A rock mechanics parameter prediction method and system based on well logging curves

Abstract

The application discloses a rock mechanics parameter prediction method and system based on well logging curves, relates to the oil and gas drilling engineering technical field, and prediction steps are as follows: well logging data acquisition and pretreatment: in the process of drilling in hard strata, the well logging curve data of the acoustic travel time logging curve, the density logging curve, the natural gamma logging curve and the resistivity logging curve are collected, and the collected well logging curve data is pretreated, including removing noise interference, filling missing data and correcting abnormal data points. The rock mechanics parameter prediction method based on the well logging curves and fully considering the combination of the borehole trajectory and the hard strata characteristics is used to fill the current technical blank, accurately analyzes the complex rock mechanics characteristics of the hard strata by integrating multi-source data, and then improves the prediction accuracy, comprehensively contains rich strata information of the well logging curves, and combines the careful consideration of the borehole trajectory.

Description

Technical Field

[0001] This invention relates to the field of oil and gas drilling engineering technology, specifically to a method and system for predicting rock mechanical parameters based on well logging curves. Background Technology

[0002] Against the backdrop of continuously rising global energy demand, oil and gas resources, as a vital energy pillar, require efficient exploration and development. Hard formations are widely distributed in numerous oil and gas reservoirs; however, drilling operations in these formations present many challenging problems due to the high strength, hardness, and complex mechanical properties of the rocks. For example, extremely slow mechanical drilling rates significantly extend drilling cycles; increased drill bit wear raises drilling costs; and frequent wellbore stability issues seriously threaten the safety of drilling operations. Therefore, accurately obtaining the rock mechanical parameters of hard formations is crucial to effectively addressing these challenges.

[0003] Traditional methods for obtaining rock mechanics parameters mainly rely on laboratory experiments and field tests. While laboratory experiments can obtain relatively accurate parameters in a relatively controlled environment, they have many limitations. Well logging technology, with its unique advantage of being able to acquire a large amount of formation information in real time and continuously during drilling, has been widely used in the oil and gas exploration and development field. Well logging curves contain rich information on rock physical properties, and theoretically, rock mechanics parameters can be predicted through in-depth analysis of these curves. However, most existing methods for predicting rock mechanics parameters based on well logging curves fail to fully consider the crucial factor of wellbore trajectory. In actual drilling, the wellbore trajectory is not simply vertical downwards, but is influenced by a combination of factors such as geological structure and drilling technology, exhibiting complex curvature and inclination. This complex wellbore trajectory makes the correspondence between well logging data and the actual formation location intricate, thus seriously interfering with the accurate prediction of rock mechanics parameters. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method and system for predicting rock mechanical parameters based on well logging curves. This application solves the problem that the accuracy of the aforementioned test data is easily affected by various factors, making it difficult to fully guarantee the reliability of the data.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for predicting rock mechanical parameters based on well logging curves includes the following steps:

[0007] During drilling in hard formations, logging curve data is collected and preprocessed, including removing noise interference, filling in missing data, and correcting abnormal data points. The logging curve data includes sonic transit time logging curves, density logging curves, natural gamma logging curves, and resistivity logging curves.

[0008] Based on the rock physical properties of hard formations, characteristic parameters related to rock mechanical parameters are extracted from preprocessed well logging data;

[0009] The vertical depth corresponding to different well depths is determined by geometric relationships. For key well depth points, linear interpolation is used to read the logging parameters corresponding to the vertical depth. Wellbore trajectory data is obtained by classifying and organizing the vertical depths. The key well depth points include measured data points, trajectory feature points, and engineering marker points.

[0010] Core rock mechanical parameters are obtained based on characteristic parameters and wellbore trajectory data. These core rock mechanical parameters include uniaxial compressive strength, formation cohesion, internal friction angle, clay content, and tensile strength.

[0011] The core rock mechanics parameters obtained are organized according to the depth-parameter value correspondence to generate a continuous stratigraphic parameter profile.

[0012] Preferably: the acoustic time-of-flight logging curve is obtained by transmitting acoustic waves into the formation and receiving the reflected signals, and recording the propagation time of the acoustic waves in the formation; the density logging curve is obtained by measuring the volume density of the formation using the Compton scattering effect of gamma rays with the formation material; the natural gamma logging curve is obtained by collecting the intensity of gamma rays released by the naturally radioactive elements in the formation; and the resistivity logging curve is obtained by applying alternating current to the formation and measuring the formation's ability to impede the current.

[0013] Preferably, the characteristic parameters related to rock mechanics parameters include P-wave velocity, formation density, natural gamma parameter, dynamic Poisson's ratio, and clay content. The P-wave velocity is obtained from the pre-processed sonic transit time logging curve, and the specific calculation formula is as follows:

[0014] V p =1 / Δt

[0015] Where V p The values ​​are expressed as P-wave velocity, the propagation speed of P-waves in rock, and Δt, representing the acoustic transit time, reflecting the time required for acoustic waves to propagate through a unit thickness of rock. Formation density is directly extracted from the pre-processed density logging curve and is expressed as the mass per unit volume of formation rock. Natural gamma parameters include natural gamma logging values, natural gamma values ​​for the target pure mudstone layer, natural gamma values ​​for the target pure sandstone layer, and the Hillch index. The natural gamma logging values ​​are directly extracted from the pre-processed natural gamma logging curve.

[0016] The natural gamma value of the pure shale layer in the target layer and the natural gamma value of the pure sandstone layer in the target layer are determined based on the natural gamma logging curve by statistically calculating the values of the pure lithology sections in the target layer;

[0017] The Hirsch index is related to the geological age. The Hirsch index needs to be determined by combining the natural gamma logging curve and the core analysis data, and the Hirsch index is used to calculate the shale content.

[0018] Preferably: The dynamic Poisson's ratio is used to describe the ratio of the lateral strain to the longitudinal strain when the rock is stressed, and it is a parameter for calculating the uniaxial compressive strength and cohesion characteristics, which is derived by extracting and deducing from the logging curve; The shale content is used to reflect the proportion of the shale component in the formation, which is calculated through the natural gamma parameter, and the input is the calculation of the mechanical parameters of the compressive strength and cohesion.

[0019] Preferably:

[0020] The method of using linear interpolation to read the logging parameters corresponding to the vertical depth specifically includes:

[0021] Given two data points as (x0, y0) and (x1, y1) respectively, and it is required to interpolate y at a certain x between x0 and x1; Assuming x0 < x < x1, the linear interpolation calculation formula is:

[0022]

[0023] where x is the horizontal coordinate well depth of the point where interpolation calculation is required, y is the corresponding vertical coordinate logging parameter, and (x0, y0) and (x1, y1) are two known data points;

[0024] Determining the vertical depth corresponding to different well depths through geometric relationships specifically includes: During the drilling process, the well depth and the well deviation angle are obtained synchronously, with a frequency not lower than 1 time / 0.125 m; The vertical depth H is obtained through the following formula:

[0025] H = L × cosα

[0026] In the formula, L is the well depth and α is the well deviation angle;

[0027] Adaptive calculations are performed for different trajectory types of the drilling. The different trajectory types of the drilling include vertical sections, inclined sections, and curved sections. For the curved section, the interval is encrypted, key well depth points are selected, and linear interpolation is used to obtain the logging parameters corresponding to the vertical depth; The vertical depth is divided into three types of intervals according to the trajectory type, and a table containing the vertical depth interval, trajectory type, well depth range, and logging parameters is sorted out, and data stratification binding is performed to obtain the wellbore trajectory data.

[0028] Preferably: The calculation formula for the uniaxial compressive strength is:

[0029]

[0030] in This represents the uniaxial compressive strength of rock, expressed in MPa. It is the hard stratum coefficient; This is an oilfield experience coefficient; Indicates the dynamic Poisson's ratio; This indicates the density of the formation, expressed in g / cm³. 3 ; It indicates the clay content, reflecting the proportion of clay components in the strata; This represents the longitudinal wave velocity, measured in m / s.

[0031] Preferably, the formula for calculating the formation cohesion is:

[0032]

[0033] in This represents formation cohesion, expressed in MPa. It is the hardness coefficient, which is related to the hardness of the rock; It is an adjustment coefficient, a parameter used to adjust the calculation of formation cohesion; Indicates the dynamic Poisson's ratio; This indicates the density of the formation, expressed in g / cm³. 3 ; It indicates the clay content, reflecting the proportion of clay components in the strata; This represents the longitudinal wave velocity, measured in m / s.

[0034] Preferably, the formula for calculating the internal friction angle is:

[0035]

[0036] in It is the internal friction angle. This represents the cohesive strength of the underlying layer, expressed in MPa.

[0037] The formula for calculating clay content is:

[0038]

[0039] in Expressed as clay content, it reflects the proportion of clay components in the formation. Formal parameters related to the calculation of clay content, The natural gamma relative value is used to measure the relative magnitude of the natural gamma logging value with respect to its minimum and maximum values.

[0040] Formal parameters The calculation formula is:

[0041]

[0042] in Formal parameters, specifically the natural gamma relative index, are the original natural gamma logging values ​​at a certain depth point. GRmax and Grmin are the minimum and maximum natural gamma values ​​in the study area, respectively.

[0043] The formula for calculating tensile strength is:

[0044]

[0045] in Tensile strength reflects the rock's ability to resist tensile failure; It is the dynamic elastic modulus; Porosity.

[0046] Preferred method: Organize the acquired core rock mechanical parameters according to the depth-parameter value correspondence to generate a continuous stratigraphic parameter profile. Specifically, this includes: organizing the core rock mechanical parameters, aligning and sorting them based on vertical depth, and processing missing data through weighted interpolation to generate a continuous stratigraphic parameter profile.

[0047] A rock mechanics parameter prediction system based on well logging curves includes a preprocessing module, a feature extraction module, a wellbore trajectory data acquisition module, a mechanical parameter acquisition module, and a prediction module.

[0048] The preprocessing module is used to collect logging curve data during drilling in hard formations and to preprocess the collected logging curve data. Specifically, it includes removing noise interference, filling missing data, and correcting abnormal data points. The logging curve data includes sonic transit time logging curves, density logging curves, natural gamma logging curves, and resistivity logging curves.

[0049] The feature extraction module extracts feature parameters related to rock mechanics parameters from preprocessed well logging data based on the rock physical properties of hard formations.

[0050] The wellbore trajectory data acquisition module determines the vertical depth corresponding to different well depths through geometric relationships. For key well depth points, linear interpolation is used to read the logging parameters corresponding to the vertical depth. Wellbore trajectory data is obtained by classifying and organizing the vertical depths. The key well depth points include measured data points, trajectory feature points, and engineering marker points.

[0051] The mechanical parameter acquisition module acquires core rock mechanical parameters based on characteristic parameters and wellbore trajectory data. These core rock mechanical parameters include uniaxial compressive strength, formation cohesion, internal friction angle, clay content, and tensile strength.

[0052] The prediction module organizes the acquired core rock mechanics parameters according to the depth-parameter value correspondence, generating a continuous stratigraphic parameter profile.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] This invention discloses a method for predicting rock mechanical parameters based on well logging curves. By integrating multi-source data, it accurately analyzes the complex rock mechanical properties of hard formations, thereby improving prediction accuracy. It comprehensively utilizes the rich formation information contained in the well logging curves and combines it with meticulous consideration of the wellbore trajectory. This overcomes the errors caused by neglecting the wellbore trajectory in traditional methods, accurately calibrates the predicted rock mechanical parameters, and ensures that they are highly consistent with the actual mechanical properties of hard formations, thus laying a solid data foundation for subsequent engineering decisions.

[0055] This invention addresses the significant differences in the composition, structure, and mechanical properties of hard formation rocks, enabling flexible responses to hard formations under varying geological conditions. It significantly improves predictive efficiency in various complex hard formation environments, providing strong support for diverse oil and gas drilling projects in hard formations. With its high-precision prediction of rock mechanical parameters, it assists engineers in scientifically and rationally optimizing drilling parameters, such as drilling pressure, rotational speed, and displacement, during the drilling engineering design phase, ensuring efficient drilling operations. Simultaneously, it allows for the development of targeted preventative measures in advance, effectively reducing the incidence of downhole accidents and significantly improving the safety and economy of drilling operations. Attached Figure Description

[0056] Figure 1 This is a flowchart of a rock mechanics parameter prediction method based on well logging curves according to the present invention. Detailed Implementation

[0057] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0058] Reference Figure 1 As shown, the present invention provides a method for predicting rock mechanical parameters based on well logging curves, and the prediction steps are as follows:

[0059] Well logging data acquisition and preprocessing: During drilling in hard formations, well logging curve data is acquired and preprocessed, specifically including noise removal, missing data filling, and correction of abnormal data points; the well logging curve data includes sonic transit time logging curves, density logging curves, natural gamma logging curves, and resistivity logging curves.

[0060] Feature parameter extraction: Based on the rock physical properties of hard formations, feature parameters related to rock mechanical parameters are extracted from preprocessed well logging data;

[0061] Wellbore trajectory data acquisition: The vertical depth corresponding to different well depths is determined through geometric relationships. For key well depth points, linear interpolation is used to read the logging parameters corresponding to the vertical depth. Wellbore trajectory data is obtained by classifying and organizing the vertical depths. The key well depth points include measured data points, trajectory feature points, and engineering marker points.

[0062] Obtaining core rock mechanical parameters: Core rock mechanical parameters are obtained based on characteristic parameters and wellbore trajectory data. These core rock mechanical parameters include uniaxial compressive strength (UCS), formation cohesion (C), and internal friction angle ( ). ), mud content (V) ci ) and tensile strength (S) t );

[0063] Model application and parameter output: The core rock mechanics parameters are organized according to the depth-parameter value correspondence to generate a continuous formation parameter profile, which can be directly used for drilling engineering design, and drilling parameters can be optimized to obtain wellbore collapse pressure and formulate anti-collapse measures in advance.

[0064] This application presents a method for predicting rock mechanics parameters based on well logging curves. By combining wellbore trajectory data with classification and correction, it solves the problem of traditional methods ignoring the influence of trajectory, making the predicted mechanical parameters of hard formations more closely match the actual formation stress state. The deviation from core sampling experimental data can be controlled within 8%. Data is collected using conventional well logging equipment, eliminating the need for costly indoor rock mechanics experiments or field tests, significantly reducing manpower and material resources, and lowering the exploration cost per well by more than 30%. Targeted calculations are performed on the mechanical properties of hard formations and complex wellbore trajectories, demonstrating good adaptability in various hard formation drilling scenarios. The generated continuous parameter profile can be directly used for drilling parameter optimization and wellbore stability assessment, effectively increasing the mechanical drilling rate by more than 20% and reducing the wellbore collapse accident rate, providing reliable data support for the efficient and safe implementation of drilling projects.

[0065] The acoustic time-of-flight logging curve is obtained by transmitting acoustic waves into the formation and receiving the reflected signals, recording the propagation time of the acoustic waves in the formation; the density logging curve is obtained by measuring the volume density of the formation using the Compton scattering effect of gamma rays with the formation material; the natural gamma logging curve is obtained by collecting the intensity of gamma rays released by the naturally occurring radioactive elements in the formation; and the resistivity logging curve is obtained by applying alternating current to the formation and measuring the formation's ability to impede the current.

[0066] This application uses a method of transmitting and receiving acoustic waves to measure propagation time, which can directly reflect the density and elastic properties of rocks. The data can be directly used to calculate the P-wave velocity, which is a core input for key mechanical parameters such as thrust elastic modulus and uniaxial compressive strength. Moreover, this method is real-time and continuous, can cover the entire drilling section, and accurately capture subtle changes in the longitudinal mechanical properties of hard formations.

[0067] The relevant characteristic parameters include P-wave velocity, formation density, natural gamma parameter, dynamic Poisson's ratio, and clay content. The P-wave velocity is obtained from the pre-processed sonic transit time logging curve, and the specific calculation formula is as follows:

[0068] V p =1 / Δt

[0069] Where V p The values ​​are expressed as P-wave velocity, the propagation speed of P-waves in rock, and Δt, representing the acoustic transit time, reflecting the time required for acoustic waves to propagate through a unit thickness of rock. Formation density is directly extracted from the pre-processed density logging curve and is expressed as the mass per unit volume of formation rock. Natural gamma parameters include natural gamma logging values, natural gamma values ​​for the target pure mudstone layer, natural gamma values ​​for the target pure sandstone layer, and the Hillch index. The natural gamma logging values ​​are directly extracted from the pre-processed natural gamma logging curve.

[0070] The natural gamma values ​​of the target layer pure mudstone layer and the target layer pure sandstone layer are determined based on the statistical analysis of the natural gamma logging curves of the pure lithological section of the target layer.

[0071] The Hillch index is related to geological age and is determined by natural gamma logging curves and core analysis data. It is used to calculate clay content.

[0072] In this application, the P-wave velocity is directly calculated from the sonic transit time curve, relying on the continuous coverage of well logging data to accurately reflect rock elasticity. This serves as the core input for deriving parameters such as compressive strength, with simple and reproducible logic, reducing human error. Formation density is extracted from the pre-processed density curve, retaining the advantages of density logging in terms of anti-interference and high precision, while pre-processing eliminates anomalies, ensuring the data closely matches the true quality of hard formations and reducing calculation deviations in subsequent mechanical formulas. Among the natural gamma parameters, the Hilch index is determined by combining geological age and core data, allowing the clay content calculation to be adapted to the target block and eliminating the influence of non-hard formations. Furthermore, these parameters constitute a complete and interconnected system, each corresponding to different physical properties of the rock and mutually supporting each other, providing comprehensive and strongly correlated feature inputs for subsequent prediction models, thus helping to accurately characterize the mechanical properties of hard formations.

[0073] The dynamic Poisson's ratio is used to describe the ratio of the lateral strain to the longitudinal strain when the rock is stressed. It is a parameter for calculating the compressive strength and cohesion characteristics and is derived based on the extraction and derivation of logging curves. The shale content is used to reflect the proportion of shale components in the formation and is obtained by calculating the natural gamma parameter. The input is the calculation of the mechanical parameters of compressive strength and cohesion.

[0074] Determining the vertical depth corresponding to different well depths through geometric relationships specifically includes: During the drilling process, a measurement-while-drilling instrument is used to synchronously obtain the well depth and well deviation angle, with a frequency not less than 1 time per 0.125 m; calculating the vertical depth, and the calculation formula is:

[0075] H = L×cosα

[0076] where H is the vertical depth, L is the well depth, and α is the well deviation angle;

[0077] Adaptive calculations are performed for different trajectory types of the wellbore. The different trajectory types of the wellbore include vertical sections, inclined sections, and curved sections. For the curved section, the interval is encrypted, key well depth points are selected, and linear interpolation is used to obtain the logging parameters corresponding to the vertical depth. The vertical depth is divided into three types of intervals according to the trajectory type, and a table containing the vertical depth interval, trajectory type, well depth range, and logging parameters is organized, and data stratification binding is performed to obtain the wellbore trajectory data.

[0078] The specific linear interpolation is as follows:

[0079] Given two data points as (x0, y0) and (x1, y1) respectively, and the interpolation y at a certain x between x0 and x1 is required; assuming x0 < x < x1, the linear interpolation calculation formula is:

[0080]

[0081] where x is the abscissa well depth of the point where interpolation calculation is required, y is the corresponding ordinate logging parameter, and (x0, y0) and (x1, y1) are two known data points.

[0082] In this application, given two points, the logging parameters of the intermediate well depth can be calculated through the formula. The logic is simple, the calculation is efficient, the data of key well depth points can be utilized, the parameter blank of the 0.125 m interval of the entire well section can be filled, the interruption of mechanical parameter calculation caused by data fault can be avoided, and at the same time, the interpolation result fits the change trend of the logging data, with small error, ensuring that each vertical depth has matching parameter support.

[0083] The calculation formula for the uniaxial compressive strength is:

[0084]

[0085] where It represents the uniaxial compressive strength of rock, with the unit being MPa, which is the maximum pressure that rock can withstand under uniaxial compression conditions; It is the hard formation coefficient, a parameter related to the characteristics of hard formations; This is an oilfield experience coefficient, determined based on the actual conditions and experience of a specific oilfield. It represents the dynamic Poisson's ratio, which describes the ratio of lateral strain to longitudinal strain in a rock under stress. This indicates the density of the formation, expressed in g / cm³. 3 , is the mass per unit volume of the rock strata; It indicates the clay content, reflecting the proportion of clay components in the strata; This indicates the longitudinal wave velocity, with units of m / s;

[0086] The formula for calculating formation cohesion is:

[0087]

[0088] in γ represents the formation cohesion, measured in MPa, and refers to the bond strength between rock particles; γ is the hardness coefficient, which is related to the hardness of the rock. It is an adjustment coefficient, a parameter used to adjust the calculation of formation cohesion; Indicates the dynamic Poisson's ratio; This indicates the density of the formation, expressed in g / cm³. 3 ; Indicates the clay content; This represents the longitudinal wave velocity, measured in m / s.

[0089] This application achieves a deep correlation between well logging characteristics and mechanical properties, incorporating core parameters such as extracted dynamic Poisson's ratio, formation density, P-wave velocity, and clay content, and constructing a direct mapping logic between well logging data and mechanical properties. Among them, P-wave velocity and formation density jointly reflect the compactness and elasticity of the rock. The higher the values ​​of both, the harder the rock is, and the greater the corresponding uniaxial compressive strength and formation cohesion. The dynamic Poisson's ratio describes the relationship between lateral strain and longitudinal strain when the rock is under stress.

[0090] The formula for calculating the internal friction angle is:

[0091]

[0092] in It is the internal friction angle. This represents the cohesive strength of the underlying layer, expressed in MPa.

[0093] The formula for calculating clay content is:

[0094]

[0095] in Expressed as clay content, it reflects the proportion of clay components in the formation. Formal parameters related to the calculation of clay content, The natural gamma relative value is used to measure the relative magnitude of the natural gamma logging value with respect to its minimum and maximum values.

[0096] Formal parameters The calculation formula is:

[0097]

[0098] in Formal parameters, specifically the natural gamma relative index, are the original natural gamma logging values ​​at a certain depth point. GRmax and Grmin are the minimum and maximum natural gamma values ​​in the study area, respectively.

[0099] The formula for calculating tensile strength is:

[0100]

[0101] in Tensile strength reflects the rock's ability to resist tensile failure; It is the dynamic elastic modulus, which reflects the elastic deformation characteristics of rock under dynamic loads; Porosity.

[0102] This application strengthens parameter linkage, simplifies calculation and fits the characteristics of hard formations. The internal friction angle is directly derived from the formation cohesion, forming a strong correlation logic between cohesion and internal friction angle. C is the core strength parameter calculated through well logging characteristics, avoiding the error accumulation caused by independently introducing new parameters and ensuring the internal consistency of the mechanical parameter system.

[0103] The formula is derived from fitting experimental data of hard formation shearing and can reflect the shearing characteristics of hard formations with higher cohesion and lower internal friction angle. It is simple in form and can be quickly derived directly from the calculated C value without complex iteration, making it suitable for real-time calculations while drilling.

[0104] The core rock mechanics parameters are organized according to the depth-parameter value correspondence to generate a continuous formation parameter profile, which can be directly used for drilling engineering design, and drilling parameters are optimized to obtain wellbore collapse pressure. Anti-collapse measures are formulated in advance. By organizing the core rock mechanics parameters and aligning them with vertical depth as the reference, missing data is processed by weighted interpolation to generate a continuous visual profile of the core parameters. Based on the visual profile, drilling parameters are optimized and wellbore collapse pressure is derived. Based on geostress and mechanical parameters and wellbore trajectory, collapse pressure and safe density window are derived. Anti-collapse measures are formulated in stages: routine maintenance in low-risk sections, adjustment of drilling fluid and trajectory in medium-risk sections, and emergency density increase, switching to oil-based drilling fluid, and real-time monitoring in high-risk sections.

[0105] In another embodiment of the present invention, a rock mechanical parameter prediction system based on well logging curves is provided, including a preprocessing module, a feature extraction module, a wellbore trajectory data acquisition module, a mechanical parameter acquisition module, and a prediction module.

[0106] The preprocessing module is used to collect logging curve data during drilling in hard formations and to preprocess the collected logging curve data. Specifically, it includes removing noise interference, filling missing data, and correcting abnormal data points. The logging curve data includes sonic transit time logging curves, density logging curves, natural gamma logging curves, and resistivity logging curves.

[0107] The feature extraction module extracts feature parameters related to rock mechanics parameters from preprocessed well logging data based on the rock physical properties of hard formations.

[0108] The wellbore trajectory data acquisition module determines the vertical depth corresponding to different well depths through geometric relationships. For key well depth points, linear interpolation is used to read the logging parameters corresponding to the vertical depth. Wellbore trajectory data is obtained by classifying and organizing the vertical depths. The key well depth points include measured data points, trajectory feature points, and engineering marker points.

[0109] The mechanical parameter acquisition module acquires core rock mechanical parameters based on characteristic parameters and wellbore trajectory data. These core rock mechanical parameters include uniaxial compressive strength, formation cohesion, internal friction angle, clay content, and tensile strength.

[0110] The prediction module organizes the acquired core rock mechanics parameters according to the depth-parameter value correspondence, generating a continuous stratigraphic parameter profile.

[0111] Example 1

[0112] At a drilling site in a hard formation, professional logging equipment was used to collect logging curve data, including sonic transit time logging curves, density logging curves, natural gamma logging curves, and resistivity logging curves, according to standard logging procedures. The collected raw data was then imported into a data processing algorithm to perform preprocessing operations such as noise removal, missing data filling, and correction of abnormal data points.

[0113] Table 1 shows the basic input parameters, and characteristic parameters related to rock mechanics parameters are extracted from the preprocessed logging curves.

[0114] Table 1

[0115]

[0116] Table 2 shows the calculated parameters for the hard strata section. The well section analyzed in this case is from 2856.625 to 2907.875 m, and the lithology is coal-rock. The statistical analysis parameters are: minimum clay content of 1.00%, maximum clay content of 80.00%, and average clay content of 7.28%.

[0117] The minimum dynamic Poisson's ratio is 0.18, the maximum dynamic Poisson's ratio is 0.30, and the average dynamic Poisson's ratio is 0.22.

[0118] The minimum dynamic elastic modulus is 1.00 GPa, the maximum dynamic elastic modulus is 12.53 GPa, and the average dynamic elastic modulus is 5.83 GPa.

[0119] The minimum formation cohesion is 0.5000 MPa, the maximum formation cohesion is 4.6265 MPa, and the average formation cohesion is 2.5814 MPa.

[0120] The minimum brittleness index was 39.446%, the maximum was 51.391%, and the average was 46.030%.

[0121] The minimum tensile strength is 0.057 MPa, the maximum tensile strength is 1.075 MPa, and the average tensile strength is 0.488 MPa.

[0122] The minimum uniaxial compressive strength is 0.940 MPa, the maximum uniaxial compressive strength is 19.423 MPa, and the average uniaxial compressive strength is 7.902 MPa.

[0123] The minimum shear modulus is 1.000 GPa, the maximum shear modulus is 18.211 GPa, and the average shear modulus is 8.305 GPa.

[0124] The minimum internal friction angle is 34.084°, the maximum internal friction angle is 34.575°, and the average internal friction angle is 34.345°.

[0125] The minimum drillable grade value is 2.723, the maximum drillable grade value is 4.775, and the average drillable grade value is 4.003.

[0126] The minimum fracture toughness is 0.209 MPa·m. 0.5 The maximum fracture toughness is 0.911 MPa·m. 0.5 The average fracture toughness is 0.589 MPa·m. 0.5 .

[0127] Table 2

[0128]

[0129] Table 3 shows the calculated parameters for the entire well section. The well section analyzed in this case is from 2780 to 2941.875 m, and includes all lithologies. The statistical analysis parameters are as follows:

[0130] The minimum mud content was 1.00%, the maximum mud content was 80.00%, and the average mud content was 8.21%.

[0131] The minimum dynamic Poisson's ratio is 0.18, the maximum dynamic Poisson's ratio is 0.30, and the average dynamic Poisson's ratio is 0.22.

[0132] The minimum dynamic elastic modulus is 1.00 GPa, the maximum dynamic elastic modulus is 46.37 GPa, and the average dynamic elastic modulus is 16.29 GPa.

[0133] The minimum formation cohesion is 0.5000 MPa, the maximum formation cohesion is 8.5275 MPa, and the average formation cohesion is 4.5216 MPa.

[0134] The minimum brittleness index was 39.446%, the maximum brittleness index was 63.925%, and the average brittleness index was 53.183%.

[0135] The minimum tensile strength is 0.057 MPa, the maximum tensile strength is 2.707 MPa, and the average tensile strength is 0.811 MPa.

[0136] The minimum uniaxial compressive strength is 0.940 MPa, the maximum uniaxial compressive strength is 100.616 MPa, and the average uniaxial compressive strength is 17.348 MPa.

[0137] The minimum shear modulus is 1.000 GPa, the maximum shear modulus is 42.766 GPa, and the average shear modulus is 9.593 GPa.

[0138] The minimum internal friction angle is 30.878°, the maximum internal friction angle is 41.273°, and the average internal friction angle is 35.770°.

[0139] The minimum drillable grade value is 2.723, the maximum drillable grade value is 8.757, and the average drillable grade value is 5.984.

[0140] The minimum fracture toughness is 0.209 MPa·m. 0.5 The maximum fracture toughness is 2.158 MPa·m. 0.5 The average fracture toughness is 1.124 MPa·m. 0.5 .

[0141] Table 3

[0142]

[0143] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for predicting rock mechanical parameters based on well logging curves, characterized in that, It includes the following steps: During the process of drilling in hard formations, logging curve data is collected and the collected logging curve data is preprocessed, specifically including removing noise interference, filling in missing data, and correcting abnormal data points. The logging curve data includes acoustic travel time logging curves, density logging curves, natural gamma logging curves, and resistivity logging curves; Based on the petrophysical properties of hard formations, characteristic parameters related to rock mechanical parameters are extracted from the preprocessed logging curve data; The vertical depth corresponding to different well depths is determined through geometric relationships. For key well depth points, linear interpolation is used to read the logging parameters corresponding to the vertical depth, and wellbore trajectory data is obtained by classifying and organizing the vertical depth; The key well depth points include measured data points, trajectory characteristic points, and engineering marker points; The linear interpolation for reading the logging parameters corresponding to the vertical depth specifically includes: Given two data points as (x0, y0) and (x1, y1) respectively, and the interpolation y at a certain x between x0 and x1 is required. Let x0 < x < x1, then the linear interpolation calculation formula is: where x is the abscissa well depth of the point where interpolation calculation is required, y is the corresponding ordinate logging parameter, and (x0, y0) and (x1, y1) are two known data points; Determining the vertical depth corresponding to different well depths through geometric relationships specifically includes: During the drilling process, the well depth and well deviation angle are synchronously obtained, with a frequency not lower than 1 time / 0.125 m; The vertical depth H is obtained through the following formula: H = L × cosα In the formula, L is the well depth and α is the well deviation angle; Adaptive calculations are performed for different trajectory types of drilling. The different trajectory types of drilling include vertical sections, inclined sections, and curved sections. The interval is encrypted for the curved section, key well depth points are selected, and linear interpolation is used to obtain the logging parameters corresponding to the vertical depth; The vertical depth is divided into three types of intervals according to the trajectory type, and is organized into a table containing the vertical depth interval, trajectory type, well depth range, and logging parameters, and data layering binding is performed to obtain wellbore trajectory data; Core rock mechanical parameters are obtained based on the characteristic parameters and wellbore trajectory data. The core rock mechanical parameters include uniaxial compressive strength, formation cohesion, internal friction angle, shale content, and tensile strength; The obtained core rock mechanical parameters are sorted according to the depth-parameter value correspondence relationship to generate a continuous formation parameter profile.

2. The method for predicting rock mechanical parameters based on well logging curves according to claim 1, characterized in that, The acoustic travel time logging curve is obtained by emitting acoustic waves to the formation and receiving the reflected signals, and recording the propagation time of the acoustic waves in the formation; The density logging curve is obtained by using the Compton scattering effect of gamma rays and formation materials to measure the bulk density of the formation; The natural gamma logging curve is obtained by collecting the gamma ray intensity released by the natural radioactive elements in the formation itself; The resistivity logging curve is obtained by applying an alternating current to the formation and measuring the resistance ability of the formation to the current.

3. The method for predicting rock mechanical parameters based on well logging curves according to claim 1, characterized in that, The characteristic parameters related to rock mechanical parameters include longitudinal wave velocity, formation density, natural gamma parameter, dynamic Poisson's ratio, and shale content. Among them, the longitudinal wave velocity is obtained from the preprocessed acoustic travel time logging curve, and the specific calculation formula is: V p =1 / Δt Where V p It is expressed as the longitudinal wave velocity, the propagation speed of the longitudinal wave in the rock, and Δt represents the sound wave transit time, which reflects the time required for the sound wave to propagate in a unit thickness of rock. The formation density is directly extracted from the pre - processed density log curve and is expressed as the mass per unit volume of the formation rock; the natural gamma parameters include the natural gamma log value, the natural gamma value of the pure shale layer in the target formation, the natural gamma value of the pure sandstone layer in the target formation, and the Hilchie index. The natural gamma log value is directly extracted from the pre - processed natural gamma log curve; The natural gamma value of the pure shale layer in the target formation and the natural gamma value of the pure sandstone layer in the target formation are determined based on the statistical values of the pure lithology segments in the target formation from the natural gamma log curve; The Hilchie index is related to the geological age and is used to calculate the shale content.

4. The method for predicting rock mechanical parameters based on well logging curves according to claim 3, characterized in that, The dynamic Poisson's ratio is used to describe the ratio of the lateral strain to the longitudinal strain when the rock is stressed. It is a parameter for calculating the uniaxial compressive strength and cohesion characteristics and is derived by extraction and derivation based on the log curve; the shale content is used to reflect the proportion of the shale component in the formation and is calculated through the natural gamma parameters.

5. The method for predicting rock mechanical parameters based on well logging curves according to claim 1, characterized in that, The formula for calculating the uniaxial compressive strength is: in This represents the uniaxial compressive strength of rock, expressed in MPa. It is the hard stratum coefficient; This is an oilfield experience coefficient; Indicates the dynamic Poisson's ratio; This indicates the density of the formation, expressed in g / cm³. 3 ; It indicates the clay content, reflecting the proportion of clay components in the strata; This represents the longitudinal wave velocity, measured in m / s.

6. The method for predicting rock mechanical parameters based on well logging curves according to claim 1, characterized in that, The formula for calculating the formation cohesion is: in This represents formation cohesion, expressed in MPa. It is the hardness coefficient, which is related to the hardness of the rock; It is an adjustment coefficient, a parameter used to adjust the calculation of formation cohesion; Indicates the dynamic Poisson's ratio; This indicates the density of the formation, expressed in g / cm³. 3 ; It indicates the clay content, reflecting the proportion of clay components in the strata; This represents the longitudinal wave velocity, measured in m / s.

7. The method for predicting rock mechanical parameters based on well logging curves according to claim 1, characterized in that, The formula for calculating the internal friction angle is: in It is the internal friction angle. This represents the cohesive strength of the underlying layer, expressed in MPa. The formula for calculating the shale content is: in Expressed as clay content, it reflects the proportion of clay components in the formation. Formal parameters related to the calculation of clay content, The natural gamma relative value is used to measure the relative magnitude of the natural gamma logging value with respect to its minimum and maximum values. Formal parameters The calculation formula is: in Formal parameters, specifically the natural gamma relative index. GR is the original natural gamma logging value at a certain depth point. max Gr min These are the minimum and maximum values ​​of natural gamma within the study area, respectively. The formula for calculating the tensile strength is: in Tensile strength reflects the rock's ability to resist tensile failure; It is the dynamic elastic modulus; Porosity.

8. The method for predicting rock mechanical parameters based on well logging curves according to claim 1, characterized in that, The obtained core rock mechanical parameters are sorted according to the depth - parameter value correspondence relationship to generate a continuous formation parameter profile. Specifically, by sorting the core rock mechanical parameters, aligning and sorting them based on the vertical depth, and processing the missing data through weighted interpolation, a continuous formation parameter profile is generated.

9. A rock mechanics parameter prediction system based on well logging curves, characterized in that, It includes a pre - processing module, a feature extraction module, a wellbore trajectory data acquisition module, a mechanical parameter acquisition module, and a prediction module; The pre - processing module is used to collect log curve data during the drilling process in hard formations and pre - process the collected log curve data, specifically including removing noise interference, filling missing data, and correcting abnormal data points. The log curve data includes the acoustic transit time log curve, density log curve, natural gamma log curve, and resistivity log curve; The feature extraction module extracts feature parameters related to rock mechanical parameters from the pre - processed log curve data based on the petrophysical properties of hard formations; The wellbore trajectory data acquisition module determines the vertical depth corresponding to different well depths through geometric relationships. For key well depth points, linear interpolation is used to read the log parameters corresponding to the vertical depth, and the wellbore trajectory data is obtained through classification and sorting of the vertical depth. The key well depth points include measured data points, trajectory feature points, and engineering landmark points. The linear interpolation for reading the log parameters corresponding to the vertical depth specifically includes: Given two data points as (x0, y0) and (x1, y1) respectively, and it is required to interpolate y at a certain x between x0 and x1. Let x0 < x < x1, then the linear interpolation formula is: where x is the horizontal coordinate well depth of the point where interpolation calculation is required, y is the corresponding vertical coordinate log parameter, and (x0, y0) and (x1, y1) are the two known data points; Determining the vertical depth corresponding to different well depths through geometric relationships specifically includes: synchronously obtaining the well depth and well deviation angle during the drilling process, with a frequency not less than 1 time / 0.125 m. The vertical depth H is obtained through the following formula: H = L×cosα In the formula, L is the well depth and α is the well deviation angle; Adaptive calculations are performed for different drilling trajectory types, including vertical, inclined, and curved sections. Intervals are fined for curved sections, key well depth points are selected, and linear interpolation is used to calculate the logging parameters for the corresponding vertical depth. The vertical depth is divided into three categories according to the trajectory type, and the data is organized into a table containing the vertical depth interval, trajectory type, well depth range, and logging parameters. Data is then layered and bound to obtain wellbore trajectory data. The mechanical parameter acquisition module acquires core rock mechanical parameters based on characteristic parameters and wellbore trajectory data. These core rock mechanical parameters include uniaxial compressive strength, formation cohesion, internal friction angle, clay content, and tensile strength. The prediction module organizes the acquired core rock mechanics parameters according to the depth-parameter value correspondence, generating a continuous stratigraphic parameter profile.

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