Metamorphic buried hill reservoir overpressure monitoring method based on CCS-UCS

Abstract

This invention discloses a method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS, comprising: preprocessing drilling data; constructing a formation pressure monitoring model for drilling; constructing a real-time monitoring model for the triaxial compressive strength of the bottom hole rock; determining the segmented structure using CCS values; constructing linear regression analysis based on CCS segmented structure data and marking abnormally high-pressure segments; constructing a uniaxial compressive strength calculation model for the bottom hole rock, and obtaining real-time monitoring models for the normal pressure segment, abnormally high-pressure segment, and a series of uniaxial compressive strengths across the entire depth; combining the real-time uniaxial compressive strength monitoring model with the formation pressure monitoring model based on the minimum principal stress state of the bottom hole rock, and monitoring the formation pressure values ​​of the marked abnormally high-pressure segments in real time, thereby achieving formation pressure monitoring during drilling. This invention does not rely on the formation pressure formation mechanism or compaction trend line and is applicable to overpressure monitoring in buried hill reservoirs.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas drilling and logging engineering technology, specifically relating to a method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS. Background Technology

[0002] In recent years, with the continuous advancement of buried hill reservoir exploration and development, buried hill exploration targets have gradually attracted the attention of petroleum workers. Existing formation pressure prediction and monitoring methods, such as sonic transit time, DC index method, Eaton method, Bowers method, DST / MDT testing, well test analysis, and numerical simulation, are mainly applicable to conventional clastic or carbonate reservoirs with continuous porous media, relative homogeneity, and a unified pressure system. Metamorphic buried hill reservoirs, due to their complex geological background and unique reservoir formation, pose challenges to traditional formation pressure prediction and monitoring methods. The main reservoirs are characterized by macroscopic fractures and microscopic pores, forming a dual-pore media system. The fracture scale ranges from micrometers to centimeters, and they develop in multiple phases and groups. Conventional logging methods cannot simultaneously quantify the permeability contribution of fractures and matrix, leading to significant deviations in the estimation of fracture permeability parameters in pressure prediction models. At the same time, due to the strong heterogeneity and zonation of the reservoir, conventional well test analysis may cause abnormal pressure decay curves due to the presence of local high-permeability zones, thus misjudging the overall pressure state. Unlike conventional sandstone and mudstone reservoirs, metamorphic buried hill reservoirs exhibit conductive overpressure due to the development of newly formed paleoreservoirs. The relationship between acoustic velocity and effective stress is insensitive, limiting the applicability of existing methods for this type of reservoir. Currently, no effective overpressure monitoring method has been established for metamorphic buried hill reservoirs. Among published technical documents and patents, patent CN119227517B improves the initial accuracy of buried hill formation pressure prediction through binary data coupling, but its adaptability to nonlinear relationships, dynamic processes, and extreme physical properties is insufficient. Dong Huaping's method of using a BP neural network to predict pore pressure in buried hill calculations makes it difficult to intuitively explain the physical relationships of the pre-input parameters for pore pressure, hindering the verification and adjustment of the prediction logic. Summary of the Invention

[0003] This invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a method for monitoring overpressure in metamorphic buried hill reservoirs based on bottom rock CCS-UCS.

[0004] This invention is achieved through the following technical solution: A method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS includes the following steps: S1. Acquire logging parameters while drilling and preprocess the data to address any quality issues. The logging parameters include drill bit type, drill bit diameter, bottom hole circulating equivalent mud density (ECD), drilling pressure, rotation speed, and mechanical drilling speed. The data quality issues include locally missing parameter values, discontinuous jumps, and outlier noise. The main purpose of preprocessing data quality issues is to address the dynamic oscillation characteristics that logging parameters acquired during drilling often exhibit around a baseline value. The specific preprocessing method is as follows: S11. To address the issue of locally missing parameters, linear interpolation is used to supplement the missing logging parameters: The formula for the linear interpolation method is: In the formula: Here are the logging parameter values ​​at depth d; These are the logging parameter values ​​at depth d-1; These are the logging parameter values ​​at depth d+1; For depth Depth at the location; For depth Depth at the location; For depth intervals, ; S12. To address outlier issues, the geological 3σ rule is used for identification, and the moving average of five adjacent points is used to replace outliers. The geological 3σ rule is as follows: In the formula: These are the drilling parameters for the current lithology section; This represents the standard deviation of the current lithology section. This is the threshold for anomaly detection; The model for the moving average of the five adjacent points is as follows: In the formula: The first moving average One value; For the first part of the original data One value; S13. Under complex drilling conditions, the measurement signals of the logging engineering are disturbed by the wellbore, and there is a systematic deviation between the measured data and the true value. To address the problem of outlier noise, the wavelet domain-time frequency joint analysis method is used to reduce and smooth the data. The model of the wavelet domain-time frequency joint analysis method is as follows: In the formula: The result of the wavelet transform is a bivariate function of the translation factor and the scaling factor; These are the real-time drilling parameters that vary with time t. It is a scale factor, and >0, realizing basic wavelet Perform scaling transformation; The translation factor is used to perform a translation transformation of the basic wavelet on the time axis. For time; S2. Considering the coupling effect of drilling fluid circulation bottom hole pressure and formation pressure, a formation pressure monitoring model based on the minimum principal stress state of the bottom hole rock is constructed, which includes the following steps: S21. Calculate the minimum principal stress in the rock at the bottom of the well during drilling; Typically, the minimum principal stress is perpendicular to the bottom of the wellbore. The minimum principal stress is the difference between the pressure exerted at the bottom of the well by the hydrostatic column of drilling fluid and the circulating friction and the formation pressure. The calculation model for the minimum principal stress of the bottom rock is as follows: In the formula: The minimum principal stress in the rock at the bottom of the well is expressed in MPa. This refers to the bottom hole pressure, expressed in MPa. Formation pressure, in MPa; The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). S22. The Mohr-Coulomb strength criterion is selected to analyze the critical failure condition of shear rock breaking during the drilling process of PDC drill bits commonly used in oil and gas drilling. The expression for the critical failure condition of shear rock breaking is: In the formula: This represents the maximum principal stress in the rock at the bottom of the well, expressed in MPa. The minimum principal stress in the rock at the bottom of the well is expressed in MPa. This is the internal friction angle, expressed in degrees (°). Cohesion, measured in kPa; S23. Substitute the calculation model of minimum principal stress of bottom rock in step S21 into the expression of critical failure condition for shear rock breaking based on the Mohr-Coulomb strength criterion in step S22 to construct the mathematical and physical equation relating the triaxial compressive strength (CCS) of bottom rock to bottom pressure and formation pressure. The mathematical and physical equation relating the triaxial compressive strength of the rock at the bottom of the well to the bottom-hole pressure and the formation pressure is as follows: In the formula: The triaxial compressive strength of rock is expressed in MPa. The minimum principal stress in the rock at the bottom of the well is expressed in MPa. This is the internal friction angle, expressed in degrees (°). Cohesion, measured in kPa; The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). This represents the uniaxial compressive strength of rock, expressed in MPa. S24. Organize the mathematical and physical equations relating the triaxial compressive strength (CCS) of the bottom rock to the bottom rock pressure and formation pressure, and construct a formation pressure monitoring model based on the minimum principal stress state of the bottom rock. The formation pressure monitoring model based on the minimum principal stress state of the bottom rock is as follows: In the formula: The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The triaxial compressive strength of rock is expressed in MPa. This represents the uniaxial compressive strength of rock, expressed in MPa. Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). This is the internal friction angle, expressed in degrees (°). S3. Analyze the interaction process between the PDC drill bit and the rock, and in conjunction with the Mohr-Coulomb strength criterion, construct a real-time monitoring model for the bottom hole rock triaxial compressive strength (CCS) based on the drilling rock breaking feedback information of the PDC drill bit. The specific steps include: S31. Collect drilling pressure, rotation speed, drilling time, mud density, inlet and outlet discharge rates, and riser pressure parameters through comprehensive logging while drilling. The parameters picked up during drilling are field data and do not undergo the data processing in step S1. S32. Based on the interaction process between PDC teeth and bottom rock, establish a triaxial compressive strength model of bottom rock based on drilling rock breaking feedback information of PDC drill bit. The bottom hole rock triaxial compressive strength model based on PDC drill bit drilling rock breaking feedback information is as follows: In the formula: The triaxial compressive strength of rock is expressed in MPa. This represents the total number of cutting teeth, dimensionless. This refers to the side rotation angle, in degrees. The drill bit diameter is in mm. This refers to the mechanical drilling speed, expressed in m / h. This is the lean angle, in degrees. is the rock friction coefficient, dimensionless; b, c, g, and K are coefficients, dimensionless. This refers to drilling pressure, measured in kN. Rotational speed, in r / min; , where is the dimensionless wear of the PDC drill bit teeth, and is the ratio of tooth wear height to cutting tooth radius, which is dimensionless; S4. Using the detection principle of PELT, a data segmentation method based on CCS structure is constructed (S41+S42). The segmentation structure is determined using CCS values. The specific steps include: S41, CCS value initialization: Treat the CCS value and the depth sequence as a sub-segment, calculate the initial cost, and define the depth sequence segmentation cost function; The depth sequence segmentation cost function is: In the formula: This represents the cost function value of the Lth sub-segment or segment; This is the CCS value corresponding to the i-th depth point in the original data; The fitted value is the depth point of the i-th depth point within the L-th sub-segment; The length of the current sub-segment; Initializing CCS values ​​amplifies the impact of abnormal CCS fluctuations, making it easier to detect significant changes. S42. Recursion and Pruning: Calculate possible split points for each depth point (each point in the depth sequence), eliminate invalid candidates, and determine the optimal split point by minimizing the global objective function; The expression for the optimal split point is: In the formula: The optimal set of split points; To find the set of split points that minimizes the global objective function ; The number of dividing points is K+1, meaning the number of segments is K+1. This is the depth value, in meters (m). This represents the cost function value for the Kth sub-segment; This is a regularization parameter used to balance the number of segments and fitting accuracy, preventing overfitting. S43. Output segmentation results: Based on all optimal segmentation points, form a CCS segmentation structure according to the depth sequence; S5. Obtain the slope value based on the CCS segmented structural data through linear regression, and label the abnormal high-pressure segments by combining the abnormal high-pressure segment threshold. Construct a linear regression analysis and labeling of abnormal high-pressure segments based on the CCS segmented structural data, which specifically includes the following steps: S51. Use LinearRegression to perform linear regression analysis on the CCS segmented structure data and estimate the regression parameters. The formula for estimating the regression parameters is: In the formula: The linear regression slope of the current segment (the j-th sub-segment), i.e., the regression parameter; This is the intercept of the current segment (the j-th sub-segment); Covariance measures the linear correlation between the two. for Variance measures the degree of dispersion of CCS values; This is the data set of all CCS values ​​within the current segment; This is the data set of all D values ​​within the current segment (segment j); This is the average of all D values ​​within the current segment; This is the average of all CCS values ​​within the current segment. S52, using the negative slope of the linear regression slope ( ) Filter and label, identify regions where CCS decreases abnormally with depth, and define selection criteria for abnormal high-pressure sections: The expression for the selection condition of the abnormal high-voltage section is: In the formula: This is a segment with a negative slope; The linear regression slope of the current segment (the j-th sub-segment); This represents the depth position of the (j+1)th sub-segment; Let j be the depth position of the j-th sub-segment; This is the set of data point indices for the j-th sub-segment (the j-th negative slope segment); The selection criteria for abnormal high-pressure sections are that the negative slope section is the abnormal high-pressure section; For each negative slope segment Calculate the label position; The formula for calculating the marked position is: In the formula: The mean value of CCS within the j-th negative slope segment is used as the horizontal position of the label; The mean depth value within the j-th negative slope segment is used as the vertical position of the label; The number of data points within the j-th sub-segment; Let CCS be the value of the i-th data point; Let i be the depth value of the i-th data point; This is the set of data point indices for the j-th sub-segment (specifically, the j-th negative slope segment); S53. Mark abnormal regions: Record the start and end depths of each abnormal region and mark it; The sliding formula is used to eliminate potential noise or small fluctuations in the data. The sliding formula is as follows: In the formula: For depth The average CCS value within the sliding window; This represents the total length of the sliding window; The width is half the width of the window; The CCS value at depth (or index) i+k; The decision (or judgment) formula is used to identify individual outlier candidate points. The decision (or judgment) formula is as follows: when Valid only if it is valid; otherwise, it is invalid if it is NaN. In the formula: For depth The average CCS value within the sliding window; The CCS value of the previous (or preceding) region; For threshold ratio parameter, when When the value is 0.1, it indicates a 10% decrease, triggering an exception. S6. Assuming that the uniaxial compressive strength of the bottom rock is not affected by changes in formation pressure, construct a calculation model for the uniaxial compressive strength (UCS) of the bottom rock, and obtain real-time monitoring models for the uniaxial compressive strength (UCS) of the bottom rock under normal pressure, the uniaxial compressive strength (UCS1) under abnormal high pressure, and the uniaxial compressive strength (UCS2) across the entire depth. The specific steps include: S61. Set the minimum principal stress of the rock at the bottom of the well to zero. Substituting the values ​​into the Mohr-Coulomb strength criterion, we obtain the uniaxial compressive strength calculation model for the bottom-hole rock: In the formula: The uniaxial compressive strength of the rock at the bottom of the well is expressed in MPa. This is the internal friction angle, expressed in degrees (°). Cohesion, measured in kPa; The calculation model of uniaxial compressive strength of rock at the bottom of the well shows that the uniaxial compressive strength is relatively stable and is not affected by changes in formation pressure. S62. Based on the hydrostatic pressure conditions of the area, the formation pressure equivalent mud density is set to be equal to the hydrostatic pressure equivalent density. Substitute this into the uniaxial compressive strength calculation model for the bottom rock to calculate the uniaxial compressive strength profile: In the formula: This represents the uniaxial compressive strength of rock, expressed in MPa. The triaxial compressive strength of rock is expressed in MPa. The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). This is the internal friction angle, expressed in degrees (°). S63. For the marked abnormal high pressure section, the average value of the uniaxial compressive strength of the same lithology as the adjacent normal pressure section above the abnormal high pressure section is used to replace it, so as to obtain the real-time monitoring model of the uniaxial compressive strength (UCS1) of the abnormal high pressure section. S64. Real-time monitoring models for uniaxial compressive strength (UCS) of rock at normal pressure range at the bottom of the well, real-time monitoring models for uniaxial compressive strength (UCS1) of rock at abnormal high pressure range, and real-time monitoring models for uniaxial compressive strength (UCS2) of rock at full depth. S7. Combine the real-time monitoring model of uniaxial compressive strength (UCS) of normal pressure section of bottom rock, the real-time monitoring model of uniaxial compressive strength (UCS1) of abnormal high pressure section, and the real-time monitoring model of uniaxial compressive strength (UCS2) of full depth series with the formation pressure monitoring model of minimum principal stress state of bottom rock, and monitor the formation pressure value of the marked abnormal high pressure section in real time to realize the formation pressure value monitoring while drilling. Specifically, substitute the real-time monitoring model of uniaxial compressive strength (UCS2) of full depth series into the formation pressure monitoring model of minimum principal stress state of bottom rock obtained in step S2 to realize the monitoring of formation pressure value.

[0005] The beneficial effects of this invention are: This invention provides a method for monitoring overpressure in metamorphic buried hill reservoirs based on bottom hole rock CCS-UCS. By analyzing the bottom hole stress state under different formation pressures, and using the Mohr-Coulomb strength criterion, a model relating the triaxial compressive strength (CCS) of the bottom hole rock to formation pressure, a predictive model for the uniaxial compressive strength (UCS), and a monitoring model for the triaxial compressive strength are derived. A formation pressure monitoring model, workflow, and method based on CCS-UCS are constructed. The method does not rely on normal compaction trend lines or loading / unloading equations, but directly calculates formation pressure based on drilling logging engineering parameters and bottom hole circulating pressure equivalent mud density (ECD). It is applicable to overpressure monitoring in conductive buried hill reservoirs of newly formed paleo-reservoirs, breaking through the homogeneous assumption and single-medium model of traditional methods. It is a very useful supplement to ensuring drilling operation safety and has certain guiding significance for engineering practice. Attached Figure Description

[0006] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This describes the distribution characteristics of logging acquisition parameters before and after data preprocessing in Embodiment 1 of the present invention. Figure 3 This refers to the real-time monitoring of the triaxial compressive strength of the bottom rock before and after data preprocessing in Embodiment 1 of the present invention. Figure 4 This is the result of data segmentation using CCS values ​​in Embodiment 1 of the present invention; Figure 5 This is Embodiment 1 of the present invention, which is based on the segmented structural characteristics of CCS and the slopes and abnormal high-pressure layers of each segment obtained by linear regression analysis. Figure 6 This is the overpressure monitoring result of the buried hill reservoir in Embodiment 1 of the present invention.

[0007] For those skilled in the art, other related figures can be obtained from the above figures without any creative effort. Detailed Implementation

[0008] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. Example 1

[0009] like Figure 1 As shown, a method for monitoring overpressure in metamorphic buried hill reservoirs based on bottom-hole rock CCS-UCS includes the following steps: S1. Acquire logging parameters while drilling and preprocess the data to address any quality issues. Taking the BZ19-6 metamorphic buried hill formation as an example, the logging parameters collected during drilling are shown in Table 1.

[0010] Table 1: Logging parameters acquired while drilling The data in Table 1 is preprocessed. The specific preprocessing method is as follows: S11. To address the issue of locally missing parameters, linear interpolation is used to supplement the missing logging parameters: The formula for the linear interpolation method is: In the formula: Here are the logging parameter values ​​at depth d; These are the logging parameter values ​​at depth d-1; These are the logging parameter values ​​at depth d+1; For depth Depth at the location; For depth Depth at the location; For depth intervals, ; S12. To address outlier issues, the geological 3σ rule is used for identification, and the moving average of five adjacent points is used to replace outliers. The geological 3σ rule is as follows: In the formula: These are the drilling parameters for the current lithology section; This represents the standard deviation of the current lithology section. This is the threshold for anomaly detection; The model for the moving average of the five adjacent points is as follows: In the formula: The first moving average One value; For the first part of the original data One value; S13. Under complex drilling conditions, the measurement signals of the logging engineering are disturbed by the wellbore, and there is a systematic deviation between the measured data and the true value. To address the problem of outlier noise, the wavelet domain-time frequency joint analysis method is used to reduce and smooth the data. The model of the wavelet domain-time frequency joint analysis method is as follows: In the formula: The result of the wavelet transform is a bivariate function of the translation factor and the scaling factor; These are the real-time drilling parameters that vary with time t. It is a scale factor, and >0, realizing basic wavelet Perform scaling transformation; The translation factor is used to perform a translation transformation of the basic wavelet on the time axis. For time; Data distribution characteristics before and after data preprocessing, such as Figure 2 As shown.

[0011] S2. Considering the coupling effect of drilling fluid circulation bottom hole pressure and formation pressure, a formation pressure monitoring model based on the minimum principal stress state of the bottom hole rock is constructed, which includes the following steps: S21. Calculate the minimum principal stress in the rock at the bottom of the well during drilling; Typically, the minimum principal stress is perpendicular to the bottom of the wellbore. The minimum principal stress is the difference between the pressure exerted at the bottom of the well by the hydrostatic column of drilling fluid and the circulating friction and the formation pressure. The calculation model for the minimum principal stress of the bottom rock is as follows: In the formula: The minimum principal stress in the rock at the bottom of the well is expressed in MPa. This refers to the bottom hole pressure, expressed in MPa. Formation pressure, in MPa; The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). S22. The Mohr-Coulomb strength criterion is selected to analyze the critical failure condition of shear rock breaking during the drilling process of PDC drill bits commonly used in oil and gas drilling. The expression for the critical failure condition of shear rock breaking is: In the formula: This represents the maximum principal stress in the rock at the bottom of the well, expressed in MPa. The minimum principal stress in the rock at the bottom of the well is expressed in MPa. This is the internal friction angle, expressed in degrees (°). Cohesion, measured in kPa; S23. Substitute the calculation model of minimum principal stress of bottom rock in step S21 into the expression of critical failure condition for shear rock breaking based on the Mohr-Coulomb strength criterion in step S22 to construct the mathematical and physical equation relating the triaxial compressive strength (CCS) of bottom rock to bottom pressure and formation pressure. The mathematical and physical equation relating the triaxial compressive strength of the rock at the bottom of the well to the bottom-hole pressure and the formation pressure is as follows: In the formula: The triaxial compressive strength of rock is expressed in MPa. The minimum principal stress in the rock at the bottom of the well is expressed in MPa. This is the internal friction angle, expressed in degrees (°). Cohesion, measured in kPa; The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). This represents the uniaxial compressive strength of rock, expressed in MPa. S24. Organize the mathematical and physical equations relating the triaxial compressive strength (CCS) of the bottom rock to the bottom rock pressure and formation pressure, and construct a formation pressure monitoring model based on the minimum principal stress state of the bottom rock. The formation pressure monitoring model based on the minimum principal stress state of the bottom rock is as follows: In the formula: The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The triaxial compressive strength of rock is expressed in MPa. This represents the uniaxial compressive strength of rock, expressed in MPa. Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). This is the internal friction angle, expressed in degrees (°). S3. Analyze the interaction process between the PDC drill bit and the rock, and in conjunction with the Mohr-Coulomb strength criterion, construct a real-time monitoring model for the bottom hole rock triaxial compressive strength (CCS) based on the drilling rock breaking feedback information of the PDC drill bit. The specific steps include: S31. Parameters such as drilling pressure, rotation speed, drilling time, mud density, inlet and outlet discharge rate, and riser pressure are collected through comprehensive logging while drilling. S32. Based on the interaction process between PDC drill bits and bottom-hole rock, establish a drilling rock-breaking reaction system based on PDC drill bits. The well bottom rock triaxial compressive strength model is fed back information; The bottom hole rock triaxial compressive strength model based on PDC drill bit drilling rock breaking feedback information is as follows: In the formula: The triaxial compressive strength of rock is expressed in MPa. This represents the total number of cutting teeth, dimensionless. This refers to the side rotation angle, in degrees. The drill bit diameter is in mm. This refers to the mechanical drilling speed, expressed in m / h. This is the lean angle, in degrees. is the rock friction coefficient, dimensionless; b, c, g, and K are coefficients, dimensionless. Drilling pressure, unit: kN; Rotational speed, in r / min; , where is the dimensionless wear of the PDC drill bit teeth, and is the ratio of tooth wear height to cutting tooth radius, which is dimensionless; Using the established real-time monitoring model for the triaxial compressive strength (CCS) of bottom-hole rock, the real-time monitored CCS values ​​are as follows: Figure 3 As shown.

[0012] S4. Using the detection principle of PELT, a data segmentation method based on CCS structure is constructed. The segmentation structure is determined using CCS values. The specific steps include: S41, CCS value initialization: Treat the CCS value and the depth sequence as a sub-segment, calculate the initial cost, and define the depth sequence segmentation cost function; The depth sequence segmentation cost function is: In the formula: This represents the cost function value of the Lth sub-segment or segment; This is the CCS value corresponding to the i-th depth point in the original data; The fitted value is the depth point of the i-th depth point within the L-th sub-segment; The length of the current sub-segment; Initializing CCS values ​​amplifies the impact of abnormal CCS fluctuations, making it easier to detect significant changes. S42. Recursion and Pruning: Calculate possible split points at each depth point, eliminate invalid candidates, and determine the optimal split point by minimizing the global objective function; The expression for the optimal split point is: In the formula: The optimal set of split points; To find the set of split points that minimizes the global objective function ; The number of dividing points is K+1, meaning the number of segments is K+1. This is the depth value, in meters (m). This represents the cost function value for the Kth sub-segment; This is a regularization parameter used to balance the number of segments and fitting accuracy, preventing overfitting. S43. Output segmentation results: Based on all optimal segmentation points, form a CCS segmentation structure according to the depth sequence; The results of data segmentation using CCS values ​​established by S4 are as follows: Figure 4 As shown.

[0013] S5. Obtain the slope value based on the CCS segmented structural data through linear regression, and label the abnormal high-pressure segments by combining the abnormal high-pressure segment threshold. Construct a linear regression analysis and labeling of abnormal high-pressure segments based on the CCS segmented structural data, which specifically includes the following steps: S51. Use LinearRegression to perform linear regression analysis on the CCS segmented structure data and estimate the regression parameters. The formula for estimating the regression parameters is: In the formula: The linear regression slope of the current segment (the j-th sub-segment); This is the intercept of the current segment (the j-th sub-segment); Covariance measures the linear correlation between the two. for Variance measures the degree of dispersion of CCS values; This is the data set of all CCS values ​​within the current segment; This is the data set of all D values ​​within the current segment (segment j); This is the average of all D values ​​within the current segment; This is the average of all CCS values ​​within the current segment. S52, using the negative slope of the linear regression slope ( ) Filter and label, identify regions where CCS decreases abnormally with depth, and define selection criteria for abnormal high-pressure sections: The expression for the selection condition of the abnormal high-voltage section is: In the formula: This is a segment with a negative slope; The linear regression slope of the current segment (the j-th sub-segment); This represents the depth position of the (j+1)th sub-segment; Let j be the depth position of the j-th sub-segment; This is the set of data point indices for the j-th sub-segment (specifically, the j-th negative slope segment); For each negative slope segment Calculate the label position; The formula for calculating the marked position is: In the formula: The mean value of CCS within the j-th negative slope segment is used as the horizontal position of the label; The mean depth value within the j-th negative slope segment is used as the vertical position of the label; The number of data points within the j-th sub-segment; Let CCS be the value of the i-th data point; Let i be the depth value of the i-th data point; This is the set of data point indices for the j-th sub-segment (specifically, the j-th negative slope segment); S53. Mark abnormal regions: Record the start and end depths of each abnormal region and mark it; In the formula: For depth The average CCS value within the sliding window; This represents the total length of the sliding window; The width is half the width of the window; The CCS value at depth (or index) i+k; when Valid only if it is valid; otherwise, it is invalid if it is NaN. In the formula: For depth The average CCS value within the sliding window; The CCS value of the previous (or preceding) region; For threshold ratio parameter, when When the value is 0.1, it indicates a 10% decrease, triggering an exception. The established structural features based on CCS segmentation and the slopes and anomalous high-pressure layers of each segment obtained through linear regression analysis, such as... Figure 5 As shown.

[0014] S6. Assuming that the uniaxial compressive strength of the bottom rock is not affected by changes in formation pressure, construct a calculation model for the uniaxial compressive strength (UCS) of the bottom rock, and obtain real-time monitoring models for the uniaxial compressive strength (UCS) of the bottom rock under normal pressure, the uniaxial compressive strength (UCS1) under abnormal high pressure, and the uniaxial compressive strength (UCS2) across the entire depth. The specific steps include: S61. Set the minimum principal stress of the rock at the bottom of the well to zero. Substituting the values ​​into the Mohr-Coulomb strength criterion, we obtain the uniaxial compressive strength calculation model for the bottom-hole rock: In the formula: The uniaxial compressive strength of the rock at the bottom of the well is expressed in MPa. This is the internal friction angle, expressed in degrees (°). Cohesion, measured in kPa; The calculation model of uniaxial compressive strength of rock at the bottom of the well shows that the uniaxial compressive strength is relatively stable and is not affected by changes in formation pressure. S62. Based on the hydrostatic pressure conditions of the area, set the formation pressure equivalent mud density to the hydrostatic pressure equivalent density, and calculate the uniaxial compressive strength profile: In the formula: This represents the uniaxial compressive strength of rock, expressed in MPa. The triaxial compressive strength of rock is expressed in MPa. The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). This is the internal friction angle, expressed in degrees (°). S63. For the marked abnormal high pressure section, the average value of the uniaxial compressive strength of the same lithology as the adjacent normal pressure section above the abnormal high pressure section is used to replace it, so as to obtain the real-time monitoring model of the uniaxial compressive strength (UCS1) of the abnormal high pressure section. S64. Real-time monitoring models for uniaxial compressive strength (UCS) of rock at normal pressure range at the bottom of the well, real-time monitoring models for uniaxial compressive strength (UCS1) of rock at abnormal high pressure range, and real-time monitoring models for uniaxial compressive strength (UCS2) of rock at full depth. S7. Combine the real-time monitoring model of uniaxial compressive strength (UCS) of normal pressure section of bottom rock, the real-time monitoring model of uniaxial compressive strength (UCS1) of abnormal high pressure section, and the real-time monitoring model of uniaxial compressive strength (UCS2) of full depth series with the formation pressure monitoring model of minimum principal stress state of bottom rock, and monitor the formation pressure value of the marked abnormal high pressure section in real time to realize the formation pressure value monitoring while drilling. Specifically, substitute the real-time monitoring model of uniaxial compressive strength (UCS2) of full depth series into the formation pressure monitoring model of minimum principal stress state of bottom rock obtained in step S2 to realize the monitoring of formation pressure value.

[0015] Overpressure monitoring results of buried hill reservoirs, such as Figure 6 As shown.

[0016] This embodiment utilizes a novel overpressure monitoring method for metamorphic buried hill reservoirs based on CCS-UCS to calculate abnormal high pressure in the buried hill formation and compares it with the field-measured formation pressure. It was found that the abnormal high pressure segments detected by the new method are basically consistent with the abnormal high pressure segments measured in the field, and the agreement rate between the abnormal high pressure values ​​and the field-measured formation pressure values ​​is high, reaching over 95.7%.

[0017] The principle of this invention: In the monitoring of formation pressure in metamorphic buried hill reservoirs, traditional seepage theory relies on the assumption of a continuous medium and a unified pressure system. However, due to the discontinuity of fracture networks, strong isolation, and non-Darcy flow characteristics of this type of reservoir, prediction models based on acoustic / resistivity response cannot characterize the true pressure state, and single-point test data loses representativeness due to the isolation of pressure units. This invention establishes a rock mechanics correlation model between bottom-hole stress and formation pressure, derives a quantitative function of triaxial compressive strength (CCS) and formation pressure based on the Mohr-Coulomb strength criterion, and constructs a CCS-UCS dual-parameter collaborative analysis framework: first, core bias is eliminated by inverting UCS values ​​from logging data, and then CCS monitoring values ​​are dynamically updated while drilling. Utilizing the PELT detection principle, a segmented data method based on CCS structure is constructed, and the slope values ​​of the segmented CCS structure data are obtained through linear regression. Combined with the threshold of abnormal high-pressure sections, abnormal high-pressure sections are labeled. Considering that the uniaxial compressive strength of bottom-hole rock is not affected by changes in formation pressure, a uniaxial compressive strength (UCS) calculation model for normal pressure sections and abnormal high-pressure sections is constructed. Combined with the formation pressure monitoring model while drilling, formation pressure values ​​are monitored while drilling. The optimized mechanical monitoring model of this invention can directly quantify the weakening effect of wellbore rock strength, avoid the defects of discontinuous medium seepage modeling, and improve pressure prediction accuracy by >60% (field verification), providing a reliable guarantee for safe drilling and efficient development of complex reservoirs.

[0018] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS, characterized in that: Includes the following steps: S1. Acquire logging parameters while drilling and preprocess the data to address any quality issues. S2. Construct a formation pressure monitoring model based on the minimum principal stress state of the bottom rock; S3. Combine the Mohr-Coulomb strength criterion to construct a real-time monitoring model for the triaxial compressive strength of bottom hole rock based on the drilling rock breaking feedback information of PDC drill bit; S4. Using the detection principle of PELT, construct a data segmentation method based on CCS structure, and use CCS values ​​to determine the segmentation structure. S5. Obtain the slope value based on CCS segmented structural data through linear regression, combine it with the threshold of abnormal high pressure segment, label the abnormal high pressure segment, and construct a linear regression analysis and labeling of abnormal high pressure segment based on CCS segmented structural data. S6. Assuming that the uniaxial compressive strength of the bottom rock is not affected by changes in formation pressure, construct a calculation model for the uniaxial compressive strength of the bottom rock, and obtain real-time monitoring models for the uniaxial compressive strength of the bottom rock under normal pressure, abnormal high pressure, and full-depth series of real-time monitoring models for the uniaxial compressive strength. S7. Combine the real-time monitoring model of uniaxial compressive strength of normal pressure section of bottom rock, the real-time monitoring model of uniaxial compressive strength of abnormal high pressure section, and the real-time monitoring model of uniaxial compressive strength of full depth series with the formation pressure monitoring model of minimum principal stress state of bottom rock to monitor the formation pressure value of the marked abnormal high pressure section in real time, and realize the formation pressure value monitoring while drilling.

2. The method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS according to claim 1, characterized in that: The logging parameters include drill bit type, drill bit diameter, bottom hole circulating mud density, drilling pressure, rotation speed, and mechanical drilling speed; the data quality issues include locally missing parameter values, discontinuous jumps, and outlier noise.

3. The overpressure monitoring method for metamorphic buried hill reservoirs based on CCS-UCS according to claim 1, characterized in that: The preprocessing method in step S1 specifically includes the following steps: S11. To address the issue of locally missing parameters, linear interpolation is used to supplement the missing logging parameters: The formula for the linear interpolation method is: In the formula: Here are the logging parameter values ​​at depth d; These are the logging parameter values ​​at depth d-1; These are the logging parameter values ​​at depth d+1; For depth Depth at the location; For depth Depth at the location; For depth intervals, ; S12. To address outlier issues, the geological 3σ rule is used for identification, and the moving average of five adjacent points is used to replace outliers. The geological 3σ rule is as follows: In the formula: These are the drilling parameters for the current lithology section; This represents the standard deviation of the current lithology section. This is the threshold for anomaly detection; The model for the moving average of the five adjacent points is as follows: In the formula: The first moving average One value; For the first part of the original data One value; S13. To address the outlier noise problem, wavelet domain-time frequency joint analysis is used for data denoising and smoothing. The model of the wavelet domain-time frequency joint analysis method is as follows: In the formula: The result of the wavelet transform is a bivariate function of the translation factor and the scaling factor; These are the real-time drilling parameters that vary with time t. It is a scale factor, and >0, realizing basic wavelet Perform scaling transformation; The translation factor is used to perform a translation transformation of the basic wavelet on the time axis. For time.

4. The method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21. Calculate the minimum principal stress in the rock at the bottom of the well during drilling; The calculation model for the minimum principal stress of the rock at the bottom of the well is as follows: In the formula: The minimum principal stress in the rock at the bottom of the well is expressed in MPa. This refers to the bottom hole pressure, expressed in MPa. Formation pressure, in MPa; The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). S22. The Mohr-Coulomb strength criterion is selected to analyze the critical failure condition of shear rock breaking during the drilling process of PDC drill bits commonly used in oil and gas drilling. The expression for the critical failure condition of shear rock breaking is: In the formula: This represents the maximum principal stress in the rock at the bottom of the well, expressed in MPa. The minimum principal stress in the rock at the bottom of the well is expressed in MPa. This is the internal friction angle, expressed in degrees (°). Cohesion, measured in kPa; S23. Substitute the calculation model of minimum principal stress of bottom rock in step S21 into the expression of critical failure condition for shear rock breaking based on the Mohr-Coulomb strength criterion in step S22, and construct the mathematical and physical equations relating the triaxial compressive strength of bottom rock to bottom pressure and formation pressure. The mathematical and physical equation relating the triaxial compressive strength of the rock at the bottom of the well to the bottom-hole pressure and the formation pressure is as follows: In the formula: The triaxial compressive strength of rock is expressed in MPa. The minimum principal stress in the rock at the bottom of the well is expressed in MPa. This is the internal friction angle, expressed in degrees (°). Cohesion, measured in kPa; The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). This represents the uniaxial compressive strength of rock, expressed in MPa. S24. Organize the mathematical and physical equations relating the triaxial compressive strength of the bottom rock to the bottom rock pressure and formation pressure, and construct a formation pressure monitoring model based on the minimum principal stress state of the bottom rock. The formation pressure monitoring model based on the minimum principal stress state of the bottom rock is as follows: In the formula: The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The triaxial compressive strength of rock is expressed in MPa. This represents the uniaxial compressive strength of rock, expressed in MPa. Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). The internal friction angle is expressed in degrees (°).

5. The method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31. Collect drilling pressure, rotation speed, drilling time, mud density, inlet and outlet discharge rates, and riser pressure parameters through comprehensive logging while drilling. S32. Based on the interaction process between PDC teeth and bottom rock, establish a triaxial compressive strength model of bottom rock based on drilling rock breaking feedback information of PDC drill bit. The bottom hole rock triaxial compressive strength model based on PDC drill bit drilling rock breaking feedback information is as follows: In the formula: The triaxial compressive strength of rock is expressed in MPa. This represents the total number of cutting teeth, dimensionless. This refers to the side rotation angle, in degrees. The drill bit diameter is in mm. This refers to the mechanical drilling speed, expressed in m / h. This is the lean angle, in degrees. is the rock friction coefficient, dimensionless; b, c, g, and K are coefficients; Drilling pressure, unit: kN; Rotational speed, in r / min; , where is the dimensionless wear of the PDC drill bit teeth, and is the ratio of tooth wear height to cutting tooth radius, which is dimensionless.

6. The method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS according to claim 1, characterized in that: Step S4 specifically includes the following steps: S41, CCS value initialization: Treat the CCS value and the depth sequence as a sub-segment, calculate the initial cost, and define the depth sequence segmentation cost function; The depth sequence segmentation cost function is: In the formula: This represents the cost function value of the Lth sub-segment or segment; This is the CCS value corresponding to the i-th depth point in the original data; The fitted value is the depth point of the i-th depth point within the L-th sub-segment; The length of the current sub-segment; S42. Recursion and Pruning: Calculate possible split points at each depth point, eliminate invalid candidates, and determine the optimal split point by minimizing the global objective function; The expression for the optimal split point is: In the formula: The optimal set of split points; To find the set of split points that minimizes the global objective function ; The number of dividing points is K+1, meaning the number of segments is K+1. This is the depth value, in meters (m). This represents the cost function value for the Kth sub-segment; For regularization parameters; S43. Output segmentation results: Based on all optimal segmentation points, form a CCS segmentation structure according to the depth sequence.

7. The method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS according to claim 1, characterized in that: Step S5 specifically includes the following steps: S51. Use LinearRegression to perform linear regression analysis on the CCS segmented structure data and estimate the regression parameters. The formula for estimating the regression parameters is: In the formula: Let be the linear regression slope of the j-th sub-segment; Let be the intercept of the j-th sub-segment; Covariance measures the linear correlation between the two. for Variance measures the degree of dispersion of CCS values; This is the data set of all CCS values ​​within the current segment; This is the data set of all D values ​​within the j-th sub-segment; This is the average of all D values ​​within the current segment; This is the average of all CCS values ​​within the current segment. S52. Filter out negative slopes of the labeled linear regression slopes, identify regions where CCS decreases abnormally with depth, and define selection criteria for abnormal high-pressure sections: The expression for the selection condition of the abnormal high-voltage section is: In the formula: This is a segment with a negative slope; Let be the linear regression slope of the j-th sub-segment; This represents the depth position of the (j+1)th sub-segment; Let j be the depth position of the j-th sub-segment; Let j be the set of data point indices for the j-th negative slope segment; For each negative slope segment Calculate the label position; The formula for calculating the marked position is: In the formula: The mean value of CCS within the j-th negative slope segment is used as the horizontal position of the label; The mean depth value within the j-th negative slope segment is used as the vertical position of the label; The number of data points within the j-th sub-segment; Let CCS be the value of the i-th data point; Let i be the depth value of the i-th data point; Let j be the set of data point indices for the j-th negative slope segment; S53. Mark abnormal regions: Record the start and end depths of each abnormal region and mark it; In the formula: For depth The average CCS value within the sliding window; This represents the total length of the sliding window; The width is half the width of the window; The CCS value at depth i+k; when Valid only if it is valid; otherwise, it is invalid if it is NaN. In the formula: For depth The average CCS value within the sliding window; The CCS value of the previous region; For threshold ratio parameter, when When the value is 0.1, it indicates a 10% decrease, triggering an exception.

8. The method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS according to claim 1, characterized in that: Step S6 specifically includes the following steps: S61. Setting the minimum principal stress of the rock at the bottom of the well to zero, and substituting it into the Mohr-Coulomb strength criterion, we obtain the calculation model for the uniaxial compressive strength of the rock at the bottom of the well: In the formula: The uniaxial compressive strength of the rock at the bottom of the well is expressed in MPa. This is the internal friction angle, expressed in degrees (°). Cohesion, measured in kPa; S62. Based on the hydrostatic pressure conditions of the area, set the formation pressure equivalent mud density to the hydrostatic pressure equivalent density, and calculate the uniaxial compressive strength profile: In the formula: This represents the uniaxial compressive strength of rock, expressed in MPa. The triaxial compressive strength of rock is expressed in MPa. The density of the bottom-hole circulating mud is expressed in g / cm³. 3 ; The density of the mud is the equivalent of formation pressure, expressed in g / cm³. 3 ; Acceleration due to gravity, unit is m / s² 2 ; The vertical depth at the bottom of the well is expressed in meters (m). This is the internal friction angle, expressed in degrees (°). S63. For the marked abnormal high pressure section, the average uniaxial compressive strength of the lithology of the adjacent normal pressure section above the abnormal high pressure section is used to replace it, and a real-time monitoring model of the uniaxial compressive strength of the abnormal high pressure section is obtained. S64. Real-time monitoring models of uniaxial compressive strength in normal pressure zone of bottom rock, real-time monitoring models of uniaxial compressive strength in abnormal high pressure zone, and real-time monitoring models of uniaxial compressive strength in full depth series.

9. The method for monitoring overpressure in metamorphic buried hill reservoirs based on CCS-UCS according to claim 1, characterized in that: The specific steps of step S7, monitoring formation pressure, are as follows: the real-time monitoring model of uniaxial compressive strength at full depth is substituted into the formation pressure monitoring model based on the minimum principal stress state of the rock at the bottom of the well, obtained in step S2, to monitor the formation pressure value.

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