficient, the second nonlinear correlation coefficient and the second regression coefficient.

[0136] In practical applications, the similarity / correlation coefficient (i.e., r(x, y)) obtained in step S5 and step S6 is i ),ACDTW(x,y i ) and β i ), and use weighted average to calculate the first importance weight value of a certain fracturing production factor to the seismic activity and the second importance weight value of each of the production factor data to the casing deformation data.

[0137] Step S9: Determine the master production factor data according to the first importance weight and the second importance weight.

[0138] Specifically, the production factor data corresponding to the first importance weight and the second importance weight that meet the set threshold range are the main control production factor data. i Includes at least one of the production factor data.

[0139] Step S10: construct a multi-field coupling numerical model, and determine the risk threshold of the main controlling production factor in the target area based on the multi-field coupling numerical model; the multi-field coupling numerical model includes a three-dimensional geological structure model containing fault risk parameters and a multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field.

[0140] S10 specifically includes:

[0141] Step S101: Acquire surface structure information of geological survey of target area, geophysical exploration information of target area and drilling information of target area.

[0142] Step S102: Based on the cluster information of each earthquake event, the surface structure information, the geophysical exploration information of the target area and the drilling information of the target area, GoCAD and Comsol software are used to construct a three-dimensional seismic structural model of the target area; the three-dimensional seismic structural model of the target area includes a three-dimensional geological model of the activated fault and a three-dimensional geological model of the fault to be activated.

[0143] In practical applications, a three-dimensional earthquake structural model of the target area is established by combining the underground hidden faults and folds analyzed from geophysical exploration and drilling data, the fault information identified by precise earthquake positioning, and the surface structural information from regional geological survey data. The three-dimensional model is used to analyze and interpret the distribution and structural causes of earthquake clusters in the crust, and update the hidden fault information; the stress field distribution in the source area is inverted through the source mechanism and its temporal and spatial evolution laws are explored to reveal the dynamic characteristics and causes of earthquake ruptures.

[0144] Step S103: According to the geometric shapes of the three-dimensional geological model of the activated fault and the three-dimensional geological model of the fault to be activated, the slip tendency (Slip Tendency) and risk value of each fault are calculated based on principal stress parameters and quantitative risk assessment (QRA).

[0145] Step S104: obtaining a three-dimensional geological structure model including fault risk parameters according to the slip trend and risk value of each fault and the three-dimensional seismic structure model of the target area.

[0146] Step S105: inputting the main control production factor data as the disturbance item of the regional stress field into the three-dimensional geological structure model containing fault risk parameters to obtain the inverted stress field distribution of the earthquake source area.

[0147] Step S106: Using the elastic equation, construct a hydraulic fracture expansion model based on the main controlled production factors.

[0148] In practical applications, classical linear elastic fracture mechanics is used to establish the propagation criterion of quasi-static hydraulic fractures: K I =K Ic ; Among them, KI is the stress intensity factor (the inverse of the square root of the crack tip singular stress), K Ic is the fracture toughness.

[0149] The elastic equation in linear elastic fracture mechanics is used to calculate the crack width caused by the net pressure (local fluid pressure minus the confining pressure of the local rock formation) at each point on the crack trajectory:

[0150] Cw=∫ Ω(t) C(x,y;ξ,η)w(ξ,η,t)dξdη=p(x,y,t)-σ c (x, y); where P(t) is the fluid pressure in the fracture, σ c is the minimum value of the local in-situ stress, w(t) is the crack width, the function C contains all the material information of the layered elastic medium, and Ω(t) is the area occupied by the crack at time t.

[0151] The fluid pressure P(t), slit width w(t) and slit length L(t) are approximately estimated by the PKN equation:

[0152]

[0153] Where L(t) is the half length of the fracture at time t, W(t) is the width of the fracture at time t, P(t) is the initial pressure in the fracture at time t, G is the shear modulus, and q o is the injection rate, μ is the viscosity of the injected fluid, and h is the height of the fracture. Thus, a hydraulic fracture expansion model based on the main controlling production factors is obtained.

[0154] Step S107: Calculate the flow field in the hydraulic fracture after the hydraulic fracture is initiated according to the main control production factor data and the hydraulic fracture expansion model based on the main control production factor.

[0155] In practical applications, a flow field will be generated in the hydraulic fracture after it is initiated, which can be described by the fluid Reynold equation as follows: Where ρ is the fluid density, g is the gravitational acceleration vector, δ is the Dirac function, Q is the source injection rate, and for Newtonian fluids, D(w) = w 3 / 12μ.

[0156] Step S108: constructing a multi-field coupled constitutive equation of hydraulic fracturing flow field-fault slip force field.

[0157] Specifically, the multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field is:

[0158]

[0159] q i =-k il·k*(s)[p-ρ f x j g j ];

[0160] The first equation gives the relationship between the volume strain of the fluid and the pore pressure. Where p is the pore pressure, s is the fluid saturation, ε is the volume strain, T is the temperature, M is the Biot modulus, n is the porosity, α is the Biot coefficient, and β is the undrained thermal coefficient. The second equation describes the stress-strain response between the fluid and the rock skeleton in the porous medium, where σ ij * is the stress change rate, δ ij is the Kronecker symbol, H is the function describing the constitutive relation, κ is the historical stress loading parameter, σ ij represents stress, ξ ij represents strain. The third equation is the fluid transport equation, which obeys Darcy's law. i is the discharge vector of a particular fluid, k is the absolute permeability tensor of the medium, k*(s) is the relative permeability as a function of the fluid saturation s, and ρ f is the fluid density, g i (i=1, 2, 3) represents the three components of the gravity vector.

[0161] Step S109: Determine the risk threshold of the main controlled production factor in the target area through forward simulation based on the multi-field coupled constitutive equation of hydraulic fracturing flow field-fault slip force field, the inverted stress field distribution in the source area and the flow field in the fracture after the hydraulic fracture is initiated.

[0162] S109 specifically includes:

[0163] Step S1091: Determine the theoretical main control production factors through forward simulation based on the multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field, the inverted stress field distribution of the source area, the flow field in the fracture after the hydraulic fracture is initiated, and the three-dimensional geological structure model containing fault risk parameters.

[0164] Step S1092: When the theoretical master control production factor and the master control production factor are inconsistent, the master control production factor and the theoretical master control production factor are updated according to the current target area seismic data and production factor data to obtain updated master control production factors and updated theoretical master control production factors.

[0165] Step S1093: When the theoretical main controlled production factor is consistent with the main controlled production factor, the risk threshold of the main controlled production factor of the target area is determined.

[0166] In practical applications, since fault activation is the fundamental cause of earthquakes and casing deformation, it is necessary to explain the fault slip judgment criteria and risk trend calculation. The start-up criteria for fault slip again obey the Mohr-Coulomb criterion: τ = C 0 +(σ n -P 0 )tanφ.

[0167] This criterion indicates that when the maximum shear stress reaches a certain threshold, the fault will slip unsteadily. This threshold is determined by the cohesion C 0 , internal friction angle and effective normal stress σ n In addition, the fault slip trend T s Indicates the difficulty of activation: T S =τ / σ neff ≥μ S ; Among them, σ neff is the effective normal stress, μ s is the static friction coefficient and τ is the shear stress.

[0168] σ neff The calculation formula of and τ is:

[0169] σ beff =σ 1eff × 2 +σ 2eff ×m 2 +σ 3eff ×n 2 ;

[0170] τ=[(σ 1 -σ 2 ) 2 l 2 m 2 +(σ 2 -σ 3 ) 2 m 2 n 2 +(σ 3 -σ 1 ) 2 l 2 n 2 ] 1 / 2 ; Among them, σ 1 represents the maximum principal stress, σ 2 represents the intermediate principal stress and σ 3 represents the minimum principal stress, l, m, n represent the direction cosines of the fault plane normal.

[0171] The multi-field coupling numerical model constructed by the present invention satisfies the following three equilibrium equations:

[0172] (1) Fluid seepage in porous media satisfies Darcy’s law, and the corresponding equation is:

[0173] q i =-κ il ·k * (s)[p-ρ f x j g j ];

[0174] Among them, q i is the discharge vector of a specific fluid (unit: m 3 / s), κ jl is the absolute permeability tensor of the medium, k*(s) is the relative permeability, which is a function of the fluid saturation s, and ρ f is the fluid density (unit: kg / m 3 ), g i (i=1, 2, 3) represents the three components of the gravity vector.

[0175] (2) For the problem of small deformation, the weight balance equation of the fluid is expressed as: Among them, q i,i is the relative flow vector, the unit is (m / s), q v is the volume flow source intensity and ζ is the fluid volume or fluid capacity per unit volume of porous material.

[0176] (3) Fluid-solid coupling calculation satisfies the law of conservation of mass, and the equation is: Where ρ is the volume density of the model, which consists of two parts and its calculation formula is: ρ = ρ d +nsρ f ; where ρ d is the rock skeleton density, ρ f is the fluid density, n is the porosity, and s is the fluid saturation.

[0177] Step S11: Perform real-time assessment of potential earthquake risks based on the risk threshold of the main controlled production factor.

[0178] In practical applications, multiple production factors are used as disturbance terms of the regional stress field for input, and the constitutive equations and numerical calculation models are constructed based on the penetration and propagation of fracture fluids and the mechanics of fixed rocks and fault mechanics. The mechanical mechanism and actual utility of the main control factors given by the statistical analysis in step S8 in earthquake occurrence and fault activity are verified through forward simulation. The prediction results of earthquake risk and casing deformation are verified by adjusting the input parameters such as injection volume, injection rate, and construction pressure in the multi-field coupling model, and the basis for adjusting the data of the main control production factors is given. Among them, the multi-field coupling model is the model algorithm corresponding to steps S106-S108.

[0179] S11 specifically includes:

[0180] Step S111: When the main controlled production factor data is less than the risk threshold, there is no potential earthquake risk during shale gas exploitation in the target area.

[0181] Step S112: When the main controlled production factor data is not less than the risk threshold, there is a potential earthquake risk in the shale gas production process in the target area.

[0182] In addition, after executing step S10, the method further includes:

[0183] Step S113: According to the risk threshold of the main controlled production factor, the main controlled production factor data is adjusted so that the main controlled production factor data is less than the risk threshold.

[0184] After finding out the main controlling factors of induced earthquakes based on the main controlling production factor data obtained through statistical analysis in step S9, mechanism research, and numerical simulation verification in step S10, an empirical risk threshold applicable to a certain area will be given according to its data characteristics, providing a theoretical basis for the demarcation of the "risk control of induced earthquakes and casing deformation". In actual production, when the risk of induced earthquakes and casing deformation reaches a certain limit, alarm information and mitigation measures are proposed, and a consulting report is formed to guide production.

[0185] Embodiment 2

[0186] In order to execute the method corresponding to the above-mentioned embodiment 1 to achieve the corresponding functions and technical effects, a system for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters is provided below, such as Figure 5 As shown, the system comprises:

[0187] The first acquisition module 1 is used to acquire the seismic data of the shale gas exploitation process in the target area in real time through near-field monitoring with the target shale gas development platform as the center; the seismic data of the target area is the three-component continuous waveform data monitored by the seismic monitoring equipment.

[0188] The positioning module 2 is used to locate each seismic event according to the seismic data, obtain cluster information of each seismic event, and infer the spatial distribution form and geometric dimensions of the cracks and the spatial distribution form and geometric dimensions of the faults according to the cluster information.

[0189] The second acquisition module 3 is used to obtain in real time the production factor data of the construction unit during the shale gas exploitation in the target area and the casing deformation data occurring during the single well fracturing process; the production factor data include the fluid injection volume, fluid injection rate, real-time bottom hole pressure, fluid return volume and injected fluid properties; the casing deformation data include casing change time, casing change position, casing deformation amount, casing inner diameter and maximum diameter of the mill shoe.

[0190] The screening module 4 is used to construct a time-space relationship fitter, and based on the cluster information of each earthquake event, screen earthquake events that meet the set conditions based on the time-space relationship fitter to obtain an induced earthquake time series data set.

[0191] The first calculation module 5 is used to calculate the first linear correlation coefficient and the first nonlinear correlation coefficient between each of the production factor data and the induced seismic time series data set, and to calculate the second linear correlation coefficient and the second nonlinear correlation coefficient between each of the production factor data and the casing deformation data.

[0192] The second calculation module 6 is used to calculate the first regression coefficient of each of the production factor data and the induced earthquake time series data set and to calculate the second regression coefficient of each of the production factor data and the casing deformation data using a multivariate regression model.

[0193] The third calculation module 7 is used to calculate the first importance weight of each of the production factor data to the induced earthquake time series data set by weighted average according to the first linear correlation coefficient, the first nonlinear correlation coefficient and the first regression coefficient.

[0194] The fourth calculation module 8 is used to calculate the second importance weight of each of the production factor data to the casing deformation data by weighted average according to the second linear correlation coefficient, the second nonlinear correlation coefficient and the second regression coefficient.

[0195] The main controlling production factor determination module 9 is used to determine the main controlling production factor data according to the first importance weight and the second importance weight.

[0196] The risk threshold determination module 10 is used to construct a multi-field coupling numerical model and determine the risk threshold of the main controlled production factor in the target area based on the multi-field coupling numerical model; the multi-field coupling numerical model includes a three-dimensional geological structure model containing fault risk parameters and a multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field.

[0197] The evaluation module 11 is used to perform real-time evaluation of potential earthquake risks based on the risk threshold of the main controlled production factors.

[0198] Embodiment 3

[0199] An embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method of real-time assessment of potential catastrophic risks to optimize fracturing construction parameters in embodiment 1.

[0200] Optionally, the above-mentioned electronic device may be a server.

[0201] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of the first embodiment for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters.

[0202] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0203] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for real-time evaluation of potential catastrophic risks to optimize fracturing operation parameters, characterized in that: The method comprises: Taking the target shale gas development platform as the center, the seismic data of the shale gas fracturing process in the target area is obtained in real time through near-field monitoring; the seismic data of the target area is the three-component continuous waveform data monitored by the seismic monitoring equipment; According to the seismic data, each seismic event is located to obtain cluster information of each seismic event, and the spatial distribution form and geometric size of the fracture and the spatial distribution form and geometric size of the fault are inferred according to the cluster information; Real-time acquisition of production factor data of the construction unit during shale gas fracturing in the target area and casing deformation data occurring during single well fracturing; the production factor data include fluid injection volume, fluid injection rate, real-time bottom hole pressure, fluid return volume and injected fluid properties; the casing deformation data include casing change time, casing change position, casing deformation amount, casing inner diameter and maximum diameter of mill shoe; Constructing a time-space relationship fitter, and based on the cluster information of each earthquake event, screening earthquake events that meet the set conditions based on the time-space relationship fitter to obtain an induced earthquake time series data set; Calculating a first linear correlation coefficient and a first nonlinear correlation coefficient between each of the production factor data and the induced seismic time series data set, and calculating a second linear correlation coefficient and a second nonlinear correlation coefficient between each of the production factor data and the casing deformation data; Calculating the first regression coefficient of each of the production factor data and the induced earthquake time series data set and calculating the second regression coefficient of each of the production factor data and the casing deformation data using a multivariate regression model; Calculate the first importance weight of each of the production factor data to the induced earthquake time series data set by weighted average according to the first linear correlation coefficient, the first nonlinear correlation coefficient and the first regression coefficient; Calculating the second importance weight of each of the production factor data to the casing deformation data by weighted average according to the second linear correlation coefficient, the second nonlinear correlation coefficient and the second regression coefficient; Determining the master-controlled production factor data according to the first importance weight and the second importance weight; Constructing a multi-field coupling numerical model, and determining the risk threshold of the main production factor in the target area based on the multi-field coupling numerical model; the multi-field coupling numerical model includes a three-dimensional geological structure model containing fault risk parameters and a multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field; A real-time assessment of potential earthquake risks is performed based on the risk threshold of the main controlled production factors.

2. The method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters according to claim 1, characterized in that: The method of locating each seismic event according to the seismic data of the target area, obtaining cluster information of each seismic event, and inferring the spatial distribution form and geometric size of the fracture and the spatial distribution form and geometric size of the fault according to the cluster information specifically includes: Using bandpass filtering and wavelet transform methods to perform denoising on the three-component continuous waveform data to obtain a high signal-to-noise ratio continuous signal; Extracting three-component waveform signals of each earthquake event according to the high signal-to-noise ratio continuous signal; According to the three-component waveform signal of each seismic event, based on a grid search method, determining the absolute location of each seismic event; According to the absolute positioning, based on a double-difference positioning algorithm, the relative positioning of each earthquake event is determined; According to the relative positioning of each earthquake event, based on the cluster distribution of earthquakes, cluster information of each earthquake event is determined; The spatial distribution morphology and geometric dimensions of the cracks and the spatial distribution morphology and geometric dimensions of the faults are inferred based on the cluster information.

3. The method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters according to claim 1, characterized in that: The calculating of the first linear correlation coefficient and the first nonlinear correlation coefficient between each of the production factor data and the induced seismic time series data set and the calculating of the second linear correlation coefficient and the second nonlinear correlation coefficient between each of the production factor data and the casing deformation data specifically includes: Using the Pearson correlation coefficient method, a first global synchronous Pearson correlation coefficient between each of the production factor data and the induced seismic time series data set and a second global synchronous Pearson correlation coefficient between each of the production factor data and the casing deformation data are calculated; According to the first global synchronous Pearson correlation coefficient, a first set time window is selected, and a local synchronous Pearson correlation coefficient is calculated to obtain a first linear correlation coefficient; According to the second global synchronous Pearson correlation coefficient, a second set time window is selected, and a local synchronous Pearson correlation coefficient is calculated to obtain a second linear correlation coefficient; Calculating a first penalty function value according to each of the production factor data and the induced earthquake time series data set, and calculating a second penalty function value according to each of the production factor data and the casing deformation data; According to the first penalty function value, applying an adaptive dynamic time bending algorithm, calculating a first nonlinear correlation coefficient between each of the production factor data and the induced earthquake time series data set; According to the second penalty function value, an adaptive dynamic time warping algorithm is applied to calculate a second nonlinear correlation coefficient between each of the production factor data and the casing deformation data.

4. The method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters according to claim 1, characterized in that: The constructing of a multi-field coupling numerical model to determine the risk threshold of the main controlled production factor in the target area specifically includes: Obtaining surface structural information of geological surveys in the target area, geophysical exploration information in the target area, and drilling information in the target area; Constructing a three-dimensional seismic structural model of the target area according to the cluster information of each seismic event, the surface structural information, the geophysical exploration information of the target area and the drilling information of the target area; the three-dimensional seismic structural model of the target area includes a three-dimensional geological model of an activated fault and a three-dimensional geological model of a fault to be activated; According to the geometrical morphology of the three-dimensional geological model of the activated fault and the three-dimensional geological model of the fault to be activated, the slip trend and risk value of each fault are calculated by applying principal stress parameters and quantitative risk assessment method; According to the slip trend and risk value of each fault and the three-dimensional seismic structural model of the target area, a three-dimensional geological structural model including fault risk parameters is obtained; Inputting the main control production factor data as a disturbance item of the regional stress field into the three-dimensional geological structure model containing fault risk parameters to obtain the inverted stress field distribution of the earthquake source area; Using elastic equations, a hydraulic fracture propagation model based on the main controlling production factors is constructed; Calculating the flow field in the hydraulic fracture after the hydraulic fracture is initiated according to the main control production factor data and the hydraulic fracture expansion model based on the main control production factor; Construct the multi-field coupled constitutive equation of hydraulic fracturing flow field-fault slip force field; According to the multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field, the inversion source area stress field distribution and the flow field in the fracture after the hydraulic fracture is initiated, the risk threshold of the main controlled production factor in the target area is determined through forward simulation.

5. The method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters according to claim 4, characterized in that: The risk threshold of the main production factor in the target area is determined by forward simulation based on the multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field, the inverted stress field distribution in the source area and the flow field in the fracture after the hydraulic fracture is initiated, specifically including: According to the multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field, the inverted stress field distribution of the source area, the flow field in the fracture after the hydraulic fracture is initiated, and the three-dimensional geological structure model containing fault risk parameters, the theoretical main control production factors are determined through forward simulation; When the theoretical master-controlled production factor and the master-controlled production factor are inconsistent, the master-controlled production factor and the theoretical master-controlled production factor are updated according to the current target area seismic data and production factor data to obtain updated master-controlled production factors and updated theoretical master-controlled production factors; When the theoretical main controlled production factor is consistent with the main controlled production factor, the risk threshold of the main controlled production factor in the target area is determined.

6. The method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters according to claim 4, characterized in that: The method further comprises: According to the risk threshold of the main controlled production factor, the main controlled production factor data is adjusted so that the main controlled production factor data is less than the risk threshold.

7. The method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters according to claim 1, characterized in that: The real-time assessment of potential earthquake risks based on the risk threshold of the main controlled production factor specifically includes: When the main controlled production factor data is less than the risk threshold, there is no potential earthquake risk during the shale gas fracturing process in the target area; When the main controlled production factor data is not less than the risk threshold, there is a potential earthquake risk during the shale gas fracturing process in the target area.

8. A system for real-time evaluation of potential catastrophic risks to optimize fracturing operation parameters, characterized in that: The system comprises: The first acquisition module is used to acquire the seismic data of the shale gas fracturing process in the target area in real time through near-field monitoring, with the target shale gas development platform as the center; the seismic data of the target area is the three-component continuous waveform data monitored by the seismic monitoring equipment; A positioning module, used to locate each seismic event according to the seismic data, obtain cluster information of each seismic event, and infer the spatial distribution form and geometric size of the cracks and the spatial distribution form and geometric size of the faults according to the cluster information; The second acquisition module is used to obtain in real time the production factor data of the construction unit during the shale gas fracturing process in the target area and the casing deformation data occurring during the single well fracturing process; the production factor data include the fluid injection volume, fluid injection rate, real-time bottom hole pressure, fluid return volume and injected fluid properties; the casing deformation data include casing change time, casing change position, casing deformation amount, casing inner diameter and maximum diameter of the mill shoe; A screening module is used to construct a time-space relationship fitter, and based on the cluster information of each earthquake event, screen earthquake events that meet the set conditions based on the time-space relationship fitter to obtain an induced earthquake time series data set; A first calculation module, used for calculating a first linear correlation coefficient and a first nonlinear correlation coefficient between each of the production factor data and the induced seismic time series data set, and calculating a second linear correlation coefficient and a second nonlinear correlation coefficient between each of the production factor data and the casing deformation data; A second calculation module, for calculating the first regression coefficient of each of the production factor data and the induced earthquake time series data set and calculating the second regression coefficient of each of the production factor data and the casing deformation data by using a multivariate regression model; A third calculation module, configured to calculate a first importance weight of each of the production factor data to the induced earthquake time series data set by weighted average according to the first linear correlation coefficient, the first nonlinear correlation coefficient and the first regression coefficient; a fourth calculation module, for calculating the second importance weight of each of the production factor data to the casing deformation data by weighted average according to the second linear correlation coefficient, the second nonlinear correlation coefficient and the second regression coefficient; A main control production factor determination module, used to determine the main control production factor data according to the first importance weight and the second importance weight; A risk threshold determination module is used to construct a multi-field coupling numerical model and determine the risk threshold of the main control production factor in the target area based on the multi-field coupling numerical model; the multi-field coupling numerical model includes a three-dimensional geological structure model containing fault risk parameters and a multi-field coupling constitutive equation of hydraulic fracturing flow field-fault slip force field; An assessment module is used to conduct real-time assessment of potential earthquake risks based on the risk threshold of the main controlled production factors.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the method for real-time evaluation of potential catastrophic risks to optimize fracturing construction parameters as described in any one of claims 1 to 7.

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