Calculation method, system and equipment for efficient matching and integration of cluster wellheads and targets

Abstract

The present invention provides a method, system and equipment for efficient matching and integration calculation of cluster wellheads and targets, belonging to the field of oil and gas development drilling technology. The efficient matching and integration calculation method includes: calculating the horizontal displacement from each wellhead to each target point, and assigning weights to the wellheads according to the distribution of the horizontal displacement; using the smallest horizontal displacement and the highest weight as the optimal matching basis, using a bipartite graph algorithm to complete the initial matching of the wellhead and the target point; connecting the matched wellhead and the target point through a line segment, and judging whether any two preliminarily matched wellhead and target point connecting lines have the possibility of intersecting, and the intersection point falls on the horizontal projection of the wellbore trajectory. If so, a convex hull detection algorithm is used to perform secondary matching of the wellhead and the target point for the two intersecting straight lines. If not, the final matching of the wellhead and the target point is completed. The present invention can provide a unified matching mode for the matching of cluster wellheads and targets, can quickly and conveniently perform matching, and reduce the risk of wellbore collision from the design level.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas development drilling technology, and specifically to a method for efficiently matching and integrating cluster wellheads with targets, a system for efficiently matching and integrating cluster wellheads with targets, and a computer device and computer-readable storage medium for implementing efficient matching and integrating cluster wellheads with targets. Background Art

[0002] Oil and natural gas resources are a crucial energy source supporting the rapid development of China's industry. In recent years, to achieve cost-effective and high-efficiency oil and gas resource development, conventional cluster well development models have been widely adopted in the Sichuan-Chongqing and Tarim regions, while large-platform cluster wells (over 50 wells) have been adopted in the Changqing region. For these large-platform cluster wells, the drilling design process is subject to environmental constraints, the platform's topography, and surrounding conditions, resulting in irregular alignment between surface wellheads and underground targets. This makes it difficult to match wellheads with targets, irrational target allocation, and difficult to achieve non-intersecting horizontal projections of the trajectory according to the overall towing direction of the drilling rig. This results in high anti-collision pressure for construction units, complex obstacle avoidance trajectories, high friction during tripping, and unfavorable casing installation, seriously impacting subsequent operations. Furthermore, conventional directional well trajectory design software has a low degree of automation, and the wellhead-target correspondence, profile type, and trajectory design control parameters rely on individual experience, compromising the scientific and operational nature of trajectory design.

[0003] Cluster wells feature close spacing between wellheads, a large number of wells, and complex spatial relationships in wellbore trajectories, making wellhead-target matching challenging. Currently, there's no unified matching model in China, and the matching process relies entirely on the designer's personal preferences. Furthermore, manual matching of cluster wellheads and targets cannot guarantee a globally optimal match every time. Furthermore, the quality of wellhead-target matching varies widely, depending on the operator's skill level and personal experience. Poor wellhead-target matching increases the difficulty and risk of collision avoidance operations. Generally speaking, trajectory design for a shale gas platform with 5-8 horizontal wells takes approximately three days, while trajectory design for a platform with 9-13 horizontal wells takes approximately six days.

[0004] Efficiently matching wellheads and targets is fundamental to collision avoidance design for platform wells and large cluster wells, and is a crucial approach to reducing wellbore collisions in large platform cluster wells. Current drilling engineering design software, such as Landmark (foreign) and Sunning Pathing (domestic), lacks efficient platform wellhead and target matching capabilities. This requires manual intervention in the initial stages of wellhead and target matching, which is difficult and inefficient. If there are a large number of platform wells, such as the 50-60 wells on a single platform in Changqing, manual intervention is required to match wellheads and targets, resulting in a high design workload and no guarantee of globally optimal matching.

[0005] Currently, there is a lack of theoretical research on the efficient matching of wellheads and targets. For example, patent document CN102392601A, published on March 28, 2012, and entitled "Method for Determining Borehole Trajectory of Multi-Target Horizontal Wells," describes a method for determining the wellbore trajectory of multi-target horizontal wells. This method combines engineering and geology to achieve wellbore trajectory matching for multi-target horizontal wells. However, this method requires high technical skills from engineering designers. Patent document CN111173450A, published on May 19, 2020, and entitled "Method for Designing Borehole Trajectory for Well Cluster Fields," describes a method for designing wellbore trajectories for well cluster fields. Although this method uses horizontal projections to match wellheads and targets, simplifying the calculation process, it still requires obtaining a large number of parameters and performing complex matching calculations for targets and wellheads. Both of the above methods require manual intervention to perform complex matching calculations, which takes a long time and a large amount of design work. The matching process is completely dependent on the designer's personal experience, and the above methods cannot achieve automatic and fast matching. Summary of the Invention

[0006] The present invention aims to address at least one of the aforementioned deficiencies in the prior art. For example, the present invention provides a method and system for efficiently integrating and calculating the matching of cluster wellheads with target points, overcoming the shortcomings of conventional methods, such as high workload, slow matching speed, and the inability to guarantee global optimal matching.

[0007] In order to achieve the above objectives, the present invention provides, on one hand, a method for efficiently matching and integrating cluster wellheads with target points.

[0008] The efficient integrated calculation method for matching wellheads and targets includes the following steps: S1, calculating the horizontal displacement Di from each wellhead to each target point, and assigning weights to the wellheads according to the distribution of the horizontal displacements; S2, using the bipartite graph KM algorithm to perform preliminary matching between the wellheads and targets, with the smallest horizontal displacement and the highest weight as the optimal matching basis; S3, connecting the matched wellheads and targets with line segments, and determining whether any two preliminarily matched lines connecting the wellheads and targets have the possibility of intersecting, and whether the intersection point falls on the horizontal projection of the wellbore trajectory. If so, a convex hull detection algorithm is used to perform secondary matching between the wellheads and targets for the two intersecting lines. If not, a final matching between the wellheads and targets is completed.

[0009] In an exemplary embodiment of the method for efficiently matching and integrating the cluster wellhead and the target point of the present invention, when Di≥2000m, the wellhead weight value is 0.1; when 1800m≤Di<2000m, the wellhead weight value is 0.2; when 1800m<Di≤1600m, the wellhead weight value is 0.3; when 1600m<Di≤1400m, the wellhead weight value is 0.4; when 1400m<Di≤1200m, the wellhead weight value is 0.5; when 1200m<Di≤1000m, the wellhead weight value is 0.6; when 1000m<Di≤800m, the wellhead weight value is 0.7; when 800m<Di≤600m, the wellhead weight value is 0.8.

[0010] In an exemplary embodiment of the method for efficiently matching and integrating the cluster wellhead and the target point of the present invention, step S2 may include the following sub-steps: S21. According to the size of Di, assign top label values to each element in {Xi} and {Yi}, where {Xi} is the wellhead set on the left side of the bipartite graph, and {Yi} is the target point set on the right side of the bipartite graph; S22. Starting from the first vertex A in {Xi}, traverse all vertices in {Yi}, and determine whether a certain vertex B in {Yi} can be found to satisfy both: A and B are the optimal match, and point B has not been matched or rematched. If so, go to sub-step S24; if not, go to sub-step S23; the basis for the optimal match is the smallest horizontal displacement and the highest weight value; S23. Under the condition that the total weight value remains unchanged before and after matching, modify the marked top label values that do not meet the matching conditions, and return to sub-step S22; S24. Mark the matched wellhead and target point, and starting from the next vertex in {Xi}, return to sub-step S22 to perform the matching of the next vertex in {Xi} and a certain point in {Yi} until all wellheads and target points are matched one by one.

[0011] In an exemplary embodiment of the method for efficiently matching and integrating the cluster wellhead and the target point of the present invention, in sub-step S23, the marked top label values that do not meet the matching conditions are modified by subtracting the d value from the marked top label value of the wellhead and adding the d value to the marked top label value of the target point, where d = min{C x[i] +C y[j] -weight[i][j]}, d is the top label correction coefficient, C x[i] is the top label value of the i-th wellhead, C y[j] is the top label value of the j-th target point, and weight[i][j] is the sum of the top label value of the i-th wellhead and the top label value of the j-th target point.

[0012] In an exemplary embodiment of the method for efficiently matching and integrating the cluster wellhead and the target point of the present invention, the specific steps for determining whether there is a possibility of intersection between any two preliminarily matched wellhead and target point connection lines and whether the intersection point falls on the horizontal projection of the wellbore trajectory are as follows: determining the coordinates (X1, Y1) of each wellhead and the coordinates (X2, Y2) of each target point; determining the straight line equations of the two preliminarily matched wellhead and target point connection lines, and determining the coordinates (x, y) of the intersection point P; if X1 < x < X2 or Y1 < y < Y2 is satisfied, it indicates that the intersection point P falls on the horizontal projection of the wellbore trajectory.

[0013] In an exemplary embodiment of the method for efficiently matching and integrating the cluster wellhead and the target point of the present invention, the specific steps for performing secondary matching of the wellhead and the target point for two intersecting straight lines by using the convex hull detection algorithm are as follows: a) putting each vertex corresponding to the case where all intersection points P fall on the horizontal projection of the wellbore trajectory into a two-dimensional rectangular coordinate system, and taking the vertex C0 with the smallest ordinate as the coordinate reference point, wherein the vertices include wellheads and target points; b) calculating the argument α of each vertex relative to the vertex C0, sorting each vertex in ascending order of the argument, and determining the first vertex C1 and the last vertex C2 in the sorting as the points on the convex hull; c) making a directed line L according to the two frontmost vertices C0, C1 at the top of the stack, and judging the azimuth relationship between the next vertex and the directed line L; d) if the next vertex is located on the right side of the directed line L, popping the element at the top of the stack, selecting the next vertex as the new current point, and repeating step c; e) if the next vertex is located on the left side of the directed line L, or the next vertex is located on the directed line L, checking whether the current vertex is the last vertex C2, if so, terminating the convex hull detection, if not, continuing to select the next vertex as the new current point, and repeating step c; f) after the convex hull detection is completed, deleting the line segments connecting wellhead to wellhead and target point to target point, matching the line segments connecting wellhead to target point, and rematching the wellheads and target points inside the convex polygon; g) judging whether there is a possibility of intersection between the rematched wellhead and target point connection lines and whether the intersection point falls on the horizontal projection of the wellbore trajectory, if so, repeating steps a to f until all line segment intersection problems are solved.

[0014] Another aspect of the present invention provides an integrated calculation system for efficiently matching cluster wellheads and targets. The system includes a basic parameter determination unit, a primary matching unit, a verification unit, and a secondary matching unit. The basic parameter determination unit is configured to calculate the horizontal displacement Di from each wellhead to each target and assign weights to the wellheads based on the distribution of the horizontal displacements. The primary matching unit is connected to the basic parameter determination unit and configured to perform preliminary matching of the wellheads and targets using a bipartite graph KM algorithm, taking the minimum horizontal displacement and the highest weight as the optimal matching criteria. The verification unit is connected to the primary matching unit and configured to determine whether any two preliminarily matched lines connecting the wellheads and targets have the possibility of intersecting, and whether the intersection point falls on the horizontal projection of the wellbore trajectory, and output the corresponding wellhead coordinates and target coordinates for all intersection points that fall on the horizontal projection of the wellbore trajectory. The secondary matching unit is connected to the verification unit and configured to perform secondary matching of the wellheads and targets for the two intersecting lines using a convex hull detection algorithm.

[0015] In an exemplary embodiment of the integrated computing system for efficient matching of cluster wellheads and targets of the present invention, the initial matching unit includes a top label assignment module, a traversal module, a top label modification module and a marking module, wherein the top label assignment module is configured to assign a top label value to each element in {Xi} and {Yi} according to the size of Di, wherein {Xi} is the set of wellheads on the left side of the bipartite graph, and {Yi} is the set of targets on the right side of the bipartite graph; the traversal module is connected to the top label assignment module and is configured to start from each vertex in {Xi}, traverse all vertices in {Yi}, and determine whether a vertex in {Yi} can be found that simultaneously satisfies: the two vertices are optimal matches, and the vertices in {Yi} have not been matched or re-matched; the top label modification module is connected to the traversal module and is configured to modify the marked top label values ​​that do not meet the matching conditions while keeping the total weight before and after matching unchanged; the marking module is connected to the traversal module and is configured to mark the matched wellheads and targets.

[0016] In an exemplary embodiment of the integrated computing system for efficient matching of cluster wellheads and targets of the present invention, the secondary matching unit includes a coordinate reference point determination module, an argument calculation module, a convex hull vertex initial determination module, an orientation judgment module, a convex hull vertex secondary determination module, a matching module, and a verification module. The coordinate reference point determination module is configured to sort the wellheads and targets corresponding to all intersection points falling on the horizontal projection of the wellbore trajectory, and to use the vertex C0 with the smallest ordinate as the coordinate reference point; the argument calculation module is connected to the coordinate reference point determination module and is configured to calculate the argument α of each vertex relative to the vertex C0; the convex hull vertex initial determination module is connected to the argument calculation module and is configured to sort the vertices in ascending order of argument, and determine the first-ranked vertex C1 and the last-ranked vertex C2 as points on the convex hull; the orientation judgment module is configured to draw a directed line L based on the first two vertices C0 and C1 at the top of the stack, and to judge the orientation relationship between the next vertex and the directed line L; The convex hull vertex secondary determination module is connected to the orientation judgment module and is configured to determine other vertices on the convex hull based on the orientation relationship between the next vertex and the directed straight line L; the matching module is connected to the convex hull vertex secondary determination module and is configured to eliminate the line segments connecting the wellheads and the target points, match the line segments connecting the wellheads and the target points, and re-match the wellheads and target points inside the convex polygon; the verification module is connected to the matching module and is configured to verify whether there is a possibility that the lines connecting the re-matched wellheads and target points intersect, and whether the intersection point falls on the horizontal projection of the wellbore trajectory.

[0017] In another aspect, the present invention provides a computer device comprising: a processor; and a memory storing a computer program. When the computer program is executed by the processor, the above-mentioned calculation method for efficiently matching a wellhead with a target point is implemented.

[0018] In another aspect, the present invention provides a computer-readable storage medium, which, when executed by a processor, implements the above-mentioned calculation method for efficient matching of wellheads and targets, thereby achieving accurate and rapid matching of wellheads and targets.

[0019] Compared with the prior art, the beneficial effects of the present invention may include at least one of the following:

[0020] (1) The present invention proposes an integrated calculation method for efficient matching of cluster wellheads and targets, which solves the technical problem of efficient matching of cluster wellheads and targets on large platforms and provides a unified matching mode for matching of cluster wellheads and targets on large platforms.

[0021] (2) The present invention effectively reduces the risk of cluster wellbore collision from the perspective of wellbore trajectory design, providing necessary guarantees for safe and rapid drilling of cluster wells on large platforms;

[0022] (3) The present invention has high calculation efficiency, short calculation output time, and a fast and convenient entire matching process, which can greatly reduce the workload of designers and improve the output efficiency of drilling design. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other objects and / or features of the present invention will become more apparent from the following description in conjunction with the accompanying drawings, in which:

[0024] Figure 1 A flow chart of a method for preliminary matching of wellheads and targets is shown, which is an exemplary embodiment of the integrated calculation method for efficient matching of cluster wellheads and targets of the present invention.

[0025] Figure 2 A secondary matching flow chart of an exemplary embodiment of the integrated calculation method for efficient matching of cluster wellheads and targets of the present invention is shown.

[0026] Figure 3 The figure shows an overall flow chart of an exemplary embodiment of the method for efficiently integrating and calculating the matching of cluster wellheads and targets of the present invention.

[0027] Figure 4 A schematic diagram showing weight assignment of wellhead and target point matching of an exemplary embodiment of the integrated calculation method for efficient matching of cluster wellheads and targets of the present invention is shown.

[0028] Figure 5A A first wellhead matching target point diagram is shown of an exemplary embodiment of the method for efficiently matching cluster wellheads with targets according to the present invention; Figure 5B A matching diagram showing a second wellhead matching target point search augmented path of an exemplary embodiment of the integrated calculation method for efficient matching of cluster wellheads and targets of the present invention is shown; Figure 5C A matching diagram after weight modification is shown for an exemplary embodiment of the method for efficiently integrating and calculating the matching between cluster wellheads and target points of the present invention; Figure 5D A matching diagram showing a second wellhead being re-matched after weight modification according to an exemplary embodiment of the present invention based on an integrated calculation method for efficient matching of cluster wellheads and targets; Figure 5E A matching diagram after modifying the weight of the third target point of an exemplary embodiment of the method for efficient integrated matching of cluster wellheads and targets of the present invention is shown; Figure 5F A matching graph showing the search for augmented paths after weight modification of an exemplary embodiment of the method for efficient integrated calculation of cluster wellheads and target points of the present invention is shown; Figure 5G A matching diagram showing successful re-matching after modification of an exemplary embodiment of the method for efficient integrated calculation of cluster wellheads and targets of the present invention is shown.

[0029] Figure 6A A diagram showing a situation in which the intersection point of two straight lines with different slopes in a two-dimensional space is outside the wellhead in an exemplary embodiment of the integrated calculation method for efficient matching of cluster wellheads and target points of the present invention; Figure 6B A diagram showing the horizontal projection of the wellbore trajectory of the intersection point of two straight lines with different slopes in a two-dimensional space in an exemplary embodiment of the integrated calculation method for efficient matching of cluster wellheads and target points of the present invention; Figure 6C A diagram showing the horizontal projection of the intersection point of two straight lines with different slopes in a two-dimensional space outside the target point in an exemplary embodiment of the integrated calculation method for efficient matching of cluster wellheads and target points of the present invention.

[0030] Figure 7A 7B shows the initial graph of convex hull detection in an exemplary embodiment of the method for efficient integrated matching of cluster wellheads and targets of the present invention; 7C shows the convex hull detection C2 point pop / push judgment graph in an exemplary embodiment of the method for efficient integrated matching of cluster wellheads and targets of the present invention; 7D shows the convex hull detection C3 point pop / push judgment graph in an exemplary embodiment of the method for efficient integrated matching of cluster wellheads and targets of the present invention; 7E shows the convex hull detection C4 point pop / push judgment graph in an exemplary embodiment of the method for efficient integrated matching of cluster wellheads and targets of the present invention; 7F shows the final result graph of convex hull detection in an exemplary embodiment of the method for efficient integrated matching of cluster wellheads and targets of the present invention. DETAILED DESCRIPTION

[0031] Hereinafter, a method, system and device for efficient matching and integrated calculation of cluster wellheads and targets of the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.

[0032] Currently, existing drilling design software at home and abroad lacks a wellhead-target matching function. In the early stages of large cluster well and platform well design, wellhead and target matching requires manual adjustment. Platforms with a large number of wells are difficult to adjust and have low adjustment efficiency, which affects the efficiency of drilling design output. An efficient matching method is urgently needed to solve this problem.

[0033] The present invention integrates the bipartite graph KM algorithm and the convex hull detection method to form an efficient matching and integration calculation method for wellheads and target points. By continuously modifying the weights of wellheads and target points through the bipartite graph KM algorithm and continuously increasing the augmenting paths of the matching until the initial matching of wellheads and target points is completed. Through the judgment criterion of the intersection of plane lines, the connecting lines of intersecting wellheads and target areas are included in the scope of secondary matching. By performing convex hull detection on these intersection points, the edges of the distribution graph of these points are detected, and the edge line segments will surely not intersect the middle line segments. Through the backtracking-verification of convex hull detection until all intersection problems are solved, the final matching of large-scale cluster wells and platform wells is completed.

[0034] To achieve the above object, on the one hand, the present invention provides an efficient matching and integration calculation method for wellheads and target points of cluster wells. The efficient matching and integration calculation method for wellheads and target points includes the following steps.

[0035] Step S1: Calculate the horizontal displacement Di from each wellhead to each target point, and assign weights to the wellheads according to the distribution of the horizontal displacement.

[0036] Specifically, when Di≥2000m, the weight of the wellhead is 0.1; when 1800m≤Di<2000m, the weight of the wellhead is 0.2; when 1800m<Di≤1600m, the weight of the wellhead is 0.3; when 1600m<Di≤1400m, the weight of the wellhead is 0.4; when 1400m<Di≤1200m, the weight of the wellhead is 0.5; when 1200m<Di≤1000m, the weight of the wellhead is 0.6; when 1000m<Di≤800m, the weight of the wellhead is 0.7; when 800m<Di≤600m, the weight of the wellhead is 0.8.

[0037] Step S2: Taking the minimum horizontal displacement and the highest weight as the basis for the optimal matching, use the bipartite graph KM algorithm to complete the preliminary matching of wellheads and target points.

[0038] Specifically, step S2 may include the following sub-steps:

[0039] Sub-step S21: Construct the objective function and constraint conditions, select the horizontal displacement c as the objective function, and the objective function is as follows: <00..... 00114>

[0040]

[0041] where n is the number of wellheads and target points, i is the wellhead serial number, j is the target point serial number, and c ij is the horizontal displacement of the wellbore trajectory matching the i-th wellhead and the j-th target point (unit: m), and x ijis the matching result between the i-th wellhead and the j-th target point. The value is 1 if the match is successful, and 0 if the match is unsuccessful. One target point can only match one wellhead. If a target point is not matched, the value is 1, and if it is matched, the value is 0.

[0042] The constraints are as follows:

[0043]

[0044]

[0045] x ij =1 or 0, i, j = 1, 2, 3...n,

[0046] Among them, x ij is the matching result between the i-th wellhead and the j-th target point, where i is the wellhead number and j is the target point number.

[0047] Sub-step S22: assign a cap value to each element in {Xi} and {Yi} according to the size of Di, where {Xi} is the wellhead set on the left side of the bipartite graph and {Yi} is the target point set on the right side of the bipartite graph.

[0048] Sub-step S23: Starting from the first vertex A in {Xi}, traverse all vertices in {Yi} to determine whether a vertex B in {Yi} can be found that satisfies the following conditions: A and B are optimal matches, and point B has not been matched or rematched. If so, go to sub-step S25; if not, go to sub-step S24; the basis for the optimal match is the minimum horizontal displacement and the highest weight.

[0049] Sub-step S24: Under the condition that the total weight before and after the matching remains unchanged, modify the marked top mark value that does not meet the matching condition, and return to sub-step S23.

[0050] Specifically, the marked top mark value that does not meet the matching conditions is modified by subtracting the d value from the top mark value of the wellhead and adding the d value to the top mark value of the target point, where d = min{C x[i] +C y[j] -weight[i][j]}, d is the top scale correction coefficient, C x[i] is the top mark value of the i-th wellhead, C y[j] is the top mark value of the j-th target point, and weight[i][j] is the sum of the top mark value of the i-th wellhead and the top mark value of the j-th target point.

[0051] Sub-step S25: Mark the matched wellheads and targets, start from the next vertex in {Xi}, and return to sub-step S23 to match the next vertex in {Xi} with a point in {Yi} until all wellheads and targets are matched one by one.

[0052] Step S3: Connect the matched wellheads and targets with line segments, and determine whether there is a possibility of intersection between any two initially matched wellhead-target connecting lines, and the intersection point falls on the horizontal projection of the wellbore trajectory. If so, use the convex hull detection algorithm to perform secondary matching of wellheads and targets for the two intersecting lines. If not, complete the final matching of wellheads and targets.

[0053] Specifically, the specific steps to determine whether there is a possibility of intersection between any two initially matched wellhead-target connecting lines and the intersection point falls on the horizontal projection of the wellbore trajectory are as follows:

[0054] Determine the coordinates (X1, Y1) of each wellhead and the coordinates (X2, Y2) of each target; determine the linear equations of the two initially matched wellhead-target connecting lines, and determine the coordinates (x, y) of the intersection point P; if X1 < x < X2 or Y1 < y < Y2 is satisfied, it means that the intersection point P falls on the horizontal projection of the wellbore trajectory.

[0055] It should be noted that in the initial matching result, if all the line segments after connecting all the targets and wellheads are parallel, there will surely be no intersection situation, that is, the desired effect is obtained in the initial matching, and no secondary matching is required; although there are intersections between some target-wellhead connecting line segments, but X1 < x < X2 or Y1 < y < Y2 is not satisfied, this kind of intersection situation also does not require secondary matching.

[0056] Based on the above calculation process and calculation flow, the initial matching flow chart of the efficient matching and integration calculation method for cluster well wellheads and targets is as Figure 1 shown.

[0057] Specifically, the specific steps to perform secondary matching of wellheads and targets for the two intersecting lines using the convex hull detection algorithm are as follows:

[0058] a) Put all the vertices corresponding to the intersection point P falling on the horizontal projection of the wellbore trajectory into a two-dimensional rectangular coordinate system, and use the vertex C0 with the smallest ordinate as the coordinate reference point, where the vertices include wellheads and targets. <00001​​​c) Draw a directed line L based on the first two vertices C0 and C1 at the top of the stack, and determine the orientation relationship between the next vertex and the directed line L.

[0061] d) If the next vertex is to the right of the directed line L, pop the top element of the stack, select the next vertex as the new current point, and repeat step c.

[0062] e) If the next vertex is on the left side of the directed line L, or if the next vertex is on the directed line L, check whether the current vertex is the last vertex C2. If so, terminate the convex hull detection. If not, continue to select the next vertex as the new current point and repeat step c.

[0063] f) After the convex hull detection is completed, the line segments connecting the wellheads and the target points are eliminated, the line segments connecting the wellheads and the target points are matched, and the wellheads and target points inside the convex polygon are re-matched.

[0064] g) Determine whether the line connecting the re-matched wellhead and target point has the possibility of intersection, and whether the intersection point falls on the horizontal projection of the wellbore trajectory. If so, repeat steps a to f until all line segments are intersected.

[0065] Based on the above calculation process and calculation process, a secondary matching flow chart of the efficient matching integrated calculation method between cluster wellheads and targets is drawn as follows: Figure 2 shown.

[0066] At the same time, based on the calculation process and calculation process of the primary matching and secondary matching, a flow chart of the efficient matching integration calculation method between the wellhead and the target of the cluster well is drawn as follows: Figure 3 shown.

[0067] Another aspect of the present invention provides a highly efficient matching and integrated computing system for cluster wellheads and target points.

[0068] In an exemplary embodiment of the efficient matching and integrated computing system for cluster wellheads and targets of the present invention, the efficient matching and integrated computing system includes a basic parameter determination unit, a primary matching unit, a verification unit, and a secondary matching unit.

[0069] The basic parameter determination unit is configured to calculate the horizontal displacement Di from each wellhead to each target point, and assign a weight value to the wellhead according to the distribution of the horizontal displacement.

[0070] The initial matching unit is connected to the basic parameter determination unit and is configured to use the minimum horizontal displacement and the highest weight as the optimal matching basis, and use the bipartite graph KM algorithm to complete the preliminary matching between the wellhead and the target.

[0071] The verification unit is connected to the initial matching unit and is configured to determine whether any two preliminarily matched wellhead and target point lines have the possibility of intersecting, and whether the intersection point falls on the horizontal projection of the wellbore trajectory, and output the corresponding wellhead coordinates and target point coordinates when all intersection points fall on the horizontal projection of the wellbore trajectory.

[0072] The secondary matching unit is connected to the verification unit and is configured to use a convex hull detection algorithm to perform secondary matching between the wellhead and the target point for two intersecting straight lines.

[0073] In this embodiment, the initial matching unit may include a topmark assignment module, a traversal module, a topmark modification module, and a marking module.

[0074] Among them, the top label assignment module is configured to assign a top label value to each element in {Xi} and {Yi} according to the size of Di, where {Xi} is the wellhead set on the left side of the bipartite graph and {Yi} is the target point set on the right side of the bipartite graph.

[0075] The traversal module is connected to the top mark assignment module and is configured to start from each vertex in {Xi}, traverse all vertices in {Yi}, and determine whether a vertex in {Yi} can be found to simultaneously meet the following conditions: the two vertices are optimally matched, and the vertex in {Yi} has not been matched or has been rematched.

[0076] The top mark modification module is connected to the traversal module and is configured to modify the marked top mark value that does not meet the matching condition while keeping the total weight before and after the matching unchanged.

[0077] The marking module is connected to the traversal module and is configured to mark the matched wellheads and targets.

[0078] In this embodiment, the secondary matching unit may include a coordinate reference point determination module, an argument calculation module, a convex hull vertex primary determination module, an orientation judgment module, a convex hull vertex secondary determination module, a matching module, and a verification module.

[0079] The coordinate reference point determination module is configured to sort the wellheads and target points corresponding to all intersection points falling on the horizontal projection of the wellbore trajectory, and use the vertex C0 with the smallest ordinate as the coordinate reference point.

[0080] The argument calculation module is connected to the coordinate reference point determination module and is configured to calculate the argument α of each vertex relative to the vertex C0.

[0081] The convex hull vertex initial determination module is connected to the argument calculation module and is configured to sort the vertices in ascending order of argument and determine the first-order vertex C1 and the last-order vertex C2 as points on the convex hull.

[0082] The orientation judgment module is configured to draw a directed line L based on the first two vertices C0 and C1 at the top of the stack, and to judge the orientation relationship between the next vertex and the directed line L.

[0083] The convex hull vertex secondary determination module is connected to the orientation judgment module and is configured to determine other vertices on the convex hull according to the orientation relationship between the next vertex and the directed line L.

[0084] The matching module is connected to the convex hull vertex secondary determination module and is configured to eliminate line segments connecting wellheads and targets, match the line segments connecting wellheads and targets, and re-match the wellheads and targets inside the convex polygon.

[0085] The checking module is connected to the matching module and is configured to check whether there is a possibility that the lines connecting the wellhead and the target point that are re-matched have an intersection, and whether the intersection point falls on the horizontal projection of the wellbore trajectory.

[0086] According to the present invention, a cluster wellhead and target efficient matching and integration calculation method can be programmed as a computer program and the corresponding program code or instructions can be stored in a computer-readable storage medium. When the program code or instructions are executed by a processor, the processor executes the above-mentioned wellhead and target efficient matching and integration calculation method. The above-mentioned processor and memory can be included in a computer device.

[0087] According to another exemplary embodiment of the present invention, a computer-readable storage medium storing a computer program is provided. The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the computational method for efficient wellhead and target matching integration according to the present invention. The computer-readable recording medium is any data storage device that can store data read by a computer system. Examples of computer-readable recording media include read-only memory, random access memory, read-only optical discs, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data storage via the Internet via a wired or wireless transmission path).

[0088] According to yet another exemplary embodiment of the present invention, a computer device is provided. The computer device includes a processor and a memory. The memory is configured to store a computer program. The computer program is executed by the processor to cause the processor to perform the computational method for efficiently integrating wellhead and target matching according to the present invention.

[0089] In order to better understand the present invention, the content of the present invention is further explained below with reference to the accompanying drawings and examples, but the content of the present invention is not limited to the following embodiments.

[0090] Step 1: First, assign weights to the wellheads. Assume that a platform has n target points and n wellheads. According to the geological design, the coordinates of the wellhead targets are known. According to the distance calculation formula between two points, the distance from each wellhead to each target is calculated. According to the size of the horizontal displacement, the distances are sorted from large to small. There are a total of C n n Item. The design trajectory profile follows the principle of minimum vertical depth and minimum horizontal displacement in the two-dimensional profile. The line connecting the wellhead and the target in the two-dimensional plane is the horizontal displacement. For each wellhead, the target with the shortest line length is selected, and the targets are uniformly assigned a weight of 0 based on the lengths shown in Table 1. Table 1 is only an example and does not limit the size of the assigned weights. In specific cases, the wellheads should be weighted according to the actual distribution of horizontal displacement. The size of the weight here only indicates the priority of the match. The larger the weight, the higher the match priority.

[0091] Table 1 Relationship between horizontal displacement length and weight

[0092]

[0093] Step 2: Complete the preliminary matching between the wellhead and the target using the bipartite graph KM algorithm. Figure 1 The flowchart of the KM algorithm's preliminary matching method between wellhead and target is shown.

[0094] like Figure 4 Figure 2 shows a schematic diagram of the weighted assignment for wellhead and target matching using the bipartite graph KM algorithm. In the figure, A represents the wellhead set on the left; B represents the target set on the right. The numbers 0.6, 0.7, 0.8, and 0.9 on the line connecting the two sets represent the weights assigned to the wellheads based on their horizontal displacements. The largest edge is selected to perform a one-to-one matching between the wellhead and the target. It should be noted that the content in the illustration is a simplified bipartite graph and does not represent the actual positional relationship between the wellhead and the target.

[0095] The specific details of the wellhead and target matching include the following sub-steps: Figure 5A 、 Figure 5B 、 Figure 5C 、 Figure 5D 、 Figure 5E 、 Figure 5F 、 Figure 5G As shown, it should be noted that in the above figure, A represents the wellhead set, where 1, 2, and 3 represent different wellheads in the wellhead set; B represents the target point set, where A, B, and C represent different targets in the target point set; the numbers to the left of different wellheads in the wellhead set represent the initial value assigned to each wellhead according to the magnitude of the horizontal displacement; the numbers to the right of different targets in the target point set represent the top mark value; the numbers on the line connecting the wellhead and target point sets represent the weights;

[0096] (a) For the first wellhead matching target, select the one with the highest weight first, and the matching result is 1-c, and mark it. Figure 5A shown.

[0097] (b) Match the target point for the second wellhead. During the matching process, it is found that C and 1 have been matched. Find the augmenting paths 2-A, 2-B, 2-C-1-A. The matching situation is as follows Figure 5B shown.

[0098] By calculation, The minimum value is 0.1, and the weights of 1, 2, and c are modified. According to the rule for modifying weights, the wellhead vertex is -0.1, and the target vertex is +0.1. The values ​​after modifying the weights are as follows: Figure 5C shown.

[0099] (c) After modifying the weights, rematch the second wellhead, following C x[i] +C y[j] =weight[i][j]} matches the second wellhead with target A. The matching situation is as follows Figure 5D shown.

[0100] (d) Match the target to the third wellhead. During the matching process, it is found that C and 1 have already matched. The weight of the third target is modified (minus 0.1). Figure 5E shown.

[0101] (e) After modifying the weights, search for the augmented path 3-C-2-A, and C has matched with target 1. The specific matching situation is as follows Figure 5F Modify the weights of wellheads 1, 2, 3 and targets A and C, the wellhead weight is -0.1, the target weight is +0.1, and after modification, rematch: 1-A, 2-B, 3-C, matching is completed, and the matching situation is as follows Figure 5G shown.

[0102] Step 3: The result of the initial matching is passed through the criterion of intersection of two straight lines in the plane to determine whether the line connecting the wellhead and the target point intersects.

[0103] Assume that there are two straight lines in a two-dimensional plane, and the equations of the lines are as follows:

[0104] y1=a1x+b1y

[0105] y2=a2x+b2y

[0106] For two straight lines with different slopes, there must be an intersection point. The coordinates of the intersection point P obtained by solving the equations of the lines are as follows:

[0107]

[0108]

[0109] Where a and b represent coefficients; y1 and y2 are two straight lines on a two-dimensional plane, (a2, b2) are the coordinates of a point on the line y1; and (a2, b2) are the coordinates of a point on the line y2.

[0110] Based on the coordinates of the wellhead segment's starting point (X1, Y1), the target segment's ending point (X2, Y2), and the intersection point P's coordinates (x, y), determine whether the point falls within the segment. The determination is: X1 < x < X2 or Y1 < y < Y2. If this condition holds, point P falls within the segment.

[0111] For two straight lines with different slopes, Figure 6A 、 Figure 6B as well as Figure 6C The three intersection situations shown need to be explained. Figure 6A 、 Figure 6B as well as Figure 6C A represents the wellhead, where A1 and A2 represent two different wellheads; B represents the target point, where B1 and B2 represent two different target points. Figure 6A shows that the intersection point is outside the wellhead; Figure 6B Indicates that the intersection point is on the horizontal projection of the wellbore trajectory; Figure 6C Indicates that the intersection point is outside the target point.

[0112] Step 4: Filter out intersecting line segments and include them in the second matching process. Use the convex hull detection method to detect the edges of the two-dimensional graph composed of intersecting line segments. Repeat the convex hull detection method until all intersections are resolved, completing the final match between the wellhead and the target.

[0113] Figure 7A The figure shows the vertices of the line segments with intersecting points P that are screened out after the initial matching, including eight points C0 to C7.

[0114] Specifically, the convex hull detection steps include:

[0115] ① Place the vertices of the line segment on the line connecting the wellhead and the target point at the intersection point P into a two-dimensional rectangular coordinate system. The minimum point of the ordinate is the point on the convex hull, and the minimum point C0 is used as the coordinate reference point.

[0116] ②. Calculate the argument α of each point relative to C0, and sort the points in ascending order. When the argument α is the same, the point closer to C0 is placed in front. According to the definition of the convex hull detection algorithm, the first point C1 and the last point C7 must be points on the convex hull. The minimum point C0 and the last point constitute the vertices of the polygon, that is, the minimum point C0 and the last point C7 have been pushed into the stack. The stacking situation is as follows: Figure 7B As shown;

[0117] ③. Draw a directed line based on the first two points C0 and C1 on the top of the stack. Use the directed line as the Y axis and determine whether the next point is on the right or left of the directed line. If it is on the right side of the line, execute sub-step ④. If it is on the line or on the left side of the line, execute sub-step ⑤. Figure 7C A schematic diagram showing a directed straight line passing through points C0 and C1 to determine whether point C2 is a stack point;

[0118] ④. If the top element of the stack, that is, the newest point, is not a point on the convex hull, pop the top element out of the stack and use the next point as the new current point, and return to sub-step ③. Figure 7D It shows that a directed line segment 1P2 is drawn, pointing to C1, C3 is on the right side of the directed line segment C1C2, C2 is popped out of the stack, and C3 is pushed into the stack;

[0119] ⑤. Determine whether the current point is the last element in sub-step ③. If so, end the program. If not, set the next point as the new current point and return to sub-step ③. Figure 7E It shows that a directed line segment C1C3 is drawn, pointing to C1, and the next vertex C4 is judged to be on the left or right side of the directed line segment C1C3. C4 is on the left side of the C1C3 line segment, and C4 is pushed into the stack;

[0120] ⑥ Eliminate the elements that do not meet the requirements of matching wellheads and targets, that is, the line segments connecting wellheads and targets, match the line segments connecting wellheads and targets, and re-match the wellheads and targets inside the convex polygon. At this time, the internal line segments will not intersect with the external line segments. Figure 7F The figure shows the final result of the convex hull detection. Through the above judgment, the stack points are finally determined to be C0, C1, C3, C6, and C7, forming a convex polygon.

[0121] ⑦. Return the filtered results to step 3 to determine whether there is any line segment intersection. If there is still a line segment intersection, repeat the above steps and iterate continuously until all line segment intersection problems are solved.

[0122] Although the present invention has been described above with reference to the exemplary embodiments and the accompanying drawings, it will be apparent to those skilled in the art that various modifications may be made to the above embodiments without departing from the spirit and scope of the claims.

Claims

1. A highly efficient integrated calculation method for matching cluster wellheads with target points, characterized in that: The efficient matching and integration calculation method includes the following steps: S1. Calculate the horizontal displacement Di from each wellhead to each target point, and assign weight values to the wellheads according to the distribution of the horizontal displacements; S2. Using the minimum horizontal displacement and the highest weight value as the basis for the optimal matching, complete the preliminary matching of the wellheads and the target points by using the bipartite graph KM algorithm; S3. Connect the matched wellheads and target points with line segments, and judge whether there is a possibility of intersection between any two lines connecting the preliminarily matched wellheads and target points, and the intersection point falls on the horizontal projection of the wellbore trajectory. If so, use the convex hull detection algorithm to perform secondary matching of the wellheads and target points for the two intersecting lines. If not, complete the final matching of the wellheads and target points; Di When the wellhead is ≥2000m, the wellhead weight value is 0.1; when the wellhead is ≤1800m, the wellhead weight value is 0.

1. Di When the wellhead is less than 2000m, the wellhead weighting value is 0.2; when the wellhead is less than 1800m, the wellhead weighting value is 0.2; Di When the wellhead is less than 1600m, the wellhead weight value is 0.3; when the wellhead is less than 1600m, the wellhead weight value is 0.3; Di When the wellhead is less than 1400m, the wellhead weight value is 0.4; when the wellhead is less than 1400m, the wellhead weight value is 0.4; Di When the wellhead is less than 1200m, the wellhead weight value is 0.5; when the wellhead is less than 1200m, the wellhead weight value is 0.5; Di When the wellhead is less than 1000m, the wellhead weight is 0.6; when the wellhead is less than 1000m, the wellhead weight is 0.6; Di When the wellhead is less than or equal to 800m, the wellhead weight is 0.7; when the wellhead is less than or equal to 800m, the wellhead weight is 0.7; Di When the depth is ≤600m, the wellhead weight is 0.8; Step S2 includes the following sub-steps: S21. According to the magnitude of Di, assign top label values to each element in {Xi} and {Yi}, where {Xi} is the set of wellheads on the left side of the bipartite graph, and {Yi} is the set of target points on the right side of the bipartite graph; S22. Starting from the first vertex A in {Xi}, traverse all vertices in {Yi}, and judge whether a certain vertex B in {Yi} can be found to satisfy both: A and B are the optimal matching, and B point has not been matched or rematched. If so, go to sub-step S24. If not, go to sub-step S23. The basis for the optimal matching is the minimum horizontal displacement and the highest weight value; S23. Under the condition of keeping the total weight value unchanged before and after matching, modify the marked top label values that do not meet the matching conditions, and return to sub-step S22; S24. Mark the matched wellheads and target points, and starting from the next vertex in {Xi}, return to sub-step S22 to perform the matching of the next vertex in {Xi} and a certain point in {Yi} until all wellheads and target points are matched one by one; In sub-step S23, the top mark value of the wellhead is subtracted d Value, target marked top mark value plus d Modify the marked top mark value that does not meet the matching conditions in a value-based way, where: , d is the top mark correction coefficient, C x[i] is the top mark value of the i-th wellhead, C y[j] is the top mark value of the j-th target, weight[ i ][ j ] is the sum of the top mark value of the i-th wellhead and the top mark value of the j-th target point.

2. The efficient integrated calculation method for matching cluster wellheads with target points according to claim 1 is characterized in that: The specific steps for judging whether there is a possibility of intersection between any two lines connecting the preliminarily matched wellheads and target points, and the intersection point falls on the horizontal projection of the wellbore trajectory are as follows: Determine the coordinates (X1, Y1) of each wellhead and the coordinates (X2, Y2) of each target point; Determine the straight line equations of the two lines connecting the preliminarily matched wellheads and target points, and determine the coordinates (x, y) of the intersection point P; If X1 < x < X2 or Y1 < y < Y2 is satisfied, it means that the intersection point P falls on the horizontal projection of the wellbore trajectory.

3. The efficient integrated calculation method for matching cluster wellheads with target points according to claim 2 is characterized in that: The specific steps for using the convex hull detection algorithm to perform secondary matching of the wellheads and target points for the two intersecting lines are as follows: a) Put all the vertices corresponding to the intersection points P falling on the horizontal projection of the wellbore trajectory into a two-dimensional rectangular coordinate system, and use the vertex C0 with the smallest ordinate as the coordinate reference point, where the vertices include wellheads and target points; b) Calculate the azimuth angle α of each vertex relative to the vertex C0, sort all the vertices in ascending order of the azimuth angle, and determine the points C1 and C2 with the first and last sorting as the points on the convex hull; c) Make a directed line L according to the first two vertices C0 and C1 at the top of the stack, and judge the azimuth relationship between the next vertex and the directed line L; d) If the next vertex is on the right side of the directed line L, pop the top element of the stack, select the next vertex as the new current point, and repeat step c; e) If the next vertex is on the left side of the directed line L, or the next vertex is on the directed line L, check whether the current vertex is the last vertex C2. If so, terminate the convex hull detection. If not, continue to select the next vertex as the new current point and repeat step c. f) After the convex hull detection is completed, the line segments connecting the wellheads and the target points are eliminated, the line segments connecting the wellheads and the target points are matched, and the wellheads and target points inside the convex polygon are re-matched; g) Determine whether the line connecting the re-matched wellhead and target point has the possibility of intersecting, and whether the intersection point falls on the horizontal projection of the wellbore trajectory. If so, repeat steps a to f until all line segments are intersected.

4. A cluster wellhead and target efficient matching integrated computing system, characterized by: The efficient matching integrated computing system includes a basic parameter determination unit, a primary matching unit, a verification unit and a secondary matching unit, wherein: The basic parameter determination unit is configured to calculate the horizontal displacement Di from each wellhead to each target point and assign weight values ​​to the wellheads according to the distribution of the horizontal displacement; The initial matching unit is connected to the basic parameter determination unit and is configured to use the minimum horizontal displacement and the highest weight as the optimal matching basis, and use the bipartite graph KM algorithm to complete the preliminary matching between the wellhead and the target; The verification unit is connected to the initial matching unit and is configured to determine whether any two preliminarily matched lines connecting the wellhead and the target point have the possibility of intersecting, and whether the intersection point falls on the horizontal projection of the wellbore trajectory, and output the corresponding wellhead coordinates and target point coordinates when all intersection points fall on the horizontal projection of the wellbore trajectory; The secondary matching unit is connected to the verification unit and is configured to use a convex hull detection algorithm to perform secondary matching between the wellhead and the target point for two intersecting straight lines.

5. The cluster wellhead and target efficient matching integrated computing system according to claim 4 is characterized in that: The initial matching unit includes a top mark assignment module, a traversal module, a top mark modification module and a marking module, wherein: The top mark assignment module is configured to assign a top mark value to each element in {Xi} and {Yi} according to the size of Di, where {Xi} is the wellhead set on the left side of the bipartite graph and {Yi} is the target point set on the right side of the bipartite graph; The traversal module is connected to the top-mark assignment module and is configured to start from each vertex in {Xi} and traverse all vertices in {Yi} to determine whether a vertex in {Yi} can be found that satisfies both conditions: the two vertices are optimally matched, and the vertex in {Yi} has not been matched or has been rematched. The top mark modification module is connected to the traversal module and is configured to modify the marked top mark value that does not meet the matching condition while keeping the total weight before and after the matching unchanged; The marking module is connected to the traversal module and is configured to mark the matched wellheads and targets.

6. The cluster wellhead and target efficient matching integrated computing system according to claim 4 is characterized in that: The secondary matching unit includes a coordinate reference point determination module, an argument calculation module, a convex hull vertex primary determination module, an orientation judgment module, a convex hull vertex secondary determination module, a matching module and a verification module, wherein: The coordinate reference point determination module is configured to sort the wellheads and target points corresponding to all intersection points falling on the horizontal projection of the wellbore trajectory, and use the vertex C0 with the smallest ordinate as the coordinate reference point; The argument calculation module is connected to the coordinate reference point determination module and is configured to calculate the argument of each vertex relative to vertex C0. α ; The convex hull vertex initial determination module is connected to the argument calculation module and is configured to sort the vertices in ascending order of argument, and determine the first-order vertex C1 and the last-order vertex C2 as points on the convex hull; The orientation determination module is configured to draw a directed line L based on the first two vertices C0 and C1 at the top of the stack, and determine the orientation relationship between the next vertex and the directed line L; The convex hull vertex secondary determination module is connected to the orientation judgment module and is configured to determine other vertices on the convex hull based on the orientation relationship between the next vertex and the directed line L; The matching module is connected to the convex hull vertex secondary determination module and is configured to eliminate line segments connecting wellheads and targets, match the line segments connecting wellheads and targets, and re-match the wellheads and targets inside the convex polygon; The checking module is connected to the matching module and is configured to check whether there is a possibility that the lines connecting the wellhead and the target point that are re-matched have an intersection, and whether the intersection point falls on the horizontal projection of the wellbore trajectory.

7. A computer device, characterized in that: The device comprises: processor; The memory stores a computer program, and when the computer program is executed by the processor, the efficient wellhead and target point matching and integrated calculation method according to any one of claims 1 to 3 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the wellhead and target point efficient matching and integration calculation method according to any one of claims 1 to 3 is implemented.

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