Dynamic planning method for oil field dispatching route

Abstract

The invention relates to an oil field dispatching route dynamic planning method, and belongs to the field of computer technology and spatial information technology. A transaction corresponding to a target point of a to-be-processed event collected in real time and a set starting point serve as a data set, a planning parameter value between any two points is obtained through calculation according to the distance between two points in the data set and a priority coefficient value of the corresponding target point, and then an adjacent matrix is obtained; determining a target point with the minimum planning parameter value of each target point of the starting point in the data set based on the adjacency matrix, taking the target point as a new starting point, obtaining the target points with the minimum planning parameter values of the current new starting point and the remaining target points based on the adjacency matrix, and so on until no remaining target points exist, all the target points are connected in series according to the sequence of the target points selected as the starting points, the path after series connection is used as the optimal path planning result, and the method can meet actual requirements and is high in applicability.

Description

Technical Field

[0001] The invention relates to a dynamic planning method for oilfield dispatching routes, and belongs to the field of computer technology and spatial information technology. Background Art

[0002] The oilfield production process includes various types of work tasks, such as daily inspections, production anomaly verification, emergency response, etc. The target locations of various tasks are scattered and almost impossible to follow the same path. In addition, the operation sequence of each task is different. Temporary work or emergencies often occur. Therefore, when dispatching personnel, materials, and vehicles and planning routes, relying solely on manpower to allocate and plan routes will lead to low efficiency in work task allocation, high labor costs, and poor dispatching effects.

[0003] In recent years, with the development of information technology, path planning algorithms have been gradually applied across various industries. Common path planning algorithms include the Dijkstra algorithm and the A* algorithm (heuristic search algorithm). These existing path planning algorithms typically only consider path length when applied. Tasks with shorter distances are executed first, followed by tasks with longer distances. Ultimately, the path with the shortest sum of all path distances is considered the optimal path. In oilfield production, pending tasks corresponding to target locations in different directions have different urgency (i.e., priority). For example, tasks with shorter distances have lower priority and require less urgent processing, while tasks with longer distances have higher priority and require more urgent processing. Existing methods fail to meet this requirement. While existing path planning methods can improve efficiency to a certain extent, they fail to meet the practical and reasonable requirements of actual oilfield production tasks, making these path planning methods that do not consider task urgency unsuitable. Summary of the Invention

[0004] The purpose of the present invention is to provide a dynamic planning method for oilfield dispatch routes, so as to solve the problem that the existing route planning method which only considers the shortest distance does not meet the actual business needs and has poor applicability.

[0005] To achieve the above object, the solution of the present invention includes:

[0006] A method for dynamically planning an oilfield dispatch route according to the present invention comprises the following steps:

[0007] The transactions corresponding to the target points of the events to be processed collected in real time and the set starting points are used as a data set. The planning parameter values between any two points are calculated based on the distance between the two points in the data set and the priority coefficient values of the corresponding target points, thereby obtaining an adjacency matrix. The adjacency matrix includes the planning parameter values between the starting point and the target point and between any two target points. The priority coefficient values are used to determine the priority order of the events to be processed.

[0008] Based on the adjacency matrix, determine the target point with the smallest planning parameter value compared to the target points at the starting point in the data set, and use this target point as the new starting point. Based on the adjacency matrix, obtain the target point with the smallest planning parameter value compared to the current new starting point and the remaining target points. This process is repeated until there are no remaining target points. All target points are connected in series according to the order of the target points selected as the starting point, and the concatenated path is used as the optimal path planning result.

[0009] Furthermore, the planning parameter values are:

[0010]

[0011] Where, is the planning parameter value from the xth target point to the yth target point, θ is the distance weight value, δ is the priority weight value, is the distance from the x-th target point to the y-th target point, L y is the priority coefficient value of the y-th target point.

[0012] Furthermore, the calculation formula of the priority coefficient value is:

[0013] L y =α*w+β*e+γ*p

[0014] Where, L y is the priority coefficient value of the yth target point, α is the weight value of the event type ranking, β is the weight value of the event corresponding time limit ranking, γ is the weight value of the abnormal percentage ranking, w is the ranking of the target location n in the event type ranking, e is the ranking of the target location n corresponding to the time limit ranking, and p is the ranking of the target location n corresponding to the abnormal percentage ranking.

[0015] Furthermore, the adjacency matrix is:

[0016]

[0017] Where, are the planning parameter values of any two target points.

[0018] Furthermore, when connecting the target points in series, the paths between adjacent target points are determined using the Dijkstra algorithm model.

[0019] Furthermore, the method also includes visually displaying the optimal path planning results through geographic coordinates.

[0020] Furthermore, the weight values of the event type ranking, the weight values of the event corresponding time limit ranking, and the weight values of the abnormal percentage ranking are all determined through the hierarchical analysis method.

[0021] Furthermore, the distance weight value and the priority weight value are obtained through the hierarchical analysis method.

[0022] Beneficial effect: The oilfield dispatch route dynamic planning method of the present invention is a further improvement based on the existing path planning method that only considers the shortest path distance between target points. By constructing the priority coefficient value of each event, the planning parameter values of the two target points and the starting point to the target point are calculated by the priority coefficient value and the distance between each target location, and then the adjacency matrix of the planning parameters is obtained according to the planning parameter values. Then, based on the adjacency matrix, the target points are continuously selected in a manner that minimizes the planning parameter values until all the target points are selected. All the target points are connected in series in the order of selection, and the path after series connection is the optimal path planning result. This method not only takes into account the distance of the target points, but also takes into account the event priority. It takes the event priority as one of the conditions for route planning, can meet actual business needs, and improve the applicability of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 2. It is a functional structural connection diagram of the oilfield dispatch route dynamic planning method in the method embodiment of the present invention;

[0024] Figure 2 is a flowchart of a method for dynamic planning of oilfield dispatch routes in a method embodiment of the present invention;

[0025] Figure 3 It is a processing structure diagram of the data preprocessing module in the oil field dispatch route planning algorithm model in the method embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below with reference to the accompanying drawings.

[0027] Example of a dynamic planning method for oil field dispatch routes:

[0028] like Figure 1 、 Figure 2The method for dynamic planning of oilfield dispatch routes shown in the figure uses the transactions corresponding to the target points of the events to be processed collected in real time and the set starting points as data sets, calculates the planning parameter values between any two points based on the distance between the two points in the data set and the priority coefficient values of the corresponding target points, and then obtains the adjacency matrix of all planning parameter values, and then determines the target point with the smallest planning parameter value relative to each target point at the starting point in the data set based on the adjacency matrix, uses this target point as the new starting point, obtains the target point with the smallest planning parameter value relative to the current new starting point and each remaining target point based on the adjacency matrix, and so on, until there are no remaining target points, and then connects all target points in series according to the order of selecting the target points as the starting point, and the concatenated path is used as the optimal path planning result. The specific implementation is as follows:

[0029] The data set is composed of the transactions corresponding to the target points of the pending events collected in real time, as well as the set starting points. The planning parameter values between any two points are calculated based on the distance between the two points in the data set and the priority coefficient values of the corresponding target points, thereby generating an adjacency matrix. The adjacency matrix includes the planning parameter values between the location and the target point, as well as between any two target points. The priority coefficient value is used to determine the priority order of the pending events, and is determined based on the event type level, the corresponding time limit of the event, and the percentage of abnormal values.

[0030] Specific as Figure 3 As shown, the priority coefficient value L y It can be constructed by linear weighting method. As other implementation methods, other methods can be used to construct the priority coefficient value according to actual conditions. y The calculation formula is:

[0031] L y =α*w+β*e+γ*p

[0032] Where α, β, and γ represent the weights of the event type ranking, time limit ranking, and abnormal percentage ranking, respectively. The corresponding weight sequences of the three can be obtained through the analytic hierarchy process (AHP), and α, β, and γ are all greater than 0 and less than 1, and the sum of the three is 1 (for example, [0.4, 0.27, 0.33]); w, e, and p represent the ranking of the nth target point in the event type ranking, time limit ranking, and abnormal percentage ranking, respectively.

[0033] The planning parameter value S between each target point is:

[0034]

[0035] In the formula, m is the total number of target points, m0 is the starting point, represents the planning parameter values from the xth target point to the yth target point in the default order. The default order is the input order of the training set data to the model. θ and δ represent the weight values of distance and priority, respectively, which can be determined by the analytic hierarchy process (AHP). Both θ and δ are between (0, 1) and their sum is 1 (for example, the weight sequence of θ and δ is [0.2, 0.8]); L y They represent the distance from the x target point to the y target point and the priority coefficient value of the y target point respectively.

[0036] Through the planning parameter value S between each target point, the planning parameter adjacency matrix of the data set is obtained:

[0037]

[0038] Based on the adjacency matrix, determine the target point with the smallest planning parameter value compared to the target points at the starting point in the data set, and use this target point as the new starting point. Based on the adjacency matrix, obtain the target point with the smallest planning parameter value compared to the current new starting point and the remaining target points. This process is repeated until there are no remaining target points. All target points are connected in series according to the order of the target points selected as the starting point, and the concatenated path is used as the optimal path planning result.

[0039] Among them, when connecting each target point in series, the path between adjacent target points is determined by using the dispatch route planning model.

[0040] This embodiment constructs a dispatch route planning model for oil field production, that is, a dispatch route planning model constructed by the Dijkstra algorithm, which is based on the existing Dijkstra algorithm. However, before obtaining the optimal path, the input target point sequence needs to be deduced and calculated to obtain the optimal target point sequence.

[0041] Specifically, the first subpath with the minimum planning parameter value in the path connecting the starting point and multiple target points is obtained. The end point of the first subpath is used as the next starting point, and the second subpath with the minimum planning parameter value is obtained based on the adjacency matrix, until the path with the minimum sum of planning parameter values in the path connecting all target points is obtained. Specifically, the planning model each time selects the point with the minimum planning parameter value between the current starting point and the point set for which the minimum planning parameter value has not been obtained, and uses this point as a bridge to refresh the planning parameter values between the point sets for which the minimum planning parameter value has not been obtained.

[0042] Let result = {the set of vertices for which the minimum path has been found}, notFound = {the set of vertices for which the minimum path has not been found}, where the notFound point set is obtained based on the adjacency matrix. At the initial point, only the starting point m0 is known, so the current result = {m0(0)}, and the set of vertices for which the path has not been found includes points other than the starting point m0, that is, notFound = (The data in parentheses are planning parameter values from the starting point to the target point and between two target points).

[0043] If the point m with the minimum planning parameter value is taken from the notFound point set x (Assume that the point m x non-m1, m2, m m ),at this time and That is, the minimum planning parameter When the vertex set that has not found the minimum path needs to be calculated based on point m x To update the bridge, the minimum planning parameter value needs to be added from the starting point to point m. x Planning parameter values;

[0044] Based on the adjacency matrix, the planning parameter values corresponding to the remaining target points in the notFound point set are updated, and the point m with the minimum planning parameter value is taken from the updated notFound point set again. i ,at this time and

[0045] By analogy, the target point is continuously selected from the notFound point set until notFound = {} is satisfied. (Assuming m t is the second to last point, m y The resulting point set is the output of the dispatch route planning model. The optimal route planning output is visualized using geographic coordinates, for example, by combining bound geographic coordinates with applications such as GIS maps.

[0046] The transactions corresponding to the target points of the pending events in the dataset are obtained in real time. First, the input is collected for each work transaction type information, and a set of work transaction types, a daily work transaction set, is constructed, as shown in the work transaction type table in Table 1 and the daily work transaction set table in Table 2. Each daily work transaction corresponds to a unique transaction type, that is, the real-time collected daily work transaction information is bound to the transaction type in Table 1.

[0047] Table 1: Work transaction type set

[0048]

[0049]

[0050] Table 2: Daily work tasks

[0051]

[0052] To verify the effectiveness of this method, this embodiment uses the events shown in Table 2 as an example to specifically illustrate this method. After calculation, the priority coefficient value of each event in the current data set is obtained, as shown in Table 3.

[0053] Table 3: Transaction set with priority coefficients

[0054]

[0055] The planning parameter values are calculated based on the distances between the target points corresponding to the current events, and then the planning parameter adjacency matrix of the current data set is obtained:

[0056] {{0, 4.0112, 1.3304, 2.9728, 2.0472}

[0057] {1.4272, 0, 2.6136, 3.7248, 3.2952}

[0058] {0.3104, 4.1776, 0, 3.2608, 2.3736}

[0059] {0.7968, 4.1328, 2.1048, 0, 1.8232}

[0060] {1.0272, 4.8592, 2.3096, 2.9792, 0}}

[0061] Then, after the deduction and calculation of the dispatch route planning model: at the starting point, result = {m0(0)}, and notFound = {m1(4.0112), m2(1.3304), m3(2.9728), m4(2.0472)}, and then the point m2 with the minimum planning parameter value is taken out from the [point where the minimum planning parameter value is not obtained] notFound point set, and then the notFound point set is updated with m2 as the new starting point, and the distance between the new starting point and the target point in the notFound point set is calculated. The planning parameter values between, at this time result = {m0(0), m2(1.3304)}, notFound = {m1(5.508), m3(4.5912), m4(3.704)}; and so on, to notFound = {}, result = {m0(0), m2(1.3304), m4(3.704), m3(7.5704), m1(14.5)}, and at this time the result point set is the optimal path planning result output by the dispatch route planning model.

[0062] According to the optimal path planning results, the route planning is obtained: starting point → M1 transfer station → Y31 well → W12 well → X1 metering station, and the bound geographic coordinates are combined with GIS maps and other applications for visualization output.

[0063] like Figure 1 As shown, the functional structure of the above-mentioned method is divided into a data acquisition module, an oilfield production dispatch route planning model, and a result feedback module. Specifically, this embodiment is based on the oilfield production dispatch route planning model and is preceded by a data preprocessing module. The data acquisition module collects and updates the dynamic data in the current group data set in real time, and synchronously transmits it to the oilfield production dispatch route planning algorithm model. Each time the oilfield production dispatch route planning algorithm model detects a data input, it performs a deduction calculation on the latest input current group data set to obtain the latest real-time optimal route planning result, and synchronously transmits it to the result feedback module. Each time the result feedback module detects a result data input, it refreshes the application interface to achieve real-time updates of the latest display effect.

[0064] The above implementation method further improves existing path planning methods that only consider the shortest path distance between target points. By constructing a priority coefficient value for each event, the planning parameter values for the two target points and the distance between the starting point and the target point are calculated using the priority coefficient value and the distance between each target point. The adjacency matrix of the planning parameters is then obtained based on the planning parameter values. Based on the adjacency matrix, target points are continuously selected in a manner that minimizes the planning parameter value until all target points are selected. All target points are then connected in series in the order of selection. The resulting path is the optimal path planning result. This method not only considers the distance between target points but also the event priority, using event priority as a condition for route planning. It can meet practical business needs and has high applicability in daily oilfield work. It also comprehensively considers various influencing factors for deduction and calculation, and with visual output, it greatly reduces analysis time. It is simple for users to operate and easy to apply in practical work, with strong practicality.

[0065] While specific embodiments have been described above, the present invention is not limited to the described embodiments. The fundamental concept of the present invention lies in the aforementioned basic scheme. Based on the teachings of the present invention, those skilled in the art can devise various variations of models, formulas, and parameters without inventive effort. Changes, modifications, substitutions, and variations to the embodiments without departing from the principles and spirit of the present invention remain within the scope of protection of the present invention.

Claims

1. A method for dynamic planning of oilfield dispatch routes, characterized in that: The following steps are involved: The transactions corresponding to the target points of the events to be processed collected in real time and the set starting points are used as a data set. The planning parameter values between any two points are calculated based on the distance between the two points in the data set and the priority coefficient values of the corresponding target points, thereby obtaining an adjacency matrix. The adjacency matrix includes the planning parameter values between the starting point and the target point and between any two target points. The priority coefficient values are used to determine the priority order of the events to be processed. Based on the adjacency matrix, determine the target point with the smallest planning parameter value compared to the target points at the starting point in the data set, and use this target point as the new starting point. Based on the adjacency matrix, obtain the target point with the smallest planning parameter value compared to the current new starting point and the remaining target points. This process is repeated until there are no remaining target points. All target points are connected in series according to the order of the target points selected as the starting point, and the concatenated path is used as the optimal path planning result.

2. The oilfield dispatch route dynamic planning method according to claim 1, characterized in that: The planning parameter values are: Where, is the planning parameter value from the xth target point to the yth target point, θ is the distance weight value, δ is the priority weight value, is the distance from the x-th target point to the y-th target point, L y is the priority coefficient value of the y-th target point.

3. The oilfield dispatch route dynamic planning method according to claim 1 or 2, characterized in that: The priority coefficient value is calculated as follows: L y =α*w+β*e+γ*p Where, L y is the priority coefficient value of the yth target point, α is the weight value of the event type ranking, β is the weight value of the event corresponding time limit ranking, γ is the weight value of the abnormal percentage ranking, w is the ranking of the target location n in the event type ranking, e is the ranking of the target location n corresponding to the time limit ranking, and p is the ranking of the target location n corresponding to the abnormal percentage ranking.

4. The oilfield dispatch route dynamic planning method according to claim 1, characterized in that: The adjacency matrix is: …… Where, … , ...are the planning parameter values of any two target points.

5. The oilfield dispatch route dynamic planning method according to claim 1, characterized in that: When connecting the target points in series, the paths between adjacent target points are determined using the Dijkstra algorithm model.

6. The oilfield dispatch route dynamic planning method according to claim 1 or 5, characterized in that: The method also includes visually displaying the optimal path planning results through geographic coordinates.

7. The oilfield dispatch route dynamic planning method according to claim 3, characterized in that: The weight values for the ranking of event types, the weight values for the ranking of event corresponding time limits, and the weight values for the ranking of percentages exceeding normal values are all determined through the hierarchical analysis method.

8. The oilfield dispatch route dynamic planning method according to claim 2, characterized in that: The distance weight value and priority weight value are obtained through the hierarchical analysis method.

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