A method for calculating well pattern magnetic ranging error

Abstract

The application discloses a kind of well pattern magnetic ranging error calculation method, specifically relates to well pattern configuration magnetic ranging technical field of multiple well field, including the following steps: collecting the magnetic ranging data of drilling path section, constructs error parameter matrix and extracts principal axis parameter;Identify intermediate path continuity anomaly and generate compensation factor;Determine the intermediate position point of connecting target well pair and estimate its error parameter;Error estimation is corrected by interpolation algorithm and combined with compensation factor;After all error parameters are unified to the same coordinate system, superposition is carried out, eigenvalue decomposition is carried out, the error principal axis length and direction angle between target well pair are obtained, and the spatial quantitative expression of error range is completed;The application realizes the accurate spatial expression of multiple path section magnetic ranging error by error parameter matrix modeling and principal axis extraction;Introduce path anomaly perception and compensation mechanism, improve the robustness of intermediate position point error estimation;Based on coordinate unification and matrix synthesis, the quantitative calculation of error range between target well pair is completed.

Description

Technical Field

[0001] This invention relates to the field of multi-wellfield well network configuration ranging technology, and more specifically, to a method for calculating well network magnetic ranging error. Background Technology

[0002] In the development of oil and gas cluster well networks, with the rapid increase in the number of horizontal and branch wells, the spatial layout between wells has become increasingly compact, and the well network configuration has become more complex. Precisely controlling the ranging error between different wells and ensuring safe trajectory intervals has become a key technical challenge for ensuring drilling operation safety and guidance accuracy. Magnetic ranging, as a non-contact well positioning method suitable for highly disturbed underground environments and requiring no electrical connection, has become an important technical solution for well network ranging.

[0003] In estimating magnetic ranging errors, a piecewise error modeling approach is commonly used in engineering. This approach quantifies sensor measurement errors, well depth measurement errors, wellbore attitude disturbances, and tool eccentricity installation errors in each segment of the drilling path, and then sums these errors in the form of a covariance matrix. However, existing methods typically assume that the error distribution between measurement points is spatially stable and continuously changing, lacking a dynamic feedback mechanism for the actual geometric state of the path. This is especially true when abnormal situations such as slight doglegs, local deformations, or abrupt changes in the wellbore occur in the middle of the path, where standard interpolation paths are still used for error estimation. This static interpolation modeling method exhibits severe bias under nonlinear path morphology, causing the error estimate at intermediate points to deviate from the actual spatial distribution of errors.

[0004] Especially in target well pairs (such as AC wells) that form indirect ranging relationships through intermediate well connections, the error estimation deviation of the intermediate path segment directly affects the overall error synthesis result. When there are unidentified continuity anomalies in the intermediate well segment, the underestimation of interpolation error will lead to an overly idealized error ellipse in the final synthesis, failing to truly reflect the expansion range of spatial uncertainty in magnetic ranging. In scenarios where the well spacing is close to the design tolerance limit, this will cause magnetic signal misjudgment, trajectory interference and misleading, and even serious engineering consequences such as wellbore collision and directional control failure, which is particularly high-risk in complex shale well clusters and high-density parallel well deployments. Therefore, this invention proposes a method for calculating the magnetic ranging error of well networks to solve the above problems. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for calculating the magnetic ranging error of a well network includes the following steps:

[0007] Magnetic ranging data of each drilling path segment in the well network are collected, multiple independent error sources are identified, an error parameter matrix of each drilling path segment is constructed, and principal axis parameters reflecting the spatial distribution of errors are extracted by eigenvalue decomposition.

[0008] The system analyzes whether there are continuity anomalies in the intermediate drilling paths connecting the target well pairs. It judges whether there are trajectory anomalies by comparing the magnitude of path attitude changes with the preset path smoothness threshold, and generates compensation factors for adjusting error calculations based on the judgment results.

[0009] To determine the intermediate point used to connect the target well pairs, in a spatial configuration containing multiple drilling paths, select a section perpendicular to the path extension direction and calculate the intermediate point on that section that has the closest geometric relationship with each path.

[0010] Based on the relative distance from the target well to the intermediate location point and the error parameters of multiple path segments, the error parameters of the intermediate location point are calculated by an interpolation algorithm, and a compensation factor is applied to the interpolation process to correct the impact of path anomalies on error estimation.

[0011] The error parameters of multiple path segments and intermediate points related to the target well pair are uniformly transformed to the same coordinate reference system, so that various error sources can be superimposed. The transformed error parameter matrices are superimposed, and the resulting matrix is ​​decomposed into eigenvalues ​​to extract the principal axis length and direction angle of the magnetic ranging error between the target well pairs, thus completing the spatial quantitative expression of the error range between the target well pairs.

[0012] In a preferred embodiment, the step of constructing the error parameter matrix for each drilling path segment includes:

[0013] Collect magnetic induction intensity, well depth information, and installation attitude data of magnetic ranging tools related to the drilling path;

[0014] Calculate the error quantification parameters caused by eccentric installation error, sensor measurement deviation, relative well depth measurement deviation, and spatial attitude change in the drilling path, respectively;

[0015] Based on the error quantification parameters, weight functions for different error terms are established, and the influence of each error term on the ranging result is expressed in the form of covariance.

[0016] The covariances of multiple error terms are combined to form an error parameter matrix that describes the spatial uncertainty of the drilling path segment.

[0017] In a preferred embodiment, the step of extracting the principal axis parameters reflecting the spatial distribution of error includes:

[0018] Perform matrix decomposition on the error parameter matrix to transform it into a decomposition form containing two eigenvectors and two eigenvalues.

[0019] The square root of the eigenvalues ​​is used to obtain the scale values ​​of the error in the two principal directions, and these are used to determine the lengths of the major and minor axes of the error space.

[0020] The directions corresponding to the feature vectors are taken as the two main directions of the error space, which are used to describe the spatial orientation of the error ellipse.

[0021] The main shaft length and the main direction are used together as the error spatial distribution parameters of the drilling path segment.

[0022] 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 pairs.

[0023] In a preferred embodiment, the logic for generating the compensation factor refers to:

[0024] Extract the changes in well inclination angle and well azimuth angle at multiple continuous measurement points along the intermediate drilling path;

[0025] For each pair of adjacent measurement points, calculate its attitude change amplitude. The attitude change amplitude is the spatial offset measure formed by the change in well inclination angle and the change in well azimuth angle of two consecutive measurement points, which can be calculated by the angle between the corresponding attitude vectors.

[0026] The attitude change amplitude corresponding to each pair of adjacent measurement points is compared with the path smoothness threshold. If the attitude change amplitude exceeds the threshold, the path segment between the two measurement points is marked as having a trajectory anomaly.

[0027] Among all path segments marked as trajectory anomalies, calculate the total length of the anomaly segments and use the ratio of this ratio to the length of the entire intermediate path as an indicator of the path's length proportion.

[0028] In all abnormal path segments, the average value of their attitude change amplitude is calculated and used as the attitude change index for that path.

[0029] The length ratio index and attitude change index are normalized to the range between zero and one, and then nonlinear function processing is performed on them respectively. The two processing results are multiplied to obtain the disturbance influence value.

[0030] The disturbance impact value is limited to a preset range according to the restriction rules and used as a compensation factor in the interpolation correction process.

[0031] In a preferred embodiment, the generation of the compensation factor includes the following nonlinear function processing steps:

[0032] The length proportion index is input into the length influence function, which maps the input proportion to the output value of different levels according to the preset segmentation rules. There is a definite correspondence between the input interval and the output amplitude of each level, so that the proportion of anomalies in the path can be reflected in the output results in a segmented manner.

[0033] The attitude change index is input into the angle change function. The angle change function processes the input value according to a preset threshold range. In the range below the minimum threshold, the output remains at the basic level. When the input value is between the minimum and maximum thresholds, the function output increases monotonically with the input value. The specific response amplitude is controlled by a preset continuous change rule. In the range above the maximum threshold, the output increases according to the enhancement rule to reflect the hierarchical change characteristics of the attitude change on the error assessment.

[0034] In a preferred embodiment, the step of determining the intermediate location point for connecting the target well pair includes:

[0035] Multiple spatial measurement points are selected in the intermediate drilling path connecting the target well pairs, and the three-dimensional coordinates and wellbore extension direction corresponding to each measurement point are extracted.

[0036] Select a reference measuring point in the connection path, and construct a spatial section perpendicular to the wellbore extension direction of the reference measuring point as the normal vector. Project adjacent path segments into the spatial section to form the path geometric intersection area.

[0037] Within the spatial cross section, search for the intersection point that is spatially closest to multiple path segments, and use it as the intermediate point of the error propagation path between the target well pairs.

[0038] In a preferred embodiment, during 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 parameter of each measurement point is extracted.

[0039] Calculate the spatial distance between the intermediate location point and each measurement point, and assign interpolation weights based on the distance;

[0040] The cubic spline interpolation method is used to calculate the length of the error ellipse semi-axis at the intermediate position point based on the length of the error ellipse semi-axis at each measurement point and the interpolation weight, which is used as the initial interpolation result.

[0041] The compensation factor is applied to the initial interpolation result to correct the error parameters at intermediate points, forming the final error parameters used for error synthesis.

[0042] In a preferred embodiment, the steps for completing the spatial quantitative expression of the error range between target well pairs specifically include:

[0043] Extract the error parameter matrices of multiple path segments related to the target well pair and the error parameter matrices of intermediate position points, and determine the coordinate reference direction corresponding to each error parameter matrix;

[0044] Each error parameter matrix is ​​unified to a common coordinate reference system through coordinate transformation. The unified error parameter matrices are then superimposed item by item to form the total error parameter matrix of the target well pair.

[0045] Eigenvalue decomposition is performed on the total error parameter matrix to extract the principal axis length and direction angle of the error ellipse between the target well pairs, thus completing the spatial quantitative expression of the error range of the target well pairs.

[0046] The technical effects and advantages of this invention are as follows:

[0047] This invention acquires magnetic ranging data from each drilling path segment in a well network, identifies multiple independent error sources, and constructs an error parameter matrix for each drilling path segment, achieving a precise mapping from physically acquired data to a spatial uncertainty representation. Unlike traditional methods that estimate errors by mean or assume single-axis perturbation, this invention uses covariance modeling to comprehensively reflect the statistical distribution characteristics of errors in three-dimensional space. Furthermore, by extracting principal axis parameters reflecting the spatial distribution of errors through eigenvalue decomposition, the directions and scales of the major and minor axes of the error space are quantitatively expressed. This not only enhances the engineering interpretability of the error model but also provides a clear directional basis for subsequent error propagation and synthesis. This approach improves the spatial adaptability of error representation, making it particularly suitable for scenarios involving multi-segment drilling path fusion and regional well network error synthesis, thus enhancing the systematic nature and accuracy of error modeling.

[0048] This invention establishes a trajectory anomaly identification method based on comparing the amplitude of path attitude changes with a path smoothness threshold when analyzing intermediate drilling paths connecting target well pairs. By identifying attitude changes between consecutive measuring points in the path, it determines whether there are non-smooth anomaly segments and generates compensation factors to correct error estimates during the interpolation process. Unlike traditional interpolation methods that rely solely on path position relationships, this invention introduces dynamic perception and disturbance compensation logic for path continuity anomalies into the interpolation algorithm. This effectively avoids underestimation of errors caused by local collapse, small doglegs, or nonlinear disturbances. Especially when the spatial distance between target well pairs is close to the safety tolerance threshold, the introduction of the compensation mechanism significantly improves the reliability and safety margin of the estimation results. This proactive compensation method targeting the path structure enhances the engineering adaptability of the error estimation process under complex formations and non-ideal trajectory conditions.

[0049] This invention unifies the error parameters of multiple path segments and intermediate points related to the target well pair into a single coordinate reference system, enabling direct synthesis of error matrices from different sources. By mapping each error component to a unified spatial reference direction through a coordinate transformation matrix, the transformed error parameter matrices are then superimposed item by item to form a total error matrix representing the error propagation result of the target well pair. Based on this, eigenvalue decomposition is performed on this matrix to obtain the principal axis length and direction angle of the spatial distribution of magnetic ranging errors between the target well pairs, thus achieving a quantitative expression of the error space. This method abandons empirical amplification factors or piecewise weighting mechanisms in error estimation, establishing a spatial evaluation system based on the physical structure of the path and centered on the principal error axis. The final output principal error axis parameters can be used in key aspects such as inter-well interference risk control, trajectory relative positioning judgment, and drilling guidance decision-making, greatly improving the accuracy, reliability, and controllability of well network measurement systems in practical engineering. Attached Figure Description

[0050] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0051] Figure 1 This is a schematic diagram of a method for calculating the magnetic ranging error of a well network according to the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0053] Reference Figure 1 The following examples were obtained:

[0054] Example 1: A method for calculating magnetic ranging error in a well network, comprising the following steps: collecting magnetic ranging data of each drilling path segment in the well network, identifying multiple independent error sources, constructing an error parameter matrix for each drilling path segment, and extracting the principal axis parameters reflecting the spatial distribution of errors 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; representing the errors in a parameterized matrix form, and using eigenvalue decomposition to extract the major and minor axis information of the error ellipse, thereby establishing a spatial error expression for each path segment.

[0055] The analysis examines whether there are continuity anomalies in the intermediate drilling paths connecting the target well pairs. By comparing the magnitude of path attitude changes with a preset path smoothness threshold, it determines whether there are trajectory anomalies and generates compensation factors for adjusting error calculations based on the judgment results. This step is used to identify whether there are abrupt changes, disturbances, or other discontinuities in the spatial distribution of drilling paths connecting the target well pairs in the well network. By quantifying the combined changes in well inclination angle and well azimuth angle and comparing them with a predetermined smoothness standard, potential abnormal sections are identified. Based on these results, compensation factors that can correct interpolation accuracy are constructed to improve the stability and accuracy of subsequent error interpolation calculations.

[0056] The intermediate point used to connect the target well pairs is determined. In a spatial configuration containing multiple drilling paths, a cross section perpendicular to the path extension direction is selected, and the point on this cross section that is most closely related to the geometry of each path is calculated, which is the intermediate point. This step is used to identify the key nodes of error propagation in space. By analyzing the overall configuration of the paths between the target well pairs in space, a reference cross section perpendicular to the main path direction is constructed, and the intersection point closest to the geometric center of the path is selected from the cross section as the intermediate calculation reference point for error interpolation and synthesis, thereby providing a unified spatial reference for subsequent steps.

[0057] Based on the relative distance between the target well pair and the intermediate location point, as well as the error parameters of multiple path segments, the error parameters of the intermediate location point are calculated using an interpolation algorithm. A compensation factor is then applied to the interpolation process to correct the impact of path anomalies on error estimation. This step considers the distance distribution characteristics between the target well pair and the intermediate location point, combines the aforementioned error parameters of each path segment, and uses an interpolation algorithm to calculate the estimated value of the intermediate location point in the spatial error field. The compensation factor generated in the previous step is introduced into the interpolation calculation to adjust the error distortion caused by path anomalies, thereby achieving an error estimation that is closer to reality.

[0058] The error parameters of multiple path segments and intermediate points related to the target well pair are uniformly transformed to the same coordinate reference system, enabling the superposition of various error sources. The transformed error parameter matrices are then superimposed, and eigenvalue decomposition is performed on the resulting matrix to extract the principal axis length and direction angle of the magnetic ranging error between the target well pairs, thus completing a spatial quantitative expression of the error range between the target well pairs. This step is used to unify the error parameter matrices under different drilling path coordinate systems to the common coordinate system of the well network, ensuring the compatibility and synthesizability of errors from various sources. After achieving a consistent expression in matrix form through coordinate transformation, the matrices are summed to form the total covariance matrix of the target well pair. The principal axis direction and scale of the error ellipse between the well pairs are then extracted through eigenvalue decomposition to achieve a quantitative description of the error range in space, providing a basis for well network planning and collision risk control.

[0059] The steps for constructing the error parameter matrix for each drilling path segment include: In actual engineering, the sources of error for each drilling path segment in the well network are closely related to the magnetic field environment, structural attitude, and instrument status. Therefore, this implementation method first continuously collects magnetic induction intensity data during drilling, obtaining a series of three-dimensional magnetic field vector data through formation magnetic environment measurement and measurement while drilling modules. Simultaneously, well depth information is collected, including the actual well depth, vertical depth, and measurement depth along the path, to accurately depict the spatial distribution. Furthermore, the installation attitude data of the magnetic ranging tool needs to be collected, describing the instrument's three-dimensional attitude through tool face direction, inclination angle, and azimuth angle. This step provides a unified and continuous input basis for subsequent error term calculations, enabling all errors to be quantified within the same data framework.

[0060] The eccentric installation error, sensor measurement deviation, relative well depth measurement deviation, and error quantification parameters caused by spatial attitude changes in the drilling path are calculated separately. After obtaining complete input data, each type of error is quantified item by item. This embodiment discloses four types of errors through specific calculation methods. Eccentric installation error is determined by analyzing the external structural offset of the magnetic ranging tool and the radial offset of the wellbore center. For example, when the tool is offset by 5 mm relative to the wellbore center, the error contribution values ​​in the radial and axial directions can be obtained by simulating a magnetic field disturbance model. Sensor measurement deviation is quantified by comparing the magnetic field strength of continuous measurement points with the theoretical formation magnetic field, combined with the stability of repeated measurements. In actual situations, if the sensor has zero-point drift, measurement noise parameters can be constructed based on the offset in each measurement cycle. Relative well depth measurement deviation is determined by comparing the response differences of the well depth measuring tool under different loading conditions. For example, when there is a 0.2-meter error in the well depth reading between measurement points, this difference can be used as the quantified error value in the well depth direction. The error quantification parameters caused by spatial attitude changes are calculated based on the changes in tool face direction and wellbore attitude. For example, when the wellbore inclination angle and well azimuth angle change by three degrees and five degrees respectively between two measuring points, the tool attitude error components are obtained through the attitude perturbation model. These error quantification parameters are all obtained independently, providing a basis for establishing the subsequent weighting function.

[0061] Weighting functions for different error terms are established based on error quantification parameters, and the influence of each error term on the ranging result is expressed in the form of covariance. In this embodiment, each type of error quantification parameter is input into the weighting function to reflect its relative importance in the formation of magnetic ranging error. In this embodiment, the "weighting function" can adopt a piecewise response model based on error amplitude to achieve graded response adjustment of the error parameters. It should be noted that the construction method of the weighting function is not limited to a piecewise response model. Those skilled in the art can also choose existing mathematical modeling methods such as linear functions, exponential functions, neural network fitting functions, and fuzzy logic control models to construct the weighting function according to engineering needs, computing resources, error sensitivity control objectives, etc. The above construction methods are all conventional methods in this field, supported by clear published literature and commercial software, and their principles are well known to those skilled in the art. For example, when the weighting function adopts a piecewise response model based on error amplitude, for instance, when the value of the eccentric installation error is within the reference range, its weight maintains the reference value; when it exceeds a set threshold, its weight increases according to an enhancement rule, so that the proportion of the eccentric installation error in the overall error is reasonably expressed. The same logic applies to error terms caused by sensor measurement deviation, relative well depth measurement deviation, and spatial attitude changes. Based on the results processed by the weighting function, the influence of each error term is converted into a covariance form. The diagonal elements of the covariance matrix correspond to the error variance along each coordinate axis, while the off-diagonal elements represent the correlation of errors in different directions. For example, if at a certain measuring point the sensor measurement deviation produces a variance of 0.01 square units in the radial direction and is positively correlated with the axial direction, then the corresponding off-diagonal position in the covariance matrix reflects this relationship.

[0062] In this invention, when constructing the error parameter matrix for each drilling path segment, the quantified error parameters caused by eccentric installation error, sensor measurement deviation, relative well depth measurement deviation, and spatial attitude change in the drilling path are calculated respectively. The calculation of these error terms relies on several mature mathematical modeling methods or simulation techniques, such as simulated magnetic field disturbance models and attitude disturbance models. It should be noted that simulated magnetic field disturbance models and attitude disturbance models are existing technologies commonly used by those skilled in the art in related engineering projects. Their principles and construction methods are supported by numerous published documents and engineering solutions in multiple sub-fields such as magnetic measurement accuracy modeling, drilling and control system error correction, and magnetic interference assessment. Therefore, the application of these models in this invention does not constitute a reinvention of existing technologies, but rather a reasonable integration and structural application within a specific technical process.

[0063] A simulated magnetic field disturbance model reconstructs the magnetic field disturbance distribution that a magnetic ranging tool may experience by establishing the magnetic field interaction relationship between the drilling tool assembly and its surrounding formation and wellbore structure in a simulated environment. This model is typically based on finite element analysis, electromagnetic simulation, or numerical field methods, combined with tool material, cable layout, tool eccentricity, and geomagnetic background conditions to calculate the degree of magnetic vector distortion in the tool's location. By comparing the difference between the disturbed field and the ideal magnetic field, the impact of eccentric installation errors or structural interference can be quantitatively assessed. For example, with a 5 mm eccentricity, the model can output the radial and axial magnetic flux density variations, which can be used as input factors to construct the error covariance.

[0064] The attitude disturbance model refers to the model that considers the impact of drill string attitude changes (such as well inclination angle, well azimuth angle, tool face direction, etc.) on the response capability of magnetic ranging tools during drilling, and simulates and estimates the error propagation path caused by these changes in space through modeling methods. This model is typically based on a three-dimensional attitude matrix, direction cosine matrix, or quaternion operations, and combines path curvature, turning radius, and attitude change velocity to derive the sensitivity changes and signal drift range of the magnetic measuring device under different attitude combinations. This model can output the component intensity of attitude disturbances in each principal direction, used to express the contribution of attitude changes to ranging uncertainty. The simulated magnetic field disturbance model and attitude disturbance model described in this invention do not constitute the novelty of the technical solution itself, but rather are conventional engineering modeling methods used to calculate error quantification parameters. Their principles and structure are technical models that can be directly constructed and applied by those skilled in the art based on general electromagnetic theory and the principles of measurement and control while drilling, possessing a clear existing technical foundation, and will not affect the core technical protection scope of this invention regarding the error interpolation correction and synthesis method.

[0065] The steps for extracting the principal axis parameters reflecting the spatial distribution of errors include: In this invention, each drilling path segment has already had an error parameter matrix reflecting spatial uncertainty constructed through previous steps. This matrix is ​​typically a two-dimensional symmetric positive definite matrix with good numerical stability. To obtain the error magnitude of this matrix in the principal direction, matrix decomposition is required. To meet the accuracy, efficiency, and equipment capabilities of different computing scenarios, this invention provides three common existing technical paths for engineering deployment selection.

[0066] The first is the Jacobi iterative method, which is suitable for fine-scale ground simulation scenarios with small matrix dimensions and high computational accuracy requirements. It can obtain two sets of eigenvectors and their corresponding eigenvalues ​​by successively diagonalizing and rotating the largest off-diagonal element in the matrix.

[0067] Second, the eigenvalue extraction method based on QR decomposition is suitable for high-frequency continuous processing scenarios in field data streams. It has the advantages of fast iteration and matrix stability. By decomposing the error parameter matrix into the product of an orthogonal matrix and an upper triangular matrix, it iteratively approximates the eigenvalue diagonal matrix and then derives the eigenvector group.

[0068] Thirdly, there is the singular value decomposition (SVD) method, which is suitable for full-path modeling scenarios that need to consider the coupling effect of path attitude perturbations. This method decomposes the error parameter matrix into a product of a left singular vector matrix, a singular value diagonal matrix, and a right singular vector matrix, where singular values ​​are equivalent to eigenvalues, and singular vectors represent the principal error directions. All three methods described above are conventional linear algebraic computational techniques in this field, possessing high engineering applicability. Technical personnel can choose one of them for matrix decomposition based on scenario requirements.

[0069] The square root of the eigenvalues ​​yields the scale values ​​of the error in the two principal directions, which are then used to determine the lengths of the major and minor axes of the error space. After extracting the eigenvalues ​​of the error parameter matrix, two real eigenvalues ​​are obtained, representing the variance of the error in the principal directions. Since the variance unit is the square of the error, to reconstruct the spatial scale information, the square roots of the two eigenvalues ​​need to be calculated to obtain the actual half-axis lengths of the error space distribution in the principal directions of that path segment. In this invention, these are referred to as the lengths of the major and minor axes of the error space, where the major axis corresponds to the larger square root of the eigenvalue, and the minor axis corresponds to the smaller square root of the eigenvalue. For example, if the eigenvalues ​​of the error parameter matrix for a certain path segment are 0.0144 and 0.0025, then its major and minor axis lengths are 0.12 meters and 0.05 meters, respectively, directly characterizing the actual uncertainty range of the ranging error in the two directions.

[0070] The directions corresponding to the eigenvectors are used as the two principal directions of the error space to describe the spatial orientation of the error ellipse. The eigenvalues ​​describe the magnitude of the error along the principal axis, while the corresponding eigenvectors describe the orientation of the error principal axis in space. In this embodiment, the two orthogonal eigenvectors extracted by the above decomposition method constitute the two principal directions in the error space, which are represented as direction cosines or vector coordinates in the three-dimensional coordinate system. In this invention, these two principal directions are used as the spatial orientation of the error ellipse and directly participate in the subsequent coordinate system integration and error synthesis process. If the principal directions of the error ellipse formed on the path segment point to (0.8, 0.6) and (−0.6, 0.8) respectively, it indicates the distribution trend of the error in the forward and lateral directions of the path. This directional information is crucial for spatial orientation measurement and control.

[0071] This invention uses the principal axis length and principal direction as the error spatial distribution parameters for a drilling path segment. It defines the "major and minor axis lengths of the error space" and the "two principal directions of the error space" together as the error spatial distribution parameters for that path segment, and outputs them as a unified structure to subsequent interpolation calculations, intermediate position point error estimation, and final error synthesis modules. Logically, these parameters constitute a geometrically complete description of the error ellipse, including both amplitude and spatial orientation information, possessing high data adaptability and mathematical superpositionability. When spatially synthesizing multiple path segments, the error spatial distribution parameters of each segment can directly participate in covariance superposition and error ellipse construction under a unified coordinate system, ensuring that the error analysis between target well pairs has a rigorous physical basis and high-precision expression capability.

[0072] The logic for generating the compensation factor refers to extracting the changes in inclination angle and azimuth angle from multiple consecutive measurement points along the intermediate drilling path, and calculating the attitude change amplitude accordingly. Before generating the compensation factor, it is necessary to first obtain the attitude information recorded by multiple measurement points along the intermediate drilling path connecting the target well pair, including the inclination angle and azimuth angle of each point. By combining the changes in inclination angle and azimuth angle between each pair of adjacent measurement points, an attitude change vector for the path is formed. To quantify the spatial offset of this change vector, the angle between spatial attitude vectors is used as a measure of the "attitude change amplitude". For example, if two adjacent measurement points correspond to attitude vectors v1 and v2 respectively, and the angle between them is calculated to be 12 degrees, then the attitude change amplitude of this path segment is considered to be 12 degrees. This process is repeated to fully traverse the entire intermediate path to obtain the attitude change amplitude sequence of all path segments.

[0073] The attitude change amplitude corresponding to each pair of adjacent measurement points is compared with a path smoothness threshold. If the attitude change amplitude exceeds the threshold, the path segment between the two measurement points is marked as having a trajectory anomaly. After obtaining the path attitude change amplitude sequence, each item is compared with a preset path smoothness threshold. This path smoothness threshold is preset based on empirical engineering data or simulation results to identify whether there are unnatural continuous disturbances in the path. If the attitude change amplitude of a path segment exceeds the threshold, the path segment is marked as an abnormal path segment. For example, when the threshold is set to 10 degrees, and the attitude change amplitude of a certain segment is 12 degrees, then the segment is identified as having a trajectory anomaly. The purpose of this step is to screen out those abnormal disturbance segments that are most sensitive to attitude changes from all path segments, providing a basic dataset for subsequent quantification of the anomaly's impact.

[0074] In all path segments marked as trajectory anomalies, the total length of the anomaly segments is calculated and its ratio to the length of the entire intermediate path is calculated as the length proportion index of that path. Simultaneously, the average attitude change amplitude of all anomaly path segments is calculated as the attitude change index of that path. After marking the anomaly path segments, the total length of all marked anomaly path segments is calculated cumulatively based on the actual length information of each path segment in the drilling measurement data. For example, if the total length of the anomaly segment is 26 meters, and the total length of the entire intermediate path is 130 meters, then the length proportion index of that path is 0.2. At the same time, the attitude change amplitude corresponding to all anomaly path segments is averaged to obtain an attitude change index that comprehensively reflects the severity of the path anomaly. For example, if the attitude change amplitudes of five anomaly 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 quantify the anomaly impact from two directions: spatial location distribution (through the length proportion index) and change amplitude dimension (through the attitude change index), providing basic variables for the generation of the final disturbance impact value.

[0075] The length proportion index and attitude change index are normalized to a range between zero and one, and then subjected to nonlinear function processing. The two processing results are multiplied to obtain the disturbance impact value. The disturbance impact value is then constrained within a preset range according to limiting rules, serving as a compensation factor in the interpolation correction process. To ensure a uniform numerical scale in subsequent processing, the length proportion index and attitude change index are normalized to a range between zero and one, for example, setting the maximum expected value to one and the minimum tolerance value to zero, while maintaining the linear mapping characteristics of the input distribution. Subsequently, nonlinear function processing is applied to the two index values ​​to enhance the expressive ability of the anomaly severity to the disturbance response. The multiplication of the two processed function results forms a disturbance impact value, which reflects the comprehensive impact intensity of the path anomaly in both spatial and amplitude dimensions. To prevent the disturbance impact value from exceeding reasonable engineering ranges, further limiting rules are applied to confine the value to a preset upper and lower limit range, for example, between 0.8 and 1.5. Ultimately, this disturbance impact value serves as a compensation factor in the interpolation correction process, participating in the error estimation correction of intermediate position points to ensure that the error interpolation process has the ability to adaptively adjust to trajectory anomalies.

[0076] The generation of the compensation factor involves the following nonlinear function processing steps: the length proportion index is input into the length influence function, which maps the input proportion to output values ​​at different levels according to a preset segmentation rule. After obtaining the ratio of the total length of the trajectory anomaly segment in the intermediate path to the length of the entire path, the length proportion index is formed. To improve the control resolution of this index on the compensation factor result, this invention sets a length influence function with progressive characteristics to segment the input proportion value. The segmentation rule of the length influence function is determined jointly by empirical values ​​and simulation data, dividing the entire input proportion interval between zero and one into several response intervals, each corresponding to a fixed or progressive output amplitude. For example, when the length proportion index is less than 10%, the output value is set to one; when the length proportion index is between 10% and 30%, the output value linearly increases to 1.5; when the length proportion index is greater than 30%, the output value increases exponentially until it does not exceed 2.5. The above settings allow the spatial distribution characteristics of trajectory anomalies in the path to exhibit a segmented, jump-like increase in the function output, reflecting the intervention intensity of different levels of anomalies on error estimation.

[0077] The attitude change index is input into an angle change function, which processes the input value according to a preset threshold range. After calculating and normalizing the average attitude change amplitude of the abnormal segment to a range between zero and one, the attitude change index is formed. To accurately express the impact of the strength of attitude disturbances in the path on error space estimation, this invention introduces an angle change function to perform nonlinear processing on this index. The angle change function is constructed based on two preset threshold ranges, namely the segmented region formed by the lowest and highest thresholds. In the range where the input value is below the lowest threshold, the function output value is set to a baseline level, for example, one, indicating that the attitude disturbance has not yet reached the intervention standard. When the input value is between the lowest and highest thresholds, the function output increases monotonically with the input value, and the response amplitude is controlled by a continuous change rule, such as using a smoothly rising cubic function or logarithmic curve, so that the function output increases from one to one hundred and fifty in this range. When the input value exceeds the highest threshold, the function output increases significantly according to an enhancement rule, for example, the output value increases by 20% for every 5% increase, with a maximum not exceeding the set value of three point zero, thereby rapidly amplifying the impact of severe attitude disturbances.

[0078] The output of the length influence function is multiplied by the output of the angle change function to form a comprehensive disturbance influence value. This invention does not simply linearly weight the length proportion index and attitude change index; instead, it maps them separately through independent nonlinear functions and multiplies the results of the two functions to construct the final disturbance influence value. This multiplication method ensures that when both indices are in the high response range, the disturbance influence value shows a significant increase, while when either index is low, it maintains a relatively mild adjustment range, avoiding accidental damage to normal path segments. For example, when the length influence function output is 1.8 and the angle change function output is 2.0, the disturbance influence value is 3.6; when they are 1 and 1.2 respectively, the disturbance influence value is only 1.2, demonstrating significant dynamic adjustment capability.

[0079] To prevent excessively large or small disturbance values ​​from causing interpolation anomalies, this invention establishes upper and lower limit rules for the disturbance influence value. In engineering implementation, the disturbance influence value is limited to a preset range, for example, a lower limit of 0.8 and an upper limit of 3.0. Values ​​exceeding this range are truncated or mapped back to the defined boundaries. This limitation process ensures both the output stability of the compensation factor and the ability to respond to differences in anomaly intensities. The final disturbance influence value serves as the compensation factor in the interpolation correction process, directly affecting the adjustment of the error parameters at intermediate location points, thus achieving dynamic intervention and compensation for path anomalies in error estimation.

[0080] The steps for determining intermediate points for connecting target well pairs include: selecting multiple spatial measurement points along the intermediate drilling path connecting the target well pairs, and extracting the three-dimensional coordinates and 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 multiple spatial measurement points at equal intervals along the drilling path connecting the target well pairs. It is recommended that the distance between measurement points not exceed 30 meters to ensure the spatial resolution of trajectory feature sampling. In actual engineering operations, path nodes are often selected based on logging data or measurement while drilling data, for example, selecting five to ten typical location points for subsequent analysis. Each spatial measurement point needs to have its corresponding three-dimensional coordinates (X, Y, Z) and the wellbore extension direction at that point extracted. The wellbore extension direction can be obtained by constructing a direction vector from the positions before and after the measurement point, which is used for subsequent spatial projection section construction. The selection criteria for multiple spatial measurement points include, but are not limited to: geometric proximity to the midpoint of the line connecting the target well pairs, representativeness of wellbore trajectory changes, and the balance of path segment distribution.

[0081] A reference measuring point is selected in the connection path, and a spatial cross-section perpendicular to this direction is constructed using the wellbore extension direction of the reference measuring point as the normal vector. Adjacent path segments are projected into this spatial cross-section to form the path geometric intersection region. After extracting the spatial information of multiple spatial measuring points, a reference measuring point needs to be selected as the center point for constructing the analysis cross-section. The reference measuring point is preferably selected from those closest to the geometric midpoint of the line connecting the target well pairs to ensure that the cross-section has spatial representativeness. After selecting the reference measuring point, its corresponding wellbore extension direction vector will be used as the cross-section normal vector, thereby constructing a plane perpendicular to this direction, which serves as the spatial cross-section defined in this invention. This cross-section is used to uniformly project all connecting path segments in three-dimensional space, forming a two-dimensional geometric projection picture, thereby transforming complex spatial path relationships into comparable planar structures. All selected measuring point path segments are orthogonally projected onto this cross-section to form the path geometric intersection region, which is used to identify the location points where error coupling is most likely to occur.

[0082] Within the spatial cross-section, the intersection point that is spatially closest to multiple path segments is searched and used as the intermediate point of the error propagation path between the target well pairs. After projecting the path segments onto the spatial cross-section, this step calculates the projected trajectories of all path segments within the cross-section and constructs their two-dimensional coordinate models. By calculating the minimum distance point between each path segment within the cross-section, the intersection region where multiple paths have the closest spatial geometric relationship is identified. Within this intersection region, the point closest to the center point of the cross-section is further identified, and its three-dimensional position coordinates are determined as the intermediate point of the error propagation path between the target well pairs. This intermediate point has two significant characteristics: first, it is the geometric center of the set of all path segments within the projected cross-section, representing the spatial layout of the paths; second, it has the minimum geometric difference in the error propagation path structure and can be used as the central node for error interpolation. This point can be selected by calculating the minimum Euclidean distance or by obtaining the intersection of the path envelopes.

[0083] The three-dimensional coordinates of the intermediate location point are recorded as a spatial connection reference point for error estimation between the target well pairs, and this location point's coordinates and orientation information are continuously referenced in subsequent error interpolation, compensation factor application, and error synthesis. After the intermediate location point is identified, its spatial coordinates and path-corresponding orientation information are used as the core basic data for subsequent error interpolation calculations. The path-corresponding orientation information can be obtained by constructing a vector by connecting adjacent measurement points on both sides of the intermediate location point, which is used to define a local coordinate reference system. In subsequent interpolation algorithms, interpolation weights need to be assigned based on the relative distance from this point to the target well pair; during the application of compensation factors, the point is also used as a correction reference to determine whether there are trajectory anomalies in the path; during error parameter synthesis, the error parameter matrix corresponding to this point needs to be uniformly converted to the common coordinate reference system aligned with the target well pairs. This intermediate location point, as the geometric and error fusion hub for the entire path segment error propagation, possesses high spatial representativeness and stability, and is a key node for achieving continuous expression of path segment errors.

[0084] During the interpolation process, multiple measurement points are selected on both sides of the intermediate position point along the path connecting the target well pairs, and the semi-axis length parameter of the error ellipse at each measurement point is extracted. To ensure that the interpolation input data covers the path changes before and after the intermediate position point, this invention recommends selecting no fewer than two measurement points before and after the intermediate position point, preferably selecting real trajectory sampling points with a spacing of no more than fifty meters. Each measurement point contains the corresponding error ellipse parameter, that is, the length value of the error in the principal and secondary axis directions. For example, the principal and secondary axis lengths of the error ellipse corresponding to the two measurement points on the front side are 0.18 meters and 0.06 meters, respectively, and the lengths of the error ellipse corresponding to the two measurement points on the back side are 0.14 meters and 0.04 meters, respectively. All these data will serve as the basic input for the interpolation calculation.

[0085] Calculate the spatial distance between the intermediate location point and each measurement point, and assign interpolation weights based on these distances. After extracting the 3D coordinates of each measurement point, this step calculates the Euclidean spatial distance from each measurement point to the intermediate location point, and uses this as the basic factor for constructing the interpolation weights. The interpolation weights can be assigned in reciprocal form, or they can be weighted in conjunction with path smoothness or direction consistency factors. Taking four measurement points at distances of 15 meters, 20 meters, 10 meters, and 5 meters from the intermediate location point as an example, the initial weights can be set to 0.067, 0.05, 0.1, and 0.2, respectively, and then redistributed as interpolation proportions after normalization.

[0086] A cubic spline interpolation method is used. Based on the semi-axis length of the error ellipse at each measurement point and the interpolation weight, the semi-axis length of the error ellipse at the intermediate position is calculated as the initial interpolation result. After obtaining the error ellipse parameters and corresponding interpolation weights for each measurement point, this step uses a cubic spline interpolation algorithm to construct a continuous interpolation function for the principal and secondary axis lengths of the error ellipse, thus achieving a natural transition of the error scale in space. The cubic spline function constructs an inter-segment polynomial using the error parameters between measurement points and satisfies the continuity constraints of position, slope, and curvature, avoiding the sharp discontinuity problem that occurs in linear interpolation. Taking the principal axis length values ​​of four measurement points as an example, after constructing the cubic spline function, the principal axis length at the intermediate position is calculated to be 0.165 meters, and the secondary axis length is 0.053 meters. This result is used as the initial interpolation value for subsequent correction.

[0087] A compensation factor is applied to the initial interpolation result to correct the error parameters of the intermediate position points, forming the final error parameters used for error synthesis. To further improve the interpolation result's responsiveness to path anomalies, this step introduces a compensation factor mechanism based on trajectory disturbance perception to adjust the values ​​of the initial interpolation. The compensation factor value originates from the aforementioned trajectory anomaly detection module, and its typical range is between 0.8 and 3.0. When the path segment where the intermediate position point is located is identified as having an abnormal disturbance, and the disturbance intensity is high, for example, when the compensation factor is 2.2, the value of the principal axis interpolation will be adjusted to 0.363 meters, and the value of the secondary axis will be adjusted to 0.117 meters. The corrected error parameters serve as the final error spatial characteristic data of the intermediate position point and are input into the subsequent error synthesis module to participate in the error spatial accumulation analysis between the intermediate and target well pairs.

[0088] The steps for quantitatively expressing the spatial error range between target well pairs specifically include: extracting error parameter matrices for multiple path segments related to the target well pairs and error parameter matrices for intermediate points, and determining the coordinate reference direction corresponding to each error parameter matrix. This step extracts all drilling path segments that form a direct or indirect connection with the target well pairs from the path data that has undergone interpolation correction processing. This includes the segment from the target well start point to the intermediate point, the segment from the intermediate point to the target well end point, and other auxiliary paths that can be directly measured. Each path segment corresponds to an error parameter matrix, typically a two-dimensional covariance matrix, which describes the error dispersion characteristics in the principal and secondary spatial directions, respectively. Simultaneously, the error parameter matrix of the intermediate point is also included as an independent input in the calculation. While extracting each error parameter matrix, the direction vector or attitude reference direction of its corresponding path segment must also be recorded as the basic reference for subsequent coordinate transformation processing.

[0089] The error parameter matrices are unified to a common coordinate reference system through coordinate transformation. The unified error parameter matrices are then superimposed item by item to form the total error parameter matrix of the target well pair. This step establishes a common coordinate reference system with the direction of the target well pair connection as the main axis, typically set as the X-axis, establishing a right-handed coordinate system to form a standard reference system in three-dimensional space. Subsequently, each error parameter matrix is ​​mapped from its original directional coordinate system to this common reference system, specifically through a coordinate rotation matrix. For example, if the original direction of the path segment has an angle θ with the common coordinate system, the error matrix needs to undergo a similarity transformation operation using the rotation matrix R(θ) to ensure that all error components are additable under a unified spatial projection. All transformed error parameter matrices are then added item by item according to their corresponding element positions to form the total error parameter matrix of the target well pair. This matrix contains the synergistic effects of all error propagation paths between the target well pairs, and can fully reflect the distribution trend and uncertainty boundary of the error space.

[0090] Eigenvalue decomposition is performed on the total error parameter matrix to extract the principal axis length and direction angle of the error ellipse between the target well pairs. After constructing the total error parameter matrix, a standard matrix eigenvalue decomposition algorithm (such as the Jacobi iterative method, QR-based decomposition, or singular value decomposition) is used to calculate the matrix, obtaining its two eigenvalues ​​and corresponding eigenvectors. The square root of the eigenvalue represents the scale of the error space in the principal and secondary directions, i.e., the lengths of the major and minor axes of the error ellipse of the target well pair, in meters. The eigenvectors describe the direction angles of the principal and secondary directions of the error space in three-dimensional space, and the angles with the coordinate reference system are used to express the spatial orientation of the error uncertainty boundary. For example, in a certain calculation, the principal axis length is 0.26 meters, the secondary axis length is 0.12 meters, and the corresponding principal direction deviates from the direction of the line connecting the target well pairs by approximately 15 degrees.

[0091] The eigenvalue decomposition results are used as a spatial quantitative expression of the error range between target well pairs, for use in well safety tolerance assessment, interference avoidance strategy formulation, and magnetic ranging parameter optimization. This step uses the extracted primary axis length, secondary axis length, and orientation angle as the final error expression output, which can be used for error visualization between target well pairs, engineering risk assessment, and dynamic tolerance control.

[0092] The above algorithms or formulas are all dimensionless and numerical calculations, and the results are obtained by software simulation based on a large amount of collected data to obtain the most recent real-world results. The preset parameters are set by those skilled in the art according to the actual situation.

[0093] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0094] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for calculating the magnetic ranging error of a well network, characterized in that, Includes the following steps: Magnetic ranging data of each drilling path segment in the well network are collected, multiple independent error sources are identified, an error parameter matrix of each drilling path segment is constructed, and principal axis parameters reflecting the spatial distribution of errors are extracted by eigenvalue decomposition. The system analyzes whether there are continuity anomalies in the intermediate drilling paths connecting the target well pairs. It judges whether there are trajectory anomalies by comparing the magnitude of path attitude changes with the preset path smoothness threshold, and generates compensation factors for adjusting error calculations based on the judgment results. To determine the intermediate point used to connect the target well pairs, in a spatial configuration containing multiple drilling paths, select a section perpendicular to the path extension direction and calculate the intermediate point on that section that has the closest geometric relationship with each path. Based on the relative distance from the target well to the intermediate location point and the error parameters of multiple path segments, the error parameters of the intermediate location point are calculated by an interpolation algorithm, and a compensation factor is applied to the interpolation process to correct the impact of path anomalies on error estimation. The error parameters of multiple path segments and intermediate points related to the target well pair are uniformly transformed to the same coordinate reference system. The transformed error parameter matrices are superimposed, and the resulting matrix is ​​decomposed by eigenvalues ​​to extract the principal axis length and direction angle of the magnetic ranging error between the target well pairs, thus completing the spatial quantitative expression of the error range between the target well pairs.

2. The method for calculating the magnetic ranging error of a well network according to claim 1, characterized in that, The steps for constructing the error parameter matrix for each drilling path segment include: Collect magnetic induction intensity, well depth information, and installation attitude data of magnetic ranging tools related to the drilling path; Calculate the error quantification parameters caused by eccentric installation error, sensor measurement deviation, relative well depth measurement deviation, and spatial attitude change in the drilling path, respectively; Based on the error quantification parameters, weight functions for different error terms are established, and the influence of each error term on the ranging result is expressed in the form of covariance. The covariances of multiple error terms are combined to form an error parameter matrix that describes the spatial uncertainty of the drilling path segment.

3. The method for calculating the magnetic ranging error of a well network according to claim 1, characterized in that, The steps for extracting the principal axis parameters that reflect the spatial distribution of error include: Perform matrix decomposition on the error parameter matrix to transform it into a decomposition form containing two eigenvectors and two eigenvalues. The square root of the eigenvalues ​​is used to obtain the scale values ​​of the error in the two principal directions, and these are used to determine the lengths of the major and minor axes of the error space. The directions corresponding to the feature vectors are taken as the two main directions of the error space, which are used to describe the spatial orientation of the error ellipse. The main shaft length and the main direction are used together as the error spatial distribution parameters of the drilling path segment.

4. The method for calculating the magnetic ranging error of a well network according to claim 3, characterized in that, 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 their corresponding eigenvalues, which are used to construct the error ellipse parameters between the target well pairs.

5. The method for calculating the magnetic ranging error of a well network according to claim 4, characterized in that, The generation logic of the compensation factor refers to: Extract the changes in well inclination angle and well azimuth angle at multiple continuous measurement points along the intermediate drilling path; For each pair of adjacent measurement points, calculate its attitude change amplitude. The attitude change amplitude is the spatial offset measure formed by the change in well inclination angle and the change in well azimuth angle of two consecutive measurement points, which 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. If the attitude change amplitude exceeds the threshold, the path segment between the two measurement points is marked as having a trajectory anomaly. Among all path segments marked as trajectory anomalies, calculate the total length of the anomaly segments and the ratio of it to the length of the entire intermediate path, which serves as the length percentage indicator for that path. In all abnormal path segments, the average value of their attitude change amplitude is calculated and used as the attitude change index for that path. The length ratio index and attitude change index are normalized to the range between zero and one, and then nonlinear function processing is performed on them respectively. The two processing results are multiplied to obtain the disturbance influence value. The disturbance impact value is limited to a preset range according to the restriction rules and used as a compensation factor in the interpolation correction process.

6. The method for calculating the magnetic ranging error of a well network according to claim 5, characterized in that, The generation of the compensation factor involves the following nonlinear function processing steps: The length proportion index is input into the length influence function, which maps the input proportion to output values ​​at different levels according to the preset segmentation rules. The attitude change index is input into the angle change function. The angle change function processes the input value according to a preset threshold range. In the range below the minimum threshold, the output remains at the basic level. When the input value is between the minimum and maximum thresholds, the function output increases monotonically with the input value. In the range above the maximum threshold, the output increases according to the enhancement rule.

7. The method for calculating the magnetic ranging error of a well network according to claim 6, characterized in that, The steps for determining the intermediate location points used to connect the target well pairs include: Multiple spatial measurement points are selected in the intermediate drilling path connecting the target well pairs, and the three-dimensional coordinates and wellbore extension direction corresponding to each measurement point are extracted. Select a reference measuring point in the connection path, and construct a spatial section perpendicular to the wellbore extension direction of the reference measuring point as the normal vector. Project adjacent path segments into the spatial section to form the path geometric intersection area. Within the spatial cross section, search for the intersection point that is spatially closest to multiple path segments, and use it as the intermediate point of the error propagation path between the target well pairs.

8. The method for calculating the magnetic ranging error of a well network according to claim 7, characterized in that, During the interpolation process, multiple measurement points located on both sides of the middle position point are selected in the path connecting the target well pairs, and the error ellipse semi-axis length parameter of each measurement point is extracted. Calculate the spatial distance between the intermediate location point and each measurement point, and assign interpolation weights based on the distance; The cubic spline interpolation method is used to calculate the length of the error ellipse semi-axis at the intermediate position point based on the length of the error ellipse semi-axis at each measurement point and the interpolation weight, which is used as the initial interpolation result. The compensation factor is applied to the initial interpolation result to correct the error parameters at intermediate points, forming the final error parameters used for error synthesis.

9. The method for calculating the magnetic ranging error of a well network according to claim 8, characterized in that, The specific steps for completing the spatial quantitative expression of the error range between target well pairs include: Extract the error parameter matrices of multiple path segments related to the target well pair and the error parameter matrices of intermediate position points, and determine the coordinate reference direction corresponding to each error parameter matrix; Each error parameter matrix is ​​unified to a common coordinate reference system through coordinate transformation. The unified error parameter matrices are then superimposed item by item to form the total error parameter matrix of the target well pair. Eigenvalue decomposition is performed on the total error parameter matrix to extract the principal axis length and direction angle of the error ellipse between the target well pairs, thus completing the spatial quantitative expression of the error range of the target well pairs.

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