A method for automatically interpreting fracturing fracture parameters based on fracturing well pump pressure

Abstract

The application discloses a kind of based on fracturing well pump pressure pressure fracture parameter automatic interpretation method, belong to oil and gas well completion engineering technical field, it is characterized in that, include the following steps: a, obtain wellbore and fracturing construction parameter;B, based on wellbore and fracturing construction parameter is simulated and simulated, and pressure fracture parameter interpretation objective function F is established based on fracturing well pump pressure by Bayes inversion in combination with fracturing well pump pressure simulation data and pump pressure actual data;C, target function optimization is carried out, and the automatic interpretation of pressure fracture parameter is completed by fitting fracturing well pump pressure.This application can realize the automatic quick interpretation of pressure fracture parameter by making full use of the characteristics and information of wellhead monitoring oscillation pressure signal after fracturing pump stops, improve fracture parameter interpretation accuracy.

Description

Technical Field

[0001] This invention relates to the field of oil and gas well completion engineering technology, and in particular to an automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well. Background Technology

[0002] Due to the tightness of shale reservoirs, shale gas requires large-scale hydraulic fracturing for economical development. However, compared to the widespread application of large-scale hydraulic fracturing, evaluating its effectiveness, especially characterizing the geometry of the fractures, has always been a challenge in the fracturing process. Accurate interpretation of the geometry and parameters of hydraulic fractures is of significant engineering value for guiding fracturing operations and subsequent efficient shale gas development.

[0003] Domestic and international experts and scholars have conducted some research on the interpretation of fracture parameters. Currently, methods for interpreting fracture parameters have been developed, including microseismic monitoring, core sampling, and flowback testing data estimation. However, these technologies all have certain shortcomings. Microseismic monitoring can only provide a rough estimate of the location of reservoir rock shearing, with low resolution, and the interpreted fracture size is often larger than the actual size. Core sampling offers high accuracy but is costly and time-consuming. Flowback testing data interpretation requires a certain period of production after fracturing, resulting in poor real-time performance. In contrast, after pump shutdown, the inertia of the fluid causes oscillating pressure signals to be observed at the wellhead. These pressure signals provide some indication of the fracture geometry and parameters. Using the pump shutdown pressure to interpret fracture geometry and parameters can provide real-time and efficient information feedback for fracturing operations. However, current methods for interpreting fracture parameters using pump shutdown pressure filter out the oscillating pressure waves, relying solely on the pressure drop to manually extrapolate fracture parameters. This fails to fully utilize the useful information in the pump shutdown pressure oscillation signal, affecting the accuracy of fracture parameter interpretation.

[0004] Chinese patent document CN112282717A, published on January 29, 2021, discloses a method for detecting and evaluating hydraulic fractures in shale gas reservoirs. The method includes: radioactive elements enriched in the shale gas reservoir migrate and enrich again after fracturing fluid enters the reservoir, causing natural gamma radioactivity anomalies in the near-wellbore zone; during the shale gas drainage process after fracturing, time-lapse logging is used to detect changes in natural gamma radioactivity in the near-wellbore zone after fracturing fluid return, obtaining natural gamma detection data; and interpreting the natural gamma detection data after preprocessing to evaluate the fracturing parameters, opening degree, and closure status of the hydraulic fractures in the near-wellbore zone of the shale gas well.

[0005] The patent document discloses a method for detecting and evaluating hydraulic fractures in shale gas reservoirs, which has the advantages of strong applicability, good economy, simple detection process, no environmental risk, and repeatable measurement. However, the accuracy of fracture parameter interpretation is not good, and it cannot achieve automatic and rapid interpretation of hydraulic fracture parameters. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides an automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well. By fully utilizing the characteristics and information of the oscillating pressure signal monitored at the wellhead after pump shutdown, this invention can achieve automatic and rapid interpretation of fracture parameters, thereby improving the accuracy of fracture parameter interpretation.

[0007] This invention is achieved through the following technical solution:

[0008] An automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well, characterized by comprising the following steps:

[0009] a. Obtain wellbore and fracturing operation parameters, including the length of the vertical section of the wellbore, the length of the horizontal section of the wellbore, the wellbore diameter, the casing wall thickness, the casing wall friction coefficient, the fracturing fluid viscosity, the fracturing fluid density, the number of fracturing clusters, the number of perforations, and the perforation diameter;

[0010] b. Simulate the pump shutdown pressure based on wellbore and fracturing construction parameters. Combine the simulated pump shutdown pressure data and the actual pump shutdown pressure data of the fracturing well with Bayesian inversion to establish the objective function F for interpreting fracturing parameters based on the pump shutdown pressure of the fracturing well.

[0011] c. Optimize the objective function and automatically interpret the fracture parameters by fitting the shutdown pressure of the fractured well.

[0012] In step b, the objective function F for interpreting the hydraulic fracture parameters is determined by Equation 1.

[0013] F = P(d|m)P(m) Equation 1

[0014] In the formula, F is the objective function for interpreting the hydraulic fracture parameters; P(d|m) is the likelihood function; P(m) is the prior probability distribution function; m is the hydraulic fracture interpretation parameter; and d is the observed pump shutdown pressure.

[0015] The fracture interpretation parameters include fracture half-length, fracture height, number of effective fracture opening clusters, and number of effective opening perforations.

[0016] The likelihood function P(d|m) is determined by Equation 2;

[0017]

[0018] In the formula, P(d|m) is the likelihood function; f(m) is the pump shutdown pressure data obtained by simulation under specified hydraulic fracturing parameters; σ n The variance of the pump stop pressure is used to observe the sampling points; M is the number of sampling points; d is the observed pump stop pressure.

[0019] The prior probability distribution function P(m) is determined by Equation 3;

[0020]

[0021] In the formula, P(m) is the prior probability distribution function; σ m denoted as the variance of the hydraulic fracture parameters; m represents the interpreted parameters of the hydraulic fracture; and M represents the number of sampling points. The average value of the known hydraulic fracturing parameters.

[0022] Step c specifically includes:

[0023] S1. Set the initial hydraulic crack half-length, crack height, upper limit constraint parameters, and lower limit constraint parameters;

[0024] S2. Add a disturbance to the current hydraulic fracture parameters to generate new hydraulic fracture parameters, and calculate the likelihood function based on the new hydraulic fracture parameters;

[0025] S3. Compare the likelihood function value under the new fracturing parameters with the current likelihood function value to determine whether to accept the new fracturing parameters.

[0026] S4. Based on the updated crack parameters, continue to sample and update crack parameters, and repeat the above discrimination steps until the number of update steps reaches the specified number.

[0027] S5. Statistically analyze the crack parameters accepted during the optimization process, provide the average value and variance data of the crack interpretation parameters, and conduct uncertainty analysis of the crack parameters.

[0028] In step S3, determining whether to accept new fracturing parameters specifically means that if the new likelihood function value is greater than the current likelihood function value, then the new fracturing parameters are accepted; if the new likelihood function value is less than the current likelihood function value, then the acceptance probability α is used to determine whether to accept the new fracturing parameters.

[0029] The acceptance probability α is determined by Equation 4;

[0030]

[0031] In the formula, α is the acceptance probability; P(d|m) is the likelihood function; p(d|m) new ) is the new likelihood function.

[0032] The method of determining whether to accept new hydraulic fracturing parameters by using the acceptance probability α refers to comparing the acceptance probability α with the random number u(0,1).

[0033] The random number u(0,1) is generated uniformly between 0 and 1. If the random number u(0,1) is greater than the acceptance probability α, the new pressure fracture parameters are accepted; if the random number u(0,1) is less than the acceptance probability α, the fracture parameters are rejected.

[0034] The beneficial effects of this invention are mainly reflected in the following aspects:

[0035] 1. This invention: a) Acquires wellbore and fracturing operation parameters, including the length of the vertical section of the wellbore, the length of the horizontal section of the wellbore, the wellbore diameter, the casing wall thickness, the pipe wall friction coefficient, the fracturing fluid viscosity, the fracturing fluid density, the number of fracturing clusters, the number of perforations, and the perforation diameter; b) Simulates the pump shutdown pressure based on the wellbore and fracturing operation parameters, and establishes an objective function F for interpreting fracturing parameters based on the pump shutdown pressure of the fracturing well through Bayesian inversion by combining the simulated pump shutdown pressure data and the actual pump shutdown pressure data; c) Optimizes the objective function, and completes the automatic interpretation of fracturing parameters by fitting the pump shutdown pressure of the fracturing well. Compared with the prior art, by making full use of the characteristics and information of the oscillating pressure signal monitored at the wellhead after pump shutdown, it can achieve automatic and rapid interpretation of fracturing parameters and improve the accuracy of fracturing parameter interpretation.

[0036] 2. This invention uses the fracturing pump shutdown pressure to interpret the geometry and parameters of the fracturing fracture, which can provide real-time and efficient information feedback for fracturing operations and ensure the smooth progress of the operation.

[0037] 3. This invention uses objective function optimization to automatically interpret fracture parameters by fitting the pump shutdown pressure of the fractured well. Compared with drilling and coring, this can effectively shorten the construction time and reduce the implementation cost.

[0038] 4. Compared with the method of interpreting fracture parameters by flowback test data, which requires a certain period of production after fracturing, this invention automatically interprets fracture parameters by fitting the pump shutdown pressure of the fracturing well, thus having better real-time performance.

[0039] 5. This invention overcomes the limitations of existing interpretation methods and has better applicability. Attached Figure Description

[0040] The present invention will now be further described in detail with reference to the accompanying drawings and specific embodiments, wherein:

[0041] Figure 1 This is a schematic diagram of the pump shutdown pressure interception in this invention;

[0042] Figure 2 This is a schematic diagram of the pump shutdown pressure fitting of the present invention. Detailed Implementation

[0043] Example 1

[0044] See Figure 1 and Figure 2 An automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well includes the following steps:

[0045] a. Obtain wellbore and fracturing operation parameters, including the length of the vertical section of the wellbore, the length of the horizontal section of the wellbore, the wellbore diameter, the casing wall thickness, the casing wall friction coefficient, the fracturing fluid viscosity, the fracturing fluid density, the number of fracturing clusters, the number of perforations, and the perforation diameter;

[0046] b. Simulate the pump shutdown pressure based on wellbore and fracturing construction parameters. Combine the simulated pump shutdown pressure data and the actual pump shutdown pressure data of the fracturing well with Bayesian inversion to establish the objective function F for interpreting fracturing parameters based on the pump shutdown pressure of the fracturing well.

[0047] c. Optimize the objective function and automatically interpret the fracture parameters by fitting the shutdown pressure of the fractured well.

[0048] This embodiment is the most basic implementation method. By making full use of the characteristics and information of the oscillating pressure signal monitored at the wellhead after fracturing and pump shutdown, it is possible to achieve automatic and rapid interpretation of fracturing parameters and improve the accuracy of fracturing parameter interpretation.

[0049] Example 2

[0050] See Figure 1 and Figure 2 An automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well includes the following steps:

[0051] a. Obtain wellbore and fracturing operation parameters, including the length of the vertical section of the wellbore, the length of the horizontal section of the wellbore, the wellbore diameter, the casing wall thickness, the casing wall friction coefficient, the fracturing fluid viscosity, the fracturing fluid density, the number of fracturing clusters, the number of perforations, and the perforation diameter;

[0052] b. Simulate the pump shutdown pressure based on wellbore and fracturing construction parameters. Combine the simulated pump shutdown pressure data and the actual pump shutdown pressure data of the fracturing well with Bayesian inversion to establish the objective function F for interpreting fracturing parameters based on the pump shutdown pressure of the fracturing well.

[0053] c. Optimize the objective function and automatically interpret the fracture parameters by fitting the shutdown pressure of the fractured well.

[0054] In step b, the objective function F for interpreting the hydraulic fracture parameters is determined by Equation 1.

[0055] F = P(d|m)P(m) Equation 1

[0056] In the formula, F is the objective function for interpreting the hydraulic fracture parameters; P(d|m) is the likelihood function; P(m) is the prior probability distribution function; m is the hydraulic fracture interpretation parameter; and d is the observed pump shutdown pressure.

[0057] The fracture interpretation parameters include fracture half-length, fracture height, number of effective fracture opening clusters, and number of effective opening perforations.

[0058] This embodiment is a preferred implementation method. It uses the fracturing pump shutdown pressure to interpret the geometry and parameters of the fracturing fracture, which can provide real-time and efficient information feedback for fracturing operations and ensure the smooth progress of the operation.

[0059] Example 3

[0060] See Figure 1 and Figure 2 An automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well includes the following steps:

[0061] a. Obtain wellbore and fracturing operation parameters, including the length of the vertical section of the wellbore, the length of the horizontal section of the wellbore, the wellbore diameter, the casing wall thickness, the casing wall friction coefficient, the fracturing fluid viscosity, the fracturing fluid density, the number of fracturing clusters, the number of perforations, and the perforation diameter;

[0062] b. Simulate the pump shutdown pressure based on wellbore and fracturing construction parameters. Combine the simulated pump shutdown pressure data and the actual pump shutdown pressure data of the fracturing well with Bayesian inversion to establish the objective function F for interpreting fracturing parameters based on the pump shutdown pressure of the fracturing well.

[0063] c. Optimize the objective function and automatically interpret the fracture parameters by fitting the shutdown pressure of the fractured well.

[0064] Furthermore, in step b, the objective function F for interpreting the hydraulic fracturing parameters is determined by Equation 1;

[0065] F = P(d|m)P(m) Equation 1

[0066] In the formula, F is the objective function for interpreting the hydraulic fracture parameters; P(d|m) is the likelihood function; P(m) is the prior probability distribution function; m is the hydraulic fracture interpretation parameter; and d is the observed pump shutdown pressure.

[0067] The fracture interpretation parameters include fracture half-length, fracture height, number of effective fracture opening clusters, and number of effective opening perforations.

[0068] The likelihood function P(d|m) is determined by Equation 2;

[0069]

[0070] In the formula, P(d|m) is the likelihood function; f(m) is the pump shutdown pressure data obtained by simulation under specified hydraulic fracturing parameters; σ n The variance of the pump stop pressure is used to observe the sampling points; M is the number of sampling points; d is the observed pump stop pressure.

[0071] The prior probability distribution function P(m) is determined by Equation 3;

[0072]

[0073] In the formula, P(m) is the prior probability distribution function; σ m denoted as the variance of the hydraulic fracture parameters; m represents the interpreted parameters of the hydraulic fracture; and M represents the number of sampling points. The average value of the known hydraulic fracturing parameters.

[0074] This embodiment is another preferred implementation method. It adopts objective function optimization and completes the automatic interpretation of fracture parameters by fitting the pump shutdown pressure of the fractured well. Compared with the drilling and coring method, it can effectively shorten the construction time and reduce the implementation cost.

[0075] Example 4

[0076] See Figure 1 and Figure 2 An automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well includes the following steps:

[0077] a. Obtain wellbore and fracturing operation parameters, including the length of the vertical section of the wellbore, the length of the horizontal section of the wellbore, the wellbore diameter, the casing wall thickness, the casing wall friction coefficient, the fracturing fluid viscosity, the fracturing fluid density, the number of fracturing clusters, the number of perforations, and the perforation diameter;

[0078] b. Simulate the pump shutdown pressure based on wellbore and fracturing construction parameters. Combine the simulated pump shutdown pressure data and the actual pump shutdown pressure data of the fracturing well with Bayesian inversion to establish the objective function F for interpreting fracturing parameters based on the pump shutdown pressure of the fracturing well.

[0079] c. Optimize the objective function and automatically interpret the fracture parameters by fitting the shutdown pressure of the fractured well.

[0080] In step b, the objective function F for interpreting the hydraulic fracture parameters is determined by Equation 1.

[0081] F = P(d|m)P(m) Equation 1

[0082] In the formula, F is the objective function for interpreting the hydraulic fracture parameters; P(d|m) is the likelihood function; P(m) is the prior probability distribution function; m is the hydraulic fracture interpretation parameter; and d is the observed pump shutdown pressure.

[0083] The fracture interpretation parameters include fracture half-length, fracture height, number of effective fracture opening clusters, and number of effective opening perforations.

[0084] The likelihood function P(d|m) is determined by Equation 2;

[0085]

[0086] In the formula, P(d|m) is the likelihood function; f(m) is the pump shutdown pressure data obtained by simulation under specified hydraulic fracturing parameters; σ n The variance of the pump stop pressure is used to observe the sampling points; M is the number of sampling points; d is the observed pump stop pressure.

[0087] The prior probability distribution function P(m) is determined by Equation 3;

[0088]

[0089] In the formula, P(m) is the prior probability distribution function; σ m denoted as the variance of the hydraulic fracture parameters; m represents the interpreted parameters of the hydraulic fracture; and M represents the number of sampling points. The average value of the known hydraulic fracturing parameters.

[0090] Furthermore, step c specifically includes:

[0091] S 1. Set the initial pressure crack half length, crack height, upper limit constraint parameters, and lower limit constraint parameters;

[0092] S2. Add a disturbance to the current hydraulic fracture parameters to generate new hydraulic fracture parameters, and calculate the likelihood function based on the new hydraulic fracture parameters;

[0093] S3. Compare the likelihood function value under the new fracturing parameters with the current likelihood function value to determine whether to accept the new fracturing parameters.

[0094] S4. Based on the updated crack parameters, continue to sample and update crack parameters, and repeat the above discrimination steps until the number of update steps reaches the specified number.

[0095] S5. Statistically analyze the crack parameters accepted during the optimization process, provide the average value and variance data of the crack interpretation parameters, and conduct uncertainty analysis of the crack parameters.

[0096] This embodiment is another preferred implementation. Compared with the requirement of a certain period of production after fracturing to interpret fracture parameters based on flowback test data, the automatic interpretation of fracture parameters by fitting the pump shutdown pressure of the fracturing well has better real-time performance.

[0097] Example 5

[0098] See Figure 1 and Figure 2 An automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well includes the following steps:

[0099] a. Obtain wellbore and fracturing operation parameters, including the length of the vertical section of the wellbore, the length of the horizontal section of the wellbore, the wellbore diameter, the casing wall thickness, the casing wall friction coefficient, the fracturing fluid viscosity, the fracturing fluid density, the number of fracturing clusters, the number of perforations, and the perforation diameter;

[0100] b. Simulate the pump shutdown pressure based on wellbore and fracturing construction parameters. Combine the simulated pump shutdown pressure data and the actual pump shutdown pressure data of the fracturing well with Bayesian inversion to establish the objective function F for interpreting fracturing parameters based on the pump shutdown pressure of the fracturing well.

[0101] c. Optimize the objective function and automatically interpret the fracture parameters by fitting the shutdown pressure of the fractured well.

[0102] In step b, the objective function F for interpreting the hydraulic fracture parameters is determined by Equation 1.

[0103] F = P(d|m)P(m) Equation 1

[0104] In the formula, F is the objective function for interpreting the hydraulic fracture parameters; P(d|m) is the likelihood function; P(m) is the prior probability distribution function; m is the hydraulic fracture interpretation parameter; and d is the observed pump shutdown pressure.

[0105] The fracture interpretation parameters include fracture half-length, fracture height, number of effective fracture opening clusters, and number of effective opening perforations.

[0106] The likelihood function P(d|m) is determined by Equation 2;

[0107]

[0108] In the formula, P(d|m) is the likelihood function; f(m) is the pump shutdown pressure data obtained by simulation under specified hydraulic fracturing parameters; σ n The variance of the pump stop pressure is used to observe the sampling points; M is the number of sampling points; d is the observed pump stop pressure.

[0109] The prior probability distribution function P(m) is determined by Equation 3;

[0110]

[0111] In the formula, P(m) is the prior probability distribution function; σ m denoted as the variance of the hydraulic fracture parameters; m represents the interpreted parameters of the hydraulic fracture; and M represents the number of sampling points. The average value of the known hydraulic fracturing parameters.

[0112] Step c specifically includes:

[0113] S1. Set the initial hydraulic crack half-length, crack height, upper limit constraint parameters, and lower limit constraint parameters;

[0114] S2. Add a disturbance to the current hydraulic fracture parameters to generate new hydraulic fracture parameters, and calculate the likelihood function based on the new hydraulic fracture parameters;

[0115] S3. Compare the likelihood function value under the new fracturing parameters with the current likelihood function value to determine whether to accept the new fracturing parameters.

[0116] S4. Based on the updated crack parameters, continue to sample and update crack parameters, and repeat the above discrimination steps until the number of update steps reaches the specified number.

[0117] S5. Statistically analyze the crack parameters accepted during the optimization process, provide the average value and variance data of the crack interpretation parameters, and conduct uncertainty analysis of the crack parameters.

[0118] In step S3, determining whether to accept new fracturing parameters specifically means that if the new likelihood function value is greater than the current likelihood function value, then the new fracturing parameters are accepted; if the new likelihood function value is less than the current likelihood function value, then the acceptance probability α is used to determine whether to accept the new fracturing parameters.

[0119] The acceptance probability α is determined by Equation 4;

[0120]

[0121] In the formula, α is the acceptance probability; P(d|m) is the likelihood function; p(d|m) new ) is the new likelihood function.

[0122] The method of determining whether to accept new hydraulic fracturing parameters by using the acceptance probability α refers to comparing the acceptance probability α with the random number u(0,1).

[0123] The random number u(0,1) is generated uniformly between 0 and 1. If the random number u(0,1) is greater than the acceptance probability α, the new pressure fracture parameters are accepted; if the random number u(0,1) is less than the acceptance probability α, the fracture parameters are rejected.

[0124] This embodiment is the best implementation method, which overcomes the limitations of existing interpretation methods and has better applicability.

[0125] The following explanation uses the fracturing parameters of a fracturing section in a given fracturing well, based on this invention:

[0126] The first step is to obtain key parameters for the fracturing wellbore and fracturing operations;

[0127] Based on the drilling and fracturing design data of the fractured well, the following key parameters were obtained: the vertical well section length is 3180m, the horizontal well section length is 2120m, the wellbore inner diameter is 118.6mm, the pipe wall thickness is 10.54mm, the Young's modulus of the pipe material is 206GPa, the Poisson's ratio of the pipe material is 0.3, the pipe wall roughness is 0.015mm, and the fracturing fluid density is 1000kg / m³. 3 The fracturing fluid viscosity is 2 mPa·s, and the perforation diameter is 8.128 mm.

[0128] The second step is to extract the pump shutdown pressure monitoring data for interpreting the fracture parameters based on the fracturing construction curve of the fracturing well.

[0129] Based on the pressure and displacement data recorded during the fracturing operation of the fracturing well, the pump shutdown time is determined. The point when the displacement drops to 0 is taken as the starting point for extracting pump shutdown pressure data, and the data is extracted until the wellhead pressure begins to vent. The pump shutdown pressure monitoring data during this period is used as the driving data for interpreting the fracturing parameters. The extraction process is as follows: Figure 1 As shown;

[0130] The third step is to interpret the fracture parameters under pump shutdown pressure based on the Monte Carlo-Markov chain method and obtain the fracture parameters of the fractured well.

[0131] Based on field fracturing experience, the initial values ​​and upper and lower limits of the fracturing parameters are given as follows: the initial value of the fracture half-length is 180m, with upper and lower limits of 50-200m; the initial value of the fracture height is 20m, with upper and lower limits of 5-40m; the initial value of the effective fracture cluster number is 5, with upper and lower limits of 1-5; the initial value of the effective perforation number is 25, with upper and lower limits of 10-40. Using this data, the fracturing parameters are interpreted as follows: the fracture half-length is 188.75m, the fracture height is 19.76m, the effective fracture cluster number is 5, and the effective perforation number is 11.

[0132] It is evident that the interpretation method of this invention can achieve automatic and rapid interpretation of crack parameters, thereby improving the accuracy of crack parameter interpretation.

Claims

1. An automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well, characterized in that, Includes the following steps: a. Obtain wellbore and fracturing operation parameters, including the length of the vertical section of the wellbore, the length of the horizontal section of the wellbore, the wellbore diameter, the casing wall thickness, the casing wall friction coefficient, the fracturing fluid viscosity, the fracturing fluid density, the number of fracturing clusters, the number of perforations, and the perforation diameter; b. Simulate the pump shutdown pressure based on wellbore and fracturing construction parameters. Combine the simulated pump shutdown pressure data and the actual pump shutdown pressure data of the fracturing well with Bayesian inversion to establish the objective function F for interpreting fracturing parameters based on the pump shutdown pressure of the fracturing well. c. Optimize the objective function and automatically interpret the fracture parameters by fitting the pump shutdown pressure of the fractured well; In step b, the objective function F for interpreting the hydraulic fracturing parameters is determined by Equation 1. Formula 1 In the formula, F is the objective function for interpreting the hydraulic cracking parameters; P ( d | m ) is the likelihood function; P ( m ) is the prior probability distribution function; m Interpretation parameters for hydraulic cracking; d The observed pump shutdown pressure; The likelihood function P ( d | m The result is determined by calculation using Equation 2; Formula 2 In the formula, P ( d | m ) is the likelihood function; f ( m The data represents the pump shutdown pressure obtained from simulation under specified hydraulic fracturing parameters. σ n To observe the variance of the pump shutdown pressure; M is the number of sampling points; d The observed pump shutdown pressure; The prior probability distribution function P ( m Determined by calculation using Equation 3; Formula 3 In the formula, P ( m ) is the prior probability distribution function; σ m The variance of the hydraulic cracking parameters; m The parameters are used to interpret the hydraulic cracks; M is the number of sampling points; The average value of the known hydraulic crack parameters; Step c specifically includes: S1. Set the initial hydraulic crack half-length, crack height, upper limit constraint parameter, and lower limit constraint parameter; S2. Add a disturbance to the current hydraulic fracture parameters to generate new hydraulic fracture parameters, and calculate the likelihood function based on the new hydraulic fracture parameters; S3. Compare the likelihood function value under the new fracturing parameters with the current likelihood function value to determine whether to accept the new fracturing parameters. S4. Based on the updated crack parameters, continue to sample and update crack parameters, and repeat the above discrimination steps until the number of update steps reaches the specified number. S5. Statistically analyze the crack parameters accepted during the optimization process, provide the average value and variance data of the crack interpretation parameters, and conduct uncertainty analysis of the crack parameters.

2. The automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well according to claim 1, characterized in that: The fracture interpretation parameters include fracture half-length, fracture height, number of effective fracture opening clusters, and number of effective opening perforations.

3. The automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well according to claim 1, characterized in that: In step S3, determining whether to accept the new fracturing parameters specifically means that if the new likelihood function value is greater than the current likelihood function value, then the new fracturing parameters are accepted; if the new likelihood function value is less than the current likelihood function value, then the parameters are accepted based on the probability. Determine whether to accept the new hydraulic fracturing parameters.

4. The automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well according to claim 3, characterized in that: The acceptance probability Determined by Equation 4; Formula 4 In the formula, For the probability of acceptance; P ( d | m ) is the likelihood function; Let be the new likelihood function.

5. The automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well according to claim 4, characterized in that: The probability of acceptance Determining whether to accept new hydraulic fracturing parameters refers to adjusting the acceptance probability. Compare with the random number u(0,1).

6. The automatic interpretation method for fracture parameters based on the pump shutdown pressure of a fractured well according to claim 5, characterized in that: The random number u(0,1) is generated uniformly from 0 to 1. If the random number u(0,1) is greater than the acceptance probability... If the new hydraulic fracturing parameters are not met, then the new parameters will be accepted; if the random number u(0,1) is less than the acceptance probability... If so, then the crack parameters will not be updated.

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