Calculation method of well pattern magnetic distance measurement error

Abstract

The invention discloses a well pattern magnetic distance measurement error calculation method, and particularly relates to the technical field of multi-well-site well pattern configuration magnetic distance measurement, and the method comprises the following steps: collecting magnetic distance measurement data of a drilling path section, constructing an error parameter matrix, and extracting main shaft parameters; identifying the continuity abnormity of the intermediate path and generating a compensation factor; determining a middle position point connected with the target well pair and estimating an error parameter of the middle position point; correcting error estimation through an interpolation algorithm in combination with a compensation factor; all error parameters are unified to the same coordinate system and then superposed, eigenvalue decomposition is carried out, the error principal axis length and the direction angle between the target well pairs are obtained, and spatial quantitative expression of the error range is completed; according to the invention, through error parameter matrix modeling and main shaft extraction, accurate space expression of multi-path segment magnetic distance measurement errors is realized; a path anomaly perception and compensation mechanism is introduced, so that the robustness of error estimation of the middle position point is improved; and completing quantitative calculation of the error range between the target well pairs based on coordinate unification and matrix synthesis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of well pattern configuration ranging, and more particularly to a well pattern magnetic ranging error calculation method. BACKGROUND

[0002] In the development of oil and gas cluster well pattern, with the sharp increase in the number of horizontal wells and branch wells, the interwell space layout becomes increasingly crowded, and the well pattern configuration tends to be complex. How to accurately control the ranging error between different wells and ensure the safe spacing of the trajectory has become a key technical challenge to ensure the safety of drilling operations and the accuracy of guidance. Magnetic ranging, as a non-contact interwell positioning method suitable for high interference underground environment without electrical connection, has become an important technical solution for well pattern ranging.

[0003] In the estimation of magnetic ranging error, the engineering often adopts a segmented error modeling method, which quantifies the sensor measurement error, well depth measurement error, wellbore attitude disturbance, tool eccentric installation error, etc. in each drilling path segment, and superimposes the errors in the form of a covariance matrix. However, the existing method usually assumes that the error distribution between each measuring point is stationary and continuously changes in space, lacking a dynamic feedback mechanism for the actual geometric state of the path, especially in the presence of abnormal conditions such as micro-dog leg, local deformation, and wellbore mutation in the middle path. The error estimation is still based on the standard interpolation path. This static interpolation modeling method will cause serious overestimation in non-linear path patterns, causing the error estimation of intermediate points to deviate from the actual error spatial distribution.

[0004] Especially in target well pairs (such as A-C wells) connected by intermediate wells, the error estimation deviation of the intermediate path segment will directly affect the overall error synthesis result. When there is an unidentified continuity anomaly in the intermediate well segment, the interpolation error underestimation will result in an overly idealized final synthesized error ellipse, which fails to truly reflect the expansion range of the magnetic ranging spatial uncertainty. In scenarios where the well spacing is close to the design tolerance limit, this will cause magnetic signal misjudgment, trajectory interference misdirection, and even serious engineering consequences such as wellbore collision and loss of control. This is particularly risky in complex shale well clusters and high-density parallel well layouts. Therefore, the present application proposes a well pattern magnetic ranging error calculation method to solve the above problems. SUMMARY

[0005] To achieve the above purpose, the present application provides the following technical solutions: A well pattern magnetic ranging error calculation method, comprising the following steps: Collecting magnetic ranging data of each drilling path segment in the well pattern, identifying multiple independent error sources, constructing an error parameter matrix for each drilling path segment, and extracting principal axis parameters reflecting error spatial distribution through eigenvalue decomposition; analyzing whether there is continuity abnormality in the intermediate drilling path connecting the target well pair, judging whether there is trajectory abnormality by comparing the path attitude change amplitude with a preset path smoothness threshold value, and generating a compensation factor for adjusting error calculation according to the judgment result; determining an intermediate position point for connecting the target well pair, selecting a cross section perpendicular to the path extension direction in a spatial configuration containing multiple drilling paths, and calculating the intermediate position point which is the position point closest to each path in geometry on the cross section; calculating the error parameter of the intermediate position point by an interpolation algorithm according to the relative distance from the target well pair to the intermediate position point and the error parameters of the multiple path segments, and applying the compensation factor to the interpolation process to correct the influence of path abnormality on error estimation; unifying the error parameters of the multiple path segments related to the target well pair and the error parameter of the intermediate position point into the same coordinate reference system to make various error sources superimposable, superimposing the converted error parameter matrix, performing eigenvalue decomposition on the obtained matrix, extracting the main axis length and direction angle of the magnetic ranging error between the target well pair, and completing the spatial quantitative expression of the error range between the target well pair.

[0006] In a preferred embodiment, the step of constructing the error parameter matrix of each drilling path segment includes: collecting magnetic induction intensity, well depth information and installation attitude data of the magnetic ranging tool related to the drilling path; respectively calculating error quantization parameters caused by eccentric installation error, sensor measurement deviation, relative well depth measurement deviation and spatial attitude change in the drilling path; establishing a weight function of different error items according to the error quantization parameters, and expressing the influence of each error item on the ranging result in the form of covariance; combining the covariances of multiple error items to form an error parameter matrix for describing the spatial uncertainty of the drilling path segment.

[0007] In a preferred embodiment, the step of extracting the main axis parameter reflecting the error spatial distribution includes: performing matrix decomposition on the error parameter matrix to convert the matrix into a decomposition form containing two eigenvectors and two eigenvalues; squaring the eigenvalues to obtain the scale values of the error in two main directions, and determining the lengths of the long axis and the short axis of the error space according to the scale values; taking the directions corresponding to the eigenvectors as the two main directions of the error space for describing the spatial orientation of the error ellipse; taking the main axis length and the main direction together as the error spatial distribution parameter of the drilling path segment.

[0008] In a preferred embodiment, when performing matrix decomposition on the error parameter matrix, one of the following algorithms is used: Jacobi iteration method, eigenvalue extraction method based on QR decomposition, or singular value decomposition method, to obtain two eigenvectors and corresponding eigenvalues for constructing the error ellipse parameters between the target well pair.

[0009] In a preferred embodiment, the generation logic of the compensation factor refers to: extracting the variation of inclination angle and the variation of azimuth angle of a plurality of consecutive measurement points on the intermediate drilling path; For each pair of adjacent measurement points, calculating the attitude change amplitude, which is a spatial offset measure composed of the variation of inclination angle and the variation of azimuth angle of the two consecutive measurement points, which can be calculated by the included angle between the corresponding attitude vectors; Comparing the attitude change amplitude corresponding to each pair of adjacent measurement points with the path smoothness threshold value, if the attitude change amplitude exceeds the threshold value, marking the path segment between the two measurement points as having trajectory anomaly; In all path segments marked as trajectory anomaly, calculating the total length of the abnormal segment, and calculating the ratio with the length of the entire intermediate path as the length proportion index of the path; In all abnormal path segments, calculating the average of the attitude change amplitude as the attitude change index of the path; Normalizing the length proportion index and the attitude change index to the range of zero to one respectively, and then performing nonlinear function processing respectively, and multiplying the two processing results to obtain the disturbance influence value; Limiting the disturbance influence value within a preset range according to the limitation rule as the compensation factor in the interpolation correction process.

[0010] In a preferred embodiment, the generation of the compensation factor includes the following nonlinear function processing steps: Inputting the length proportion index into the length influence function, the length influence function maps the input proportion to different levels of output values according to the preset segmentation rule; there is a determined correspondence between the input interval and the output amplitude of each level, so that the proportion of abnormalities in the path can be reflected in the output results in a segmented manner; Inputting the attitude change index into the angle change function, the angle change function processes the input value according to the preset threshold interval, the output remains at a basic level in the interval below the lowest threshold, when the input value is between the lowest threshold and the highest threshold, the function output monotonically increases with the input value, the specific response amplitude is controlled by the preset continuous change rule, and the output in the interval above the highest threshold increases according to the enhancement rule, to reflect the level change characteristics of the influence of attitude change on error evaluation.

[0011] In a preferred embodiment, the step of determining the intermediate position point for connecting the target well pair comprises: a plurality of spatial measurement points are selected in the intermediate drilling path connecting the target well pair, and the three-dimensional coordinates and the borehole extension direction corresponding to each measurement point are extracted; a reference measurement point is selected in the connecting path, and the borehole extension direction of the reference measurement point is taken as a normal vector to construct a spatial section perpendicular to the direction, the adjacent path segments are projected into the spatial section to form a path geometric intersection area; the intersection area point closest in spatial position to the plurality of path segments in the spatial section is searched as the intermediate position point of the error propagation path between the target well pair.

[0012] In a preferred embodiment, during the interpolation process, a plurality of measurement points located on both sides of the intermediate position point in the path connecting the target well pair are selected, and the error ellipse semi-axis length parameters of each measurement point are extracted; the spatial distances between the intermediate position point and each measurement point are calculated, and interpolation weights are assigned according to the distances; the error ellipse semi-axis length of the intermediate position point is calculated according to the error ellipse semi-axis length of each measurement point and the interpolation weight by using a cubic spline interpolation method, as an initial interpolation result; a compensation factor is applied to the initial interpolation result, and the error parameter of the intermediate position point is corrected to form a final error parameter for error synthesis.

[0013] In a preferred embodiment, the step of completing the spatial quantitative expression of the error range between the target well pair specifically comprises: the error parameter matrices of a plurality of path segments related to the target well pair and the error parameter matrix of the intermediate position point are extracted, and the coordinate reference directions corresponding to each error parameter matrix are determined; each error parameter matrix is unified to a common coordinate reference system through coordinate transformation, and the unified error parameter matrices are added item by item to form a total error parameter matrix of the target well pair; the total error parameter matrix is subjected to eigenvalue decomposition, and the major axis length and the direction angle of the error ellipse between the target well pair are extracted to complete the spatial quantitative expression of the error range of the target well pair.

[0014] The technical effects and advantages of the present application are: The application realizes accurate mapping from physical data acquisition to spatial uncertainty expression by collecting magnetic ranging data of each drilling path segment in the well pattern, identifying multiple independent error sources, and constructing an error parameter matrix of each drilling path segment. Unlike the mean value estimation or single-axis disturbance assumption in the traditional method, the application comprehensively reflects the statistical distribution characteristics of the error in the three-dimensional space through covariance modeling. Further, the principal axis parameters reflecting the spatial distribution of the error are extracted through eigenvalue decomposition, so that the long and short axis directions and scales of the error space are quantitatively expressed, which not only improves the engineering interpretation ability of the error model, but also provides a clear direction basis for subsequent error propagation and synthesis. This method improves the spatial adaptability of error expression, especially suitable for multi-segment drilling path fusion and regional well pattern error synthesis scenarios, and enhances the systematicness and accuracy of error modeling.

[0015] The application establishes a trajectory anomaly recognition method based on the comparison of the path attitude change amplitude and the path smoothness threshold when analyzing the intermediate drilling path of the target well pair. By identifying the attitude change between the continuous measuring points in the path, it is judged whether there is a non-smooth abnormal segment, and a compensation factor is generated accordingly to correct the error estimation in the interpolation process. Unlike the traditional interpolation method which only relies on the position relationship of the path, the application introduces dynamic perception and disturbance compensation logic for the continuity anomaly of the path in the interpolation algorithm, which can effectively avoid the problem of underestimating the error caused by local collapse, slight dogleg or nonlinear disturbance. Especially when the spatial distance between the target well pair has approached the safety tolerance threshold, the introduction of the compensation mechanism greatly improves the reliability and safety margin of the estimated results. This active compensation method for path structure enhances the engineering adaptability of the error estimation process under complex strata and non-ideal trajectory conditions.

[0016] The application uniformly converts the error parameters of multiple path segments related to the target well pair and the error parameters of the intermediate position points to the same coordinate reference system, so that the error matrices from different sources have direct synthesis feasibility. By mapping each error component to a unified spatial reference direction through a coordinate transformation matrix, and then adding the converted error parameter matrices item by item, a total error matrix representing the error propagation result of the target well pair is finally formed. On this basis, the principal axis length and direction angle of the magnetic ranging error spatial distribution between the target well pair are obtained through eigenvalue decomposition of the matrix, and the quantitative expression of the error space is completed. This method discards the empirical amplification coefficient or segmented weighting mechanism in error estimation, and establishes a spatial evaluation system based on the physical structure of the path and the principal axis of the error. The finally output error principal axis parameters can be used for well interference risk control, trajectory relative positioning judgment, drilling guidance decision and other key links, greatly improving the precision, reliability and controllability of the well pattern measurement system in actual engineering. BRIEF DESCRIPTION OF DRAWINGS

[0017] For the convenience of those skilled in the art to understand, the present application will be further described below in conjunction with the accompanying drawings; Figure 1 A schematic diagram of a well pattern magnetic ranging error calculation method in the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0019] Reference Figure 1 The following embodiments are obtained: Embodiment 1: A well pattern magnetic ranging error calculation method, comprising the following steps: collecting magnetic ranging data of each drilling path segment in the well pattern, identifying a plurality of independent error sources, constructing an error parameter matrix of each drilling path segment, and extracting principal axis parameters reflecting error space distribution through eigenvalue decomposition; obtaining original magnetic ranging related data from the drilling process, and combining physical structure and measurement mechanism to identify and model different types of errors that may affect ranging accuracy, and by expressing the errors in a parameterized manner as a matrix form and using eigenvalue decomposition means, the long axis and short axis information of the error ellipse is extracted, thereby establishing a spatial error expression of each path segment.

[0020] Analyzing whether there is a continuity anomaly in the intermediate drilling path connecting the target well pair, judging whether there is a trajectory anomaly by comparing the path attitude change amplitude with a preset path smoothness threshold, and generating a compensation factor for adjusting error calculation according to the judgment result; this step is used to identify whether there is a discontinuity phenomenon such as mutation and disturbance in the drilling path connecting the target well pair in the well pattern in space, by quantifying the joint change amount of the inclination angle and the azimuth angle and comparing it with the established smoothness standard, the potential abnormal section is identified, and the compensation factor that can correct the interpolation accuracy is constructed based on this result, to improve the stability and accuracy of subsequent error interpolation calculation.

[0021] Determining the intermediate position point for connecting the target well pair, selecting a cross section perpendicular to the path extension direction in the spatial configuration containing a plurality of drilling paths, and calculating the position point on the cross section that is most closely related to each path geometry, i.e. the intermediate position point; this step is used to clearly determine the key node of error propagation in space, by analyzing the overall configuration of the path between the target well pair in space, constructing a reference cross section perpendicular to the main direction of the path, and selecting the intersection point closest to the geometric center of the path from the cross section as the intermediate calculation reference point for error interpolation and synthesis, thereby providing a unified spatial reference for the subsequent steps.

[0022] According to the relative distance between the target well pair and the intermediate position point and the error parameters of the plurality of path segments, the error parameter of the intermediate position point is calculated by an interpolation algorithm, and a compensation factor is applied to the interpolation process to correct the influence of the path anomaly on the error estimation; this step calculates the estimated value of the intermediate position point in the spatial error field by considering the distance distribution characteristics between the target well pair and the intermediate position point, combining the error parameters of the aforementioned path segments, using an interpolation algorithm, and introducing the compensation factor generated in the previous step in the interpolation calculation to adjust the error distortion caused by the path anomaly, achieving a more realistic error estimation.

[0023] The error parameters of the plurality of path segments related to the target well pair and the error parameter of the intermediate position point are uniformly converted to the same coordinate reference system, so that various error sources have superimposability; the converted error parameter matrix is superimposed, and the obtained matrix is subjected to eigenvalue decomposition to extract the principal axis length and direction angle of the magnetic ranging error between the target well pair, completing the spatial quantitative expression of the error range between the target well pair. This step is used to unify the error parameter matrices in different drilling path coordinate systems to the well pattern public coordinate system to ensure the compatibility and compositeness of various error sources, and to realize the consistent expression of the matrix form after coordinate conversion, then to sum the matrices to form the total covariance matrix of the target well pair, and to extract the principal axis direction and scale of the error ellipse between the well pair through eigenvalue decomposition, to realize the quantitative description of the error range in space, and to provide a basis for well pattern planning and collision risk control.

[0024] The step of constructing the error parameter matrix of each drilling path segment includes: in actual engineering, the error sources of each drilling path segment in the well pattern are closely related to the magnetic field environment, structural posture and instrument state, therefore, this embodiment first continuously collects magnetic induction intensity data during drilling, and obtains a series of three-dimensional magnetic field vector data through stratigraphic magnetic environment measurement and measurement-while-drilling modules. At the same time, well depth information is collected, including real well depth, vertical depth and measured depth along the path, to depict the accurate distribution of spatial position. In addition, the installation posture data of the magnetic ranging tool is also collected, and the three-dimensional posture of the instrument is described by the tool face direction, inclination angle and azimuth angle. This step provides a unified and continuous input basis for subsequent error term calculation, so that all errors can be quantified under the same data framework.

[0025] The error quantification parameters caused by eccentric installation error, sensor measurement error, relative well depth measurement error and spatial attitude change in the drilling path are calculated respectively. After obtaining the complete input data, each type of error is quantified item by item. The four types of errors are disclosed by the specific calculation method in this embodiment. The eccentric installation error is determined by analyzing the radial offset of the magnetic ranging tool's appearance structure offset and the wellbore center, for example, when the tool is offset by five millimeters relative to the wellbore center, the error contribution values in the radial direction and the axial direction can be obtained by simulating the magnetic field disturbance model. The sensor measurement error is quantified by comparing the magnetic field strength of the continuous measurement points with the theoretical formation magnetic field, combined with the stability of repeated measurement. In actual situations, if there is a zero drift in the sensor, the measurement noise parameter can be constructed according to the offset in each measurement period. The relative well depth measurement error is obtained by comparing the response difference of the well depth measurement tool under different loading conditions, for example, when there is an error of two meters in the well depth reading between measurement points, the difference can be taken as the error quantification value in the well depth direction. The error quantification parameter caused by spatial attitude change is calculated according to the tool face direction change and the wellbore attitude change, for example, when the hole inclination angle and the hole azimuth angle change by three degrees and five degrees respectively between two measurement points, the tool attitude error component is obtained by the attitude disturbance model. These error quantification parameters are obtained in an independent form, which provides a basis for establishing the subsequent weight function.

[0026] The weight function of each error term is established according to the error quantization parameter, and the influence of each error term on the ranging result is expressed in the form of covariance. In this embodiment, the error quantization parameter of each type is input into the weight function to reflect its relative importance in the formation of the magnetic ranging error. In the embodiment of the present application, the "weight function" can adopt a segmented response model based on error amplitude to realize the hierarchical response adjustment of error parameters. It should be noted that the construction method of the weight function is not limited to the segmented response model, and the skilled person in the art can also select linear function, exponential function, neural network fitting function, fuzzy logic control model and other existing mathematical modeling methods to construct the weight function according to engineering requirements, calculation resources, error sensitivity control targets and other factors. The above construction methods are all conventional methods in the art, which have clear public literature and commercial software support, and their principles are well known to those skilled in the art. For example, when the weight function adopts a segmented response model based on error amplitude, for example, when the value of the eccentric installation error is in the reference interval, the weight remains the reference value, and when it exceeds the set threshold, the weight increases according to the enhancement rule, so that the proportion of the eccentric installation error in the overall error is reasonably expressed. The same logic is also adopted for the sensor measurement deviation, the relative well depth measurement deviation and the error caused by the spatial attitude change. According to the results processed by the weight function, the influence of each error term is converted into the form of covariance, wherein the diagonal elements of the covariance matrix correspond to the error variance along each coordinate axis direction, and the non-diagonal elements represent the correlation of the error in different directions. For example, if the sensor measurement deviation produces a zero point zero one square unit of variance in the radial direction at a certain measuring point, and there is a positive correlation with the axial direction, the corresponding non-diagonal position in the covariance matrix can reflect this relationship.

[0027] In the present application, when constructing the error parameter matrix of each drilling path segment, the error quantization parameters of the eccentric installation error, the sensor measurement deviation, the relative well depth measurement deviation and the error caused by the spatial attitude change in the drilling path are calculated respectively. The calculation of the above error terms relies on several mature mathematical modeling methods or simulation techniques, such as the simulated magnetic field disturbance model and the attitude disturbance model. It should be noted that the simulated magnetic field disturbance model and the attitude disturbance model are both existing technical means commonly used by the skilled person in the related engineering, and their principles and construction methods have been supported by a large number of public literature or engineering schemes in the fields of magnetic measurement accuracy modeling, error correction of the while-drilling measurement and control system, and magnetic interference evaluation. Therefore, the use of these models in the present application does not constitute a re-creation of the prior art, but a reasonable integration and structural application in a specific technical process.

[0028] The simulation magnetic field disturbance model refers to reconstructing the magnetic field disturbance distribution that the magnetic ranging tool may be subjected to in a simulation environment by establishing the magnetic field interaction relationship between the drilling tool assembly and the surrounding formation and the well wall structure. The model is usually based on finite element analysis, electromagnetic simulation or numerical field solution, and combines the tool material, cable arrangement, tool eccentricity and geomagnetic background conditions to calculate the magnetic vector distortion degree of the area where the tool is located. By comparing the difference between the disturbance field and the ideal magnetic field, the influence of the eccentric installation error or structural interference can be quantitatively evaluated. For example, when there is a 5mm eccentricity, the model can output the radial and axial magnetic flux density changes, which are used to construct the input factors of the error covariance.

[0029] The attitude disturbance model refers to considering the influence of the drilling tool attitude change (such as the hole inclination angle, hole azimuth angle, tool face direction, etc.) on the response capability of the magnetic ranging tool during drilling, and simulating and estimating the error propagation path caused by the tool in space through modeling means. The model is usually based on three-dimensional attitude matrix, direction cosine matrix or quaternion operation, and combines path curvature, turning radius and attitude change speed to deduce the sensitivity change and signal drift range of the magnetic measuring device under different attitude combinations. The model can output the component strength of the attitude disturbance in each main direction, which is used to express the contribution of the attitude change to the ranging uncertainty. The simulation magnetic field disturbance model and the attitude disturbance model described in the present invention do not constitute the novelty points of the technical solutions themselves, but are the conventional engineering modeling means adopted for realizing the error quantization parameter calculation. The principles and structures are technical models that can be directly constructed and applied by those skilled in the art based on general electromagnetic theory and drilling measurement and control engineering principles, have clear prior art basis, and will not affect the core technical protection range of the error interpolation correction and synthesis method of the present invention.

[0030] The step of extracting the principal axis parameters reflecting the error space distribution includes: in the present invention, each drilling path segment has constructed an error parameter matrix reflecting the spatial uncertainty through the previous steps, which is usually a two-dimensional symmetric positive definite matrix with good numerical stability. To obtain the error amplitude of the matrix in the principal direction, matrix decomposition operation is needed. To meet the precision, efficiency and equipment capacity of different calculation scenarios, three common prior art paths are provided in the present invention for engineering deployment selection.

[0031] The first is the Jacobi iteration method, which is suitable for small matrix dimension and high calculation precision requirement of ground fine simulation scenarios, and can finally converge to obtain two sets of eigenvectors and corresponding eigenvalues by iteratively diagonalizing and rotating the largest non-diagonal element in the matrix; Second, the eigenvalue extraction method based on QR decomposition is suitable for high-frequency continuous processing scene in live data stream, has the advantages of fast iteration and matrix stability, and gradually iteratively approximates the diagonal matrix of eigenvalues by decomposing the error parameter matrix into the product form of orthogonal matrix and upper triangular matrix, and then inversely deduces the eigenvector group; Third, the singular value decomposition method is suitable for full-path modeling scene considering the coupling effect of path attitude disturbance, which decomposes the error parameter matrix into the product form of left singular vector matrix, singular value diagonal matrix and right singular vector matrix, wherein the singular value is equivalent to the eigenvalue, and the singular vector is the main direction of error. The above three methods belong to the conventional linear algebra calculation means in the art, and have high engineering usability. The skilled person in the art can select one of them for matrix decomposition processing based on the scene requirements.

[0032] Square the eigenvalues to obtain the scale values of the error in the two main directions, and determine the lengths of the long axis and the short axis of the error space. After the eigenvalue extraction of the error parameter matrix, two real eigenvalues are obtained, which represent the variance values of the error in the main directions. Since the unit of variance is the square of the error amount, in order to restore the spatial scale information, square root operation is required for the two eigenvalues respectively, so as to obtain the actual distribution half-axis lengths of the error space in the main directions of the path segment. In the present application, they are called the lengths of the long axis and the short axis of the error space, wherein the long axis corresponds to the square root of the larger eigenvalue, and the short axis corresponds to the square root of the smaller eigenvalue. For example, if the eigenvalues of the error parameter matrix of a path segment are 0.0144 and 0.0025, the lengths of the long axis and the short axis are 0.12 meters and 0.05 meters respectively, which directly represent the actual uncertainty range of the ranging error in the two directions.

[0033] The directions corresponding to the eigenvectors are taken as the two main directions of the error space, which are used to describe the spatial orientation of the error ellipse. The eigenvalues describe the amplitude of the error in the main axis direction, and the corresponding eigenvectors describe the orientation of the error main axis in space. In the present embodiment, the two orthogonal eigenvectors extracted by the above decomposition method constitute the two main directions in the error space, which are represented as direction cosines or vector coordinates in the three-dimensional coordinate system. In the present application, the two main directions are taken as the spatial orientation of the error ellipse, which directly participates in the subsequent coordinate system and error synthesis process. If the main directions of the error ellipse formed on the path segment point to (0.8, 0.6) and (-0.6, 0.8) respectively, it represents the distribution trend of the error in the forward direction and the lateral direction of the path. This direction information is extremely critical for spatial orientation measurement and control.

[0034] The main shaft length and the main direction are taken as the error space distribution parameters of the drilling path segment, the long axis and the short axis length of the error space and the two main directions of the error space are jointly defined as the error space distribution parameters of the path segment, and are output to subsequent interpolation calculation, intermediate position point error estimation and final error synthesis module as a unified structure. The parameters logically constitute a geometrically complete description of the error ellipse, including amplitude information and spatial direction information, with high data adaptability and mathematical superposition. When multiple path segments are combined in space, the error space distribution parameters of each segment can directly participate in the covariance superposition and error ellipse construction in a unified coordinate system, ensuring that the error analysis between the target well pairs has a strict physical basis and high precision expression ability.

[0035] The generation logic of the compensation factor refers to extracting the inclination angle change and the azimuth angle change of multiple continuous measurement points on the intermediate drilling path, and calculating the attitude change amplitude based thereon. Before generating the compensation factor, the attitude information recorded by multiple measurement points on the intermediate drilling path connecting the target well pair is first obtained, including the inclination angle and the azimuth angle of each point. By combining the inclination angle change and the azimuth angle change between each pair of adjacent measurement points, the attitude change vector of the path is formed. To quantify the spatial offset degree of the change vector, the included angle between the spatial attitude vectors is used as the measurement method of the "attitude change amplitude". For example, if two adjacent measurement points correspond to attitude vectors v1 and v2, and the included angle between them is 12 degrees, then the attitude change amplitude of this path segment is considered to be 12 degrees. Repeat this process to completely traverse the entire intermediate path to obtain the attitude change amplitude sequence of all path segments.

[0036] Compare the attitude change amplitude corresponding to each pair of adjacent measurement points with the path smoothness threshold value, and if the attitude change amplitude exceeds the threshold value, mark the path segment between the two measurement points as having trajectory abnormalities. After obtaining the path attitude change amplitude sequence, compare it with the preset path smoothness threshold value item by item. The path smoothness threshold value is pre-set according to empirical engineering data or simulation results to identify whether the path has unnatural continuous disturbance. If the attitude change amplitude of a path segment exceeds the threshold value, the path segment is marked as an abnormal path segment. For example, when the threshold value is set to 10 degrees, and the attitude change amplitude of a certain segment is 12 degrees, the segment is identified as having trajectory abnormalities. The purpose of this step is to select those abnormal disturbance sections that are most sensitive to attitude change from all path segments, providing a basic data set for subsequent abnormal influence quantification.

[0037] In all the path segments marked as trajectory anomaly, the total length of the abnormal segments is calculated, and the ratio of the length of the entire intermediate path is calculated as the length ratio index of the path; at the same time, in all the abnormal path segments, the average of the attitude change amplitude is calculated as the attitude change index of the path. After marking the abnormal path segments, according to the actual length information of each path in the drilling measurement data, the total length of all the marked abnormal path segments is accumulated and calculated. For example, if the total length of the abnormal segments is 26 meters, and the total length of the entire intermediate path is 130 meters, then the length ratio index of the path is 0.2. At the same time, the attitude change amplitudes corresponding to all the abnormal path segments are averaged to obtain the attitude change index which comprehensively reflects the severity of the path anomaly. For example, if the attitude change amplitudes of five abnormal segments are 12 degrees, 16 degrees, 14 degrees, 15 degrees and 13 degrees, then the average is 14 degrees. The purpose of this step is to quantize the abnormal influence from two directions of spatial position distribution (through the length ratio index) and change amplitude dimension (through the attitude change index), and to provide basic variables for the final disturbance influence value generation.

[0038] The length ratio index and the attitude change index are normalized to the range of zero to one, and then nonlinear function processing is performed respectively, and the two processing results are multiplied to obtain the disturbance influence value; the disturbance influence value is limited in the preset range according to the limitation rule, as a compensation factor in the interpolation correction process. In order to make the subsequent processing have a unified numerical scale, the length ratio index and the attitude change index are respectively converted to the range of zero to one by using the normalization method, for example, the maximum expected value is set to one, the minimum tolerance value is set to zero, and the linear mapping characteristics of the input distribution are maintained. Then, nonlinear function processing is performed on the two index values respectively to enhance the expression ability of abnormal severity to disturbance response. The processed two function results are multiplied to form a disturbance influence value, which reflects the comprehensive influence intensity of the path anomaly in the space and amplitude dimensions. In order to avoid the disturbance influence value exceeding the reasonable engineering range, the value is limited in the preset upper and lower limit range, for example, between 0.8 and 1.5. Finally, the disturbance influence value is used as a compensation factor in the interpolation correction process, which participates in the error estimation and correction of the intermediate position point, and ensures that the error interpolation process has adaptive adjustment ability to the trajectory anomaly.

[0039] The generation of the compensation factor comprises the following nonlinear function processing steps: inputting the length proportion index into a length influence function, and mapping the input proportion to output values of different levels according to a preset segmentation rule. After obtaining the ratio of the total length of the abnormal trajectory segment in the intermediate path to the length of the entire path, the length proportion index is formed. In order to improve the control resolution of the index on the result of the compensation factor, the length influence function with progressive characteristics is set, and the input proportion value is segmented and mapped. The segmentation rule of the length influence function is determined by experience value and simulation data, and the input proportion interval between zero and one is divided into several response intervals, and each response interval corresponds to a fixed or progressive output amplitude. For example, when the length proportion index is less than 10%, the output value is set to 1; when the length proportion index is between 10% and 30%, the output value linearly rises to 1.5; when the length proportion index is greater than 30%, the output value increases exponentially, and the maximum is not more than 2.5. The above setting makes the spatial distribution characteristics of the abnormal trajectory in the path show a segmented and stepped increase in the function output, reflecting the intervention intensity of different levels of abnormality on error estimation.

[0040] The posture change index is input into an angle change function, and the angle change function processes the input value according to a preset threshold interval. After the average value of the abnormal segment posture change amplitude is calculated and normalized to the range between zero and one, the posture change index is formed. In order to accurately express the influence of the strength of the posture disturbance in the path on the error space estimation, the angle change function is introduced to nonlinearly process the index. The construction of the angle change function is based on two preset threshold intervals, i.e. the segmented region composed of the lowest threshold and the highest threshold. In the interval where the input value is lower than the lowest threshold, the function output value is set to the basic level, for example, set to one, indicating that the posture disturbance has not reached the intervention standard; when the input value is between the lowest threshold and the highest threshold, the function output monotonically increases with the input value, and the response amplitude is controlled by the continuous change rule, for example, using a smooth rising cubic function or a logarithmic curve, so that the function output rises from one to one hundred and fifty in this interval; when the input value exceeds the highest threshold, the function output significantly increases according to the enhancement rule, for example, the output value increases by 20% for every 5% increase, and the highest is not more than the set value of 3.0, so as to quickly amplify the influence of serious posture disturbance.

[0041] The output result of the length influence function is multiplied with the output result of the angle change function to form a comprehensive disturbance influence value. The present application does not simply linearly weight the length proportion index and the posture change index, but respectively maps them through independent nonlinear functions, and multiplies the two function results to construct the final disturbance influence value. This multiplication method can ensure that when both indexes are in the high response interval, the disturbance influence value can present a significant increase, and when either index is lower, it remains a relatively moderate adjustment amplitude, avoiding misinjury to normal path segments. For example, when the length influence function output is one point eight and the angle change function output is two point zero, the disturbance influence value is three point six; when they are one and one point two respectively, the disturbance influence value is only one point two, which has obvious dynamic adjustment ability.

[0042] To prevent the disturbance influence value from being too large or too small, resulting in abnormal interpolation results, the present application sets upper and lower limit rules for the disturbance influence value. In engineering implementation, the disturbance influence value is limited within a predetermined interval, for example, the lower limit is set to zero point eight and the upper limit is set to three point zero, and values exceeding this range will be truncated or mapped back to the limited boundary. This limiting process not only ensures the stability of the output of the compensation factor, but also retains the ability to respond differently to different abnormal intensities. The final disturbance influence value is used as a compensation factor in the interpolation correction process, directly affecting the adjustment process of the intermediate position point error parameter, realizing dynamic intervention compensation of error estimation by path abnormalities.

[0043] The step of determining the intermediate position point for connecting the target well pair includes: selecting a plurality of spatial measurement points in the intermediate drilling path connecting the target well pair, and extracting the three-dimensional coordinates and the wellbore extension direction corresponding to each measurement point. To fully reflect the spatial trend and structural change trend of the intermediate path segment, this step first selects a plurality of spatial measurement points at equal intervals from the drilling path connecting the target well pair, and the recommended measurement point interval is not greater than thirty meters to ensure the spatial resolution of the trajectory feature sampling. In actual engineering operation, path nodes are often selected according to logging data or measurement-while-drilling data, for example, five to ten typical position points are selected for subsequent analysis. Each spatial measurement point needs to extract its corresponding three-dimensional coordinates (X, Y, Z) and wellbore extension direction. The wellbore extension direction can be obtained by constructing a direction vector from the front and rear positions of the measurement point, which is used for subsequent spatial projection section construction. The selection basis of multiple spatial measurement points includes but is not limited to: geometric proximity to the midpoint of the connecting line of the target well pair, representativeness of wellbore trajectory change, balance of path segment distribution, etc.

[0044] A reference point is selected in the connection path, and the wellbore extension direction of the reference point is taken as the normal vector to construct a spatial section perpendicular to the direction. The adjacent path segments are projected into the spatial section to form a path geometric intersection area. After extracting the spatial information of multiple spatial points, a reference point is selected as the center point of the analysis section. The reference point is preferably selected from the point closest to the geometric midpoint of the connection line between the target wells to ensure that the section has spatial representativeness. After the reference point is selected, the wellbore extension direction vector corresponding to the reference point is taken as the section normal vector, and a plane perpendicular to the direction is constructed as the spatial section defined in the application. The section is used to project all connection path segments in three-dimensional space uniformly to form a two-dimensional geometric projection view, thereby converting the complex spatial path relationship into a comparable plane structure. All selected point path segments are orthogonally projected to the section to form a path geometric intersection area, which is used to identify the position point where error coupling is most likely to occur.

[0045] The intersection area point closest in spatial position to the multiple path segments in the spatial section is searched as the intermediate position point of the error propagation path between the target well pairs. After the projection of the path segments in the spatial section is completed, this step calculates the projection trajectories of all path segments in the section and constructs a two-dimensional coordinate model thereof. By calculating the minimum distance points between the path segments in the section, the intersection area where the multiple paths are most closely related in geometry in space is identified. The point closest to the center point of the section in the intersection area is further identified, and its three-dimensional position coordinates are determined as the intermediate position point of the error propagation path between the target well pairs. The intermediate position point has two significant features: first, it is the collective geometric center of all path segments in the projected section, and has representativeness of path spatial layout; second, the point has the minimum geometric difference of error propagation path structure, and can be used as the center node of error interpolation. The selection of the point can be obtained by the least Euclidean distance superposition calculation, or by the intersection of path envelope lines.

[0046] The three-dimensional coordinates of the intermediate position point are recorded as the spatial connection reference point between the target well pair for error estimation, and the position point coordinates and direction information are continuously referred to in subsequent error interpolation, compensation factor application, and error synthesis. After completing the identification of the intermediate position point, the spatial position coordinates and the corresponding direction information of the path are required as the core basic data for subsequent error interpolation calculation. The path corresponding direction information can be obtained by constructing a vector connecting the two adjacent measuring points on both sides of the intermediate position point, which is used to define the local coordinate reference system. In the subsequent interpolation algorithm, the interpolation weight needs to be allocated according to the relative distance of the point to the target well pair; in the application process of the compensation factor, it is also necessary to determine whether the point has trajectory abnormalities as a correction reference; in the error parameter synthesis process, the error parameter matrix corresponding to the point needs to be uniformly converted to the common coordinate reference system aligned with the target well pair. The intermediate position point, as the geometric and error fusion hub of the error propagation of the entire path segment, has high spatial representativeness and stability, and is the key node for realizing the continuous expression of the path segment error.

[0047] In the interpolation process, in the path connecting the target well pair, multiple measuring points located on both sides of the intermediate position point are selected, and the error ellipse semi-axis length parameters of each measuring point are extracted. In order to ensure that the interpolation input data covers the path changes before and after the intermediate position point, the present application suggests selecting not less than two measuring points before and after the intermediate position point, and preferentially selecting real trajectory sampling points with a distance of not more than fifty meters. Each measuring point includes corresponding error ellipse parameters, i.e. the length values of the error in the major axis and minor axis directions. For example, the error ellipse major axis and minor axis lengths of the two measuring points on the front side are zero point one eight meters and zero point zero six meters, and the error ellipse major axis and minor axis lengths of the two measuring points on the back side are zero point one four meters and zero point zero four meters. All these data will be used as the basic input for interpolation operation.

[0048] The spatial distances between the intermediate position point and each measuring point are calculated, and the interpolation weight is allocated according to the distance. After the three-dimensional coordinates of each measuring point are extracted, the Euclidean space distance from each measuring point to the intermediate position point is calculated in this step, which is used as the basic factor for constructing the interpolation weight. The interpolation weight can be allocated in the form of inverse, or can be weighted and controlled in combination with the path smoothness or direction consistency factor. Taking the distances of four measuring points from the intermediate position point as fifteen meters, twenty meters, ten meters, and five meters as an example, the initial weight can be set as zero point zero six seven, zero point zero five, zero point one, and zero point two, and after normalization, the interpolation proportion is redistributed.

[0049] The error ellipse semi-axis length of the intermediate position point is calculated as the initial interpolation result according to the error ellipse semi-axis length of each measurement point and the interpolation weight in the cubic spline interpolation mode. After obtaining the error ellipse parameters of each measurement point and the corresponding interpolation weight, the cubic spline interpolation algorithm is used to construct a continuous interpolation function about the length of the major axis and the minor axis of the error ellipse, so as to realize the natural transition of the error scale in space. The cubic spline function constructs a polynomial between the measurement points and meets the continuity constraints of position, slope and curvature, which can avoid the sharp breakpoint problem in linear interpolation. Taking the major axis length values of four measurement points as an example, the major axis length of the intermediate position point is calculated to be 0.165 meters and the minor axis length is calculated to be 0.053 meters after constructing the cubic spline function, which is used as the initial interpolation value for subsequent correction.

[0050] The compensation factor is applied to the initial interpolation result, and the error parameters of the intermediate position point are corrected to form the final error parameters used for error synthesis. In order to further improve the response ability of the interpolation result to the path anomaly, the compensation factor mechanism based on trajectory disturbance perception is introduced in this step to adjust the value of the initial interpolation. The compensation factor value is derived from the trajectory anomaly judgment module, and its typical value range is between 0.8 and 3.0. When the path segment where the intermediate position point is located is identified as having abnormal disturbance and the disturbance intensity is high, for example, the compensation factor is 2.2, the value of the major axis interpolation will be adjusted to 0.363 meters, and the value of the minor axis will be adjusted to 0.117 meters. The corrected error parameters are used as the final error space feature data of the intermediate position point, which are input into the subsequent error synthesis module to participate in the error space accumulation analysis between the target well pair.

[0051] The step of completing the spatial quantitative expression of the error range between the target well pair specifically includes: extracting the error parameter matrix of a plurality of path segments related to the target well pair and the error parameter matrix of the intermediate position point, and determining the coordinate reference direction corresponding to each error parameter matrix. In this step, all drilling path segments that are directly or indirectly connected to the target well pair are extracted from the path data that has completed the interpolation correction processing, including the segment from the starting point of the target well to the intermediate position point, the segment from the intermediate position point to the end point of the target well, and other auxiliary paths that can be directly measured. Each path segment corresponds to an error parameter matrix, usually a two-dimensional covariance matrix, which describes the error dispersion characteristics in the main direction and the secondary direction of space respectively. At the same time, the error parameter matrix of the intermediate position point is also input as an independent input for calculation. When each error parameter matrix is extracted, the direction vector or attitude reference direction of the path segment where it is located also needs to be recorded as the basis for subsequent coordinate transformation processing.

[0052] The error parameter matrices are unified to a common coordinate reference system by coordinate transformation, and the unified error parameter matrices are added one by one to form the total error parameter matrix of the target well pair. In this step, the common coordinate reference system is established with the target well pair connection direction as the main axis, and the target well pair connection direction is usually set as the X axis to establish a right-handed system and form a standard reference system in three-dimensional space. Then, each error parameter matrix is mapped from its original directional coordinate system to the common reference system, and the transformation is completed by a coordinate rotation matrix. For example, if there is an angle θ between the original direction of the path segment and the common coordinate system, the error matrix needs to be subjected to a similar transformation operation by the rotation matrix R(θ) to ensure that all error components have additivity under unified space projection. All the converted error parameter matrices are added one by one according to the corresponding element positions to form the total error parameter matrix of the target well pair. This matrix contains the combined effects of all error propagation paths between the target well pair and can fully reflect the distribution trend and uncertainty boundary of the error space.

[0053] The total error parameter matrix is subjected to eigenvalue decomposition to extract the major axis length and direction angle of the error ellipse between the target well pair. After the total error parameter matrix is constructed, a standard matrix eigenvalue decomposition algorithm (Jacobi iteration method, QR decomposition method or singular value decomposition method) is used to calculate the matrix to obtain two eigenvalues and the corresponding eigenvectors. The square root of the eigenvalue is the scale of the error space in the main direction and the secondary direction, i.e., the major axis length and the minor axis length of the error ellipse of the target well pair, with the unit of meter. The eigenvector describes the direction angle of the main direction and the secondary direction of the error space in the three-dimensional space, and the angle with the coordinate reference system is used to express the spatial directivity of the error uncertainty boundary. For example, in a certain calculation, the major axis length is 0.26 meters, the minor axis length is 0.12 meters, and the main direction deviates from the target well pair connection direction by about 15 degrees.

[0054] The eigenvalue decomposition results are used as the spatial quantitative expression results of the error range between the target well pair for well-to-well safety tolerance evaluation, interference avoidance strategy formulation and magnetic ranging parameter optimization. In this step, the major axis length, minor axis length and direction angle extracted in the foregoing are used as the final error expression output, which can be used for error visualization, engineering risk evaluation and dynamic tolerance control between the target well pair.

[0055] The above algorithms or formulas are dimensionless numerical calculations, and the results closest to the real situation are obtained by collecting a large amount of data for software simulation. The preset parameters are set by a person skilled in the art according to the actual situation.

[0056] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0057] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software manner depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0058] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0059] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for calculating the error of magnetic distance measurement of a well pattern, characterized in that, The method comprises the following steps: Collecting magnetic ranging data of each drilling path segment in the well pattern, identifying multiple independent error sources, constructing error parameter matrix of each drilling path segment, and extracting principal axis parameters reflecting error space distribution through eigenvalue decomposition method; Analyzing whether there is continuity anomaly in the intermediate drilling path connecting the target well pair, judging whether there is trajectory anomaly by comparing the path attitude change amplitude with the preset path smoothness threshold, and generating a compensation factor for adjusting error calculation according to the judgment result; Determining the intermediate position point for connecting the target well pair, selecting a cross section perpendicular to the path extension direction in the spatial configuration containing multiple drilling paths, and calculating the position point closest to each path in geometry on the cross section, i.e. the intermediate position point; According to the relative distance from the target well pair to the intermediate position point and the error parameters of multiple path segments, the error parameters of the intermediate position point are calculated through an interpolation algorithm, and the compensation factor is applied to the interpolation process to correct the influence of path anomaly on error estimation; The error parameters of the intermediate position point and the multiple path segments related to the target well pair are uniformly converted to the same coordinate reference system, the converted error parameter matrix is superimposed, and the obtained matrix is subjected to eigenvalue decomposition to extract the principal axis length and direction angle of the magnetic ranging error between the target well pair, and the spatial quantitative expression of the error range between the target well pair is completed.

2. The method of claim 1, wherein, The step of constructing the error parameter matrix of each drilling path segment comprises: Collecting magnetic induction intensity, well depth information and installation attitude data of the magnetic ranging tool related to the drilling path; Respectively calculating the error quantization parameters of the eccentric installation error, sensor measurement deviation, relative well depth measurement deviation and spatial attitude change in the drilling path; Establishing a weight function for different error items according to the error quantization parameters, and expressing the influence of each error item on the ranging result in the form of covariance; Combining the covariances of multiple error items to form an error parameter matrix for describing the spatial uncertainty of the drilling path segment.

3. The method of claim 1, wherein, The step of extracting the principal axis parameters reflecting the error space distribution comprises: Matrix decomposition is performed on the error parameter matrix, and the matrix is converted into a decomposition form containing two eigenvectors and two eigenvalues; The square root of the eigenvalue is obtained to determine the lengths of the long axis and the short axis of the error space; The directions corresponding to the eigenvectors are taken as the two principal directions of the error space, which are used to describe the spatial orientation of the error ellipse; The principal axis length and the principal direction are taken together as the error space distribution parameters of the drilling path segment.

4. The method of claim 3, wherein, When performing matrix decomposition on the error parameter matrix, one of the following algorithms is used: Jacobi iteration method, eigenvalue extraction method based on QR decomposition, or singular value decomposition method, to obtain two eigenvectors and corresponding eigenvalues, which are used to construct the error ellipse parameters between the target well pair.

5. The method of claim 4, wherein, The generation logic of the compensation factor is as follows: Extracting the inclination angle change and the azimuth angle change of multiple continuous measurement points on the intermediate drilling path; For each pair of adjacent measurement points, the attitude change amplitude is calculated, which is a spatial offset measure composed of the change in hole inclination and the change in hole azimuth of the two consecutive measurement points, and can be calculated by the angle between the corresponding attitude vectors; The attitude change amplitude corresponding to each pair of adjacent measurement points is compared with the path smoothness threshold value, and if the attitude change amplitude exceeds the threshold value, the path segment between the two measurement points is marked as having a trajectory anomaly; In all path segments marked as having a trajectory anomaly, the total length of the abnormal segment is calculated, and a ratio calculation is performed with the entire intermediate path length as the length ratio index of the path; In all abnormal path segments, the average value of the attitude change amplitude is calculated as the attitude change index of the path; The length ratio index and the attitude change index are normalized to the range of zero to one, respectively, and then subjected to nonlinear function processing, and the product of the two processing results is the disturbance influence value; The disturbance influence value is limited in a predetermined range according to the limitation rule as a compensation factor in the interpolation correction process.

6. The method of claim 5, wherein, The generation of the compensation factor includes the following nonlinear function processing steps: The length ratio index is input into the length influence function, and the length influence function maps the input ratio to different levels of output values according to the preset segmentation rule; The attitude change index is input into the angle change function, and the angle change function processes the input value according to the preset threshold interval, and the output remains at a basic level in the interval below the lowest threshold value. When the input value is between the lowest threshold value and the highest threshold value, the function output monotonically increases with the input value. The output increases according to the enhancement rule in the interval above the highest threshold value.

7. The method of claim 6, wherein, The steps of determining the intermediate position point for connecting the target well pair include: Selecting multiple spatial measurement points in the intermediate drilling path connecting the target well pair, and extracting the three-dimensional coordinates and the wellbore extension direction corresponding to each measurement point; Selecting a reference measurement point in the connecting path, and taking the wellbore extension direction of the reference measurement point as a normal vector to construct a spatial section perpendicular to the direction, projecting the adjacent path segments into the spatial section to form a path geometric intersection area; Searching for the intersection area point closest in spatial position to the multiple path segments in the spatial section as the intermediate position point of the error propagation path between the target well pair.

8. The method of claim 7, wherein, In the interpolation process, multiple measurement points located on both sides of the intermediate position point are selected in the path connecting the target well pair, and the error ellipse semi-axis length parameters of each measurement point are extracted; The spatial distance between the intermediate position point and each measurement point is calculated, and the interpolation weight is assigned according to the distance; The error ellipse semi-axis length of the intermediate position point is calculated as the initial interpolation result by using cubic spline interpolation method according to the error ellipse semi-axis length of each measurement point and the interpolation weight; The compensation factor is applied to the initial interpolation result to correct the error parameters of the intermediate position point to form the final error parameters for error synthesis.

9. The method of claim 8, wherein, The steps of completing the spatial quantitative expression of the error range between the target well pair include: Extracting the error parameter matrix of the multiple path segments related to the target well pair and the error parameter matrix of the intermediate position point, and determining the coordinate reference direction corresponding to each error parameter matrix; The error parameter matrices are unified to a common coordinate reference system by coordinate transformation, and the unified error parameter matrices are added one by one to form a total error parameter matrix of the target well pair; The total error parameter matrix is subjected to eigenvalue decomposition to extract the length and direction angle of the major axis of the error ellipse between the target well pair, and the spatial quantitative expression of the error range of the target well pair is completed.

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