Fault plane and well trajectory intersection point positioning method based on multi-dimensional space search and data optimization

Abstract

The invention relates to the field of oil and gas structure modeling, and particularly discloses a fault plane and well trajectory intersection point positioning method based on multi-dimensional space search and data optimization. According to the method, fault data and well trajectory data are preprocessed into a 3D point cloud data set under a unified coordinate system, a k-d tree and a nearest neighbor search algorithm are utilized to quickly retrieve a minimum distance point pair between a curved surface and a curve, and local reconstruction and gridding interpolation encryption technologies are adopted to finely optimize the curved surface. The description capability of the local geometric features of the curved surface is enhanced, and accurate positioning of the intersection point of the curved surface and the curve is finally realized through multiple iterative optimization. According to the method, the geometric algorithm and the acceleration technology are combined, the calculation efficiency is improved, meanwhile, the accuracy and stability of intersection point positioning are guaranteed, and the method is suitable for curved surface intersection point calculation in complex scenes such as oil-gas exploration, underground water monitoring and geological modeling.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas structure modeling and geological data processing, and in particular to a method for locating the intersection of a fault plane and a well trajectory based on multidimensional space search and data optimization, in particular, by combining a kd-tree acceleration algorithm, spatial data optimization and geometric calculation technology to achieve automatic positioning of the intersection of a surface and a curve. Background Art

[0002] In the field of geoscience and engineering, accurately locating the intersections between fault planes and well trajectories (i.e., "breakpoints") is crucial for oil and gas field development, groundwater monitoring, and geological modeling. These intersections can help engineers and scientists accurately analyze underground structures to optimize resource extraction and management. However, due to the complexity of underground geological structures and the irregularity of fault and well trajectory data, how to efficiently and accurately locate these intersections has become a challenging technical problem.

[0003] In traditional methods, the location of the intersection between faults and well trajectories often relies on manual identification and judgment by geologists. This experience-based method is not only inefficient, but also difficult to ensure the accuracy of identification when facing complex or large-scale data. In addition, in the absence of auxiliary information (such as well logging, mud logging and core data), solving the intersection between faults and well trajectories can be regarded as the problem of calculating the intersection of surfaces and curves. Although it can be solved by analytical mathematical methods in theory, in practical applications, faults and well trajectories are usually represented in the form of discrete 3D point clouds. In order to solve the intersection problem between these point cloud data, traditional methods often use the method of traversing all data points and calculating the Euclidean distance, and selecting the point pair with the smallest distance as the intersection approximation. Although this "brute force" method is intuitive, its time complexity is as high as O(m*n) (m and n are the number of points on the surface and curve, respectively), the calculation efficiency is low, and it is difficult to apply in practice when processing large-scale data sets. In addition, fault point cloud data is usually sparse, making it difficult to ensure the calculation accuracy, especially when the surface point spacing is large, the error increases significantly. To improve the calculation accuracy, a common improvement method is to grid the fault surface data to reduce the point spacing, so as to more accurately approximate the intersection. However, gridding not only increases the complexity of data preprocessing, but also brings significant computational load in large-scale calculations, affecting the overall efficiency. When the data is irregular, the effect of the gridding method is particularly poor, which may lead to greater positioning errors.

[0004] In response to the above problems, the present invention proposes a method for locating the intersection of oil and gas structural surfaces and curves based on multidimensional space search and data optimization technology. This method not only greatly improves the calculation efficiency by introducing the kd tree acceleration algorithm, combined with the local reconstruction of the surface and the grid interpolation encryption technology, but also significantly improves the accuracy of intersection positioning through a series of iterative optimization steps. In the implementation process, this method fully combines the technical means of data processing and spatial geometric calculation, can effectively respond to engineering needs under complex geological conditions, and provides an efficient and reliable technical solution for oil and gas reservoir exploration, groundwater monitoring and geological modeling. Summary of the invention

[0005] The purpose of the present invention is to provide a method for locating the intersection of fault planes and well trajectories based on multidimensional spatial search and data optimization. By combining spatial data processing, geometric calculation and algorithm optimization, the positioning accuracy is improved while the calculation efficiency is greatly improved to meet the engineering needs in complex data environments in oil and gas structure modeling.

[0006] To achieve the above purpose, the present invention adopts Figure 1 The technical solution shown:

[0007] S1. Fault and well trajectory data preprocessing

[0008] The fault data set of the selected target area and the well trajectory data set passing through the target area are both represented by 3D point cloud data, and the outliers in the data set are removed to establish the surface and curve data sets with consistent coordinate systems. Through data format unification and coordinate system transformation, the spatial consistency of the fault point cloud data and the well trajectory point cloud data is ensured, so as to achieve accurate matching and processing of subsequent calculations.

[0009] Among them, the curve dataset can be encrypted and resampled according to the accuracy requirements; if the surface dataset is a scattered point in the time domain, it needs to be converted to the depth domain through the time-depth relationship model so that the two datasets are unified into the same coordinate system, represented by X, Y, and Z, to ensure the accuracy of the calculation and the engineering applicability of the data.

[0010] S2, original minimum distance point pair retrieval

[0011] By building an efficient spatial index and using an acceleration algorithm, the minimum distance point pair between the surface and the curve is quickly found and the current minimum distance is recorded. This acceleration algorithm combines the kd tree structure with the nearest neighbor search algorithm ( Figure 2 ), the detailed steps are as follows:

[0012] Step S21: constructing the surface data set into a kd-tree structure for efficient indexing and retrieval in a high-dimensional space;

[0013] Step S22: For each point in the curve data set, use the nearest neighbor search algorithm to find the closest point on the surface on the kd tree and record the current minimum distance.

[0014] S3, surface local reconstruction and meshing interpolation encryption

[0015] In the point pair found in step S2, the point located on one side of the surface is called point A. Based on the acceleration algorithm in step S2, search for the N points closest to point A in the surface data set (N is generally 10 to 30, and does not include point A), and check whether the outer envelope formed by these points can include point A. If the envelope cannot include point A, increase the value of N until the envelope can include point A. Based on these points, a 3D surface mesh is constructed by the bivariate spline interpolation method to optimize the geometric feature description of the surface data to replace the initial surface data set, realize the accurate reconstruction of the local structure of the fault plane, and ensure the calculation accuracy and the smoothness and continuity of the surface.

[0016] S4, Iterative Optimization and Intersection Approximation

[0017] Repeat steps S2 and S3 to gradually reduce the minimum distance between the surface and the curve. Through multiple iterations and optimization calculations, the intersection point is accurately approximated until the desired positioning accuracy is achieved to meet engineering requirements.

[0018] Beneficial effects of the present invention:

[0019] (1) By constructing a kd tree structure and using the nearest neighbor search algorithm, the computational time complexity of sparse fault surface data is significantly reduced. Compared with the traditional traversal method, it can quickly narrow the search range, reduce a large number of invalid calculations, and improve data processing efficiency, especially in large-scale oil and gas structure modeling.

[0020] (2) Through local reconstruction and grid interpolation encryption, the local feature description and positioning accuracy of the surface composed of fault planes and well trajectory data are improved, while the smoothness and continuity of the surface representation are ensured, which is suitable for oil and gas exploration and development in complex geological structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below in conjunction with the accompanying drawings.

[0022] Figure 1 It is the overall technical roadmap for the rapid location method of the intersection of underground space surfaces and curves;

[0023] Figure 2 It is a schematic diagram of the nearest neighbor acceleration algorithm for the fast location method of the intersection of underground space surface and curve;

[0024] Figure 3It is a scatter plot of faults and well trajectories after preprocessing according to the embodiment of the present invention;

[0025] Figure 4 It is to use the local gridding interpolation encryption map of the fault scattered points described in the embodiment of the present invention;

[0026] Figure 5 It is a local enlarged image after gridding interpolation encryption as described in the embodiment of the present invention;

[0027] Figure 6 It is a graph showing the change in the number of grid parameters using the calculation time and error according to the embodiment of the present invention. DETAILED DESCRIPTION

[0028] Below, a certain area in the Jizhong Depression in Hebei Province is taken as an example study area, a certain fault is selected as an example of an underground space surface, and the well trajectory of a certain well is selected as an example of a space curve, and the implementation scheme of the present invention is described in conjunction with the accompanying drawings. The embodiment of the present invention is intended to show how to achieve efficient and accurate positioning of the intersection of the surface and the curve by combining spatial data processing, geometric calculations and optimization algorithms. Obviously, the described embodiments are only partial implementations of the present invention, not all implementations. Other embodiments obtained by ordinary technicians in this field without doing creative work should also belong to the scope of protection of the present invention.

[0029] The specific implementation steps are as follows:

[0030] Step S1: Surface data and curve data preprocessing

[0031] A fault data set in a certain area of ​​the Jizhong Depression in Hebei Province was selected, outliers in the data were removed, and the time domain data was transformed into the depth domain through the time-depth relationship model to establish a standard XYZ coordinate system scattered data set; a well trajectory data set that passed through the above fault was selected, outliers in the data were removed, and through encryption and resampling, the vertical resolution of the well trajectory data reached 0.1 meters to ensure the accuracy and consistency of the data in subsequent calculations. Through data preprocessing, two 3D point cloud data sets in a unified coordinate system were established, representing the fault surface and the well trajectory curve, respectively, as the basis for subsequent calculations.

[0032] like Figure 3 As shown, the fault scatter points are distributed relatively sparsely in the XYZ space, while the well trajectory is relatively denser, similar to a surface formed by a straight line passing through the fault scatter points.

[0033] Step S2: Minimum distance point pair retrieval based on spatial index and acceleration algorithm

[0034] Using spatial indexing technology (kd-tree) and acceleration algorithms, the minimum distance point pair retrieval is performed in surface and curve data sets. First, the point cloud data on the surface is constructed into a kd-tree structure for efficient indexing and retrieval in high-dimensional space; then, for each point on the curve, the nearest neighbor search algorithm is used to find the nearest point on the surface in the kd-tree structure, record the shortest distance from each curve point to the corresponding surface point, and save the index information of both. This method can quickly and accurately find the global minimum distance point pair, significantly improving the computational efficiency.

[0035] like Figure 4 As shown in the figure, the N points searched in the surface data set based on the acceleration algorithm are distributed around point A. The encrypted mesh surface obtained by interpolation based on these points is located in the original surface and is also passed through the well trajectory. Point A and point B in the figure are the minimum distance point pair, A is a point on the surface, and B is a point on the well trajectory. The distance between the two is more than 50 meters.

[0036] Step S3: Local surface reconstruction and meshing interpolation encryption

[0037] Among the minimum distance point pairs found in step S2, the point located on one side of the surface is called point A. Based on the acceleration algorithm, the N points closest to point A (N is generally 10 to 30) are searched from the surface data set, and based on these points, the local 3D surface mesh is reconstructed by the bivariate spline interpolation method. The reconstructed surface mesh better describes the local geometric features, improves the smoothness and continuity of the surface representation, and ensures the precision and accuracy of the intersection location.

[0038] Step S4: Iterative optimization and intersection approximation

[0039] The tolerance threshold is set to 5 meters. After local reconstruction and meshing interpolation encryption of the surface, the acceleration algorithm in step S2 is re-run to obtain a new minimum distance point pair. At this time, the minimum distance is 3.09 meters, which is less than the set threshold, meets the expected requirements, and achieves accurate approximation of the intersection. Through multiple iterative optimizations, the present invention can obtain high-precision surface and curve intersection positioning in a relatively short time, meeting the high-efficiency and high-precision requirements in oil and gas exploration and development.

[0040] like Figure 5 As shown, after running step S2 again, a new minimum distance point pair is obtained, where C is a point on the new surface after encryption, and D is a point on the well trajectory. The positions of the two points are almost coincident, which greatly improves the positioning accuracy.

[0041] like Figure 6As shown in Figure 1, the number of locally encrypted grids significantly affects the calculation efficiency and accuracy. The more grids there are, the smaller the calculation error is, but the longer the calculation time is. It should be noted that when the number of grids reaches a certain level, the accuracy improvement effect is no longer obvious, and the calculation time will increase sharply.

[0042] The present invention combines spatial data processing with geometric optimization technology, and realizes efficient and accurate positioning of the intersection of the fault plane and the well trajectory through steps such as kd tree indexing and iterative optimization. It is particularly suitable for oil and gas structure modeling in complex geological environments. Experimental data show that the calculation time of the traditional traversal method exceeds 20 minutes, while the acceleration algorithm of the present invention takes less than 2 seconds, which significantly improves efficiency. At the same time, the number of local encrypted grids has a significant impact on the calculation accuracy and efficiency, and needs to be reasonably set according to the scene requirements in actual applications to balance the calculation time and accuracy requirements.

[0043] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0044] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A method for locating the intersection of a fault plane and a well trajectory based on multidimensional space search and data optimization, characterized in that: The following steps are involved: S1. Preprocessing of fault and well trajectory data: The fault data set of the selected target area and the well trajectory data set passing through the target area are both represented by 3D point cloud data, and the outliers in the data set are removed to establish the surface and curve data sets with consistent coordinate systems; through data format unification and coordinate system transformation, the fault point cloud data is mapped to the surface data set, and the well trajectory point cloud data is mapped to the curve data set to ensure the consistency and accuracy of subsequent spatial calculations; S2. Original minimum distance point pair retrieval: Based on spatial indexing technology and acceleration algorithm, the minimum distance point pair between the surface and the curve is retrieved in high-dimensional space, and the current minimum distance is recorded; the acceleration algorithm combines the kd tree structure and the nearest neighbor search algorithm, and realizes the retrieval and precise matching of large-scale data sets through the established inter-index system; S3, local reconstruction of the surface and meshing interpolation encryption: Among the point pairs found in step S2, the point located on one side of the surface is called point A. Based on the acceleration algorithm, the N points closest to point A are searched in the surface data set, where N is 10 to 30 and does not include point A, and it is checked whether the outer envelope of these points can include point A; if the outer envelope cannot include point A, the value of N is increased until it is included; based on these points, the local 3D surface mesh is reconstructed by the interpolation method to improve the accuracy of the geometric feature description of the surface, so as to replace the initial surface data set and ensure the smoothness and continuity of the spatial structure; S4, iterative optimization and intersection approximation: repeat steps S2 and S3, gradually reduce the minimum distance between the surface and the curve, and achieve intersection approximation through multiple iterations and precise calculations, and finally achieve the desired positioning accuracy.

2. A method for locating the intersection of a fault plane and a well trajectory based on multidimensional space search and data optimization according to claim 1, characterized in that: In step S1, the curve data set can be encrypted and resampled according to the accuracy requirements; if the surface data set is scattered point data in the time domain, it is converted to the depth domain through the time-depth relationship model to ensure that the two data sets are unified in the same coordinate system, expressed in the X, Y, and Z coordinate system, to facilitate subsequent calculations.

3. The method for locating the intersection of a fault plane and a well trajectory based on multidimensional space search and data optimization according to claim 1, characterized in that: The acceleration algorithm in step S2 includes the following steps: S21, constructing the surface dataset into a kd-tree structure for indexing and retrieval in high-dimensional space; S22. For each point in the curve data set, use the nearest neighbor search algorithm to find the nearest point on the corresponding surface in the kd tree structure, and record the current minimum distance.

4. The method for locating the intersection of a fault plane and a well trajectory based on multidimensional space search and data optimization according to claim 1, characterized in that: The interpolation method in step S3 is a bivariate spline interpolation method.

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