Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for non-pipelined wells
The marginal wells in low-permeability oil fields are characterized by small storage size, scattered distribution, intermittent production, etc. The construction of large-scale gathering pipelines has large investment. So the current production mode is featured by single well tank oil storage, oil t...
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024173380 |
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| author | Qiushi Li Yuze Li Haitong Sun Wei Song Honghong Li Xiaoyong Gao Chaodong Tan Yaoyun Liu Hongbing Liu |
| author_facet | Qiushi Li Yuze Li Haitong Sun Wei Song Honghong Li Xiaoyong Gao Chaodong Tan Yaoyun Liu Hongbing Liu |
| author_sort | Qiushi Li |
| collection | DOAJ |
| description | The marginal wells in low-permeability oil fields are characterized by small storage size, scattered distribution, intermittent production, etc. The construction of large-scale gathering pipelines has large investment. So the current production mode is featured by single well tank oil storage, oil tank truck transportation and manual tank truck scheduling. At present, oil well production and crude oil transportation scheduling mainly rely on manual formulation, which has poor coordination and seriously restricts the release of oil well production capacity and the reduction of transportation cost. A mixed-integer linear programming (MILP) model representation was proposed in our previous work. The scale of the built model variables is huge, and the model solution time is long. In this paper, a genetic algorithm based on multi-layer coding is proposed. The first layer of the design code is the driving path of the oil tanker, and the second layer is the crude oil loading and unloading amount and the cumulative time. The algorithm expands the search domain by flipping, exchanging and shifting the code. In the case analysis part, the exact algorithm and the genetic algorithm designed in this paper are used to solve cases of different scales (5, 10, 30 and 200 oil wells) respectively, and the correctness and effectiveness of the algorithm are verified. The results show that, compared with the exact algorithm, the genetic algorithm designed in this paper can quickly solve a feasible scheduling scheme under different oil well scales, especially in the case of large-scale (200 wells) oil well groups. The optimal one-time result in the calculation example (200 wells) takes 1062.3 s to run, and the total driving distance of all tank trucks in the obtained feasible scheme is 11280 km. This study can guide oilfields to quickly formulate dispatching plans and minimize travel distances in non-pipeline well tanker dispatching. |
| format | Article |
| id | doaj-art-981397d97e404144b53ae5b74d28914c |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-981397d97e404144b53ae5b74d28914c2025-01-17T04:50:51ZengElsevierHeliyon2405-84402025-01-01111e41307Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for non-pipelined wellsQiushi Li0Yuze Li1Haitong Sun2Wei Song3Honghong Li4Xiaoyong Gao5Chaodong Tan6Yaoyun Liu7Hongbing Liu8Digital and Intelligent Business Unit, PetroChina Changqing Oil Field Branch, Xi'an, 710018, ChinaSinopec Gas Storage Branch, Zhengzhou, 450000, ChinaDepartment of Automation, China University of Petroleum, Beijing, 102249, ChinaQinghai Oilfield Company, PetroChina, Qinghai, 817500, ChinaKunlun Digital Technology Co., Ltd, Beijing, 102249, ChinaDepartment of Automation, China University of Petroleum, Beijing, 102249, ChinaDepartment of Automation, China University of Petroleum, Beijing, 102249, China; Corresponding author.Tianjin Petroleum Vocational and Technical College, Tianjin, 301607, ChinaTianjin Petroleum Vocational and Technical College, Tianjin, 301607, ChinaThe marginal wells in low-permeability oil fields are characterized by small storage size, scattered distribution, intermittent production, etc. The construction of large-scale gathering pipelines has large investment. So the current production mode is featured by single well tank oil storage, oil tank truck transportation and manual tank truck scheduling. At present, oil well production and crude oil transportation scheduling mainly rely on manual formulation, which has poor coordination and seriously restricts the release of oil well production capacity and the reduction of transportation cost. A mixed-integer linear programming (MILP) model representation was proposed in our previous work. The scale of the built model variables is huge, and the model solution time is long. In this paper, a genetic algorithm based on multi-layer coding is proposed. The first layer of the design code is the driving path of the oil tanker, and the second layer is the crude oil loading and unloading amount and the cumulative time. The algorithm expands the search domain by flipping, exchanging and shifting the code. In the case analysis part, the exact algorithm and the genetic algorithm designed in this paper are used to solve cases of different scales (5, 10, 30 and 200 oil wells) respectively, and the correctness and effectiveness of the algorithm are verified. The results show that, compared with the exact algorithm, the genetic algorithm designed in this paper can quickly solve a feasible scheduling scheme under different oil well scales, especially in the case of large-scale (200 wells) oil well groups. The optimal one-time result in the calculation example (200 wells) takes 1062.3 s to run, and the total driving distance of all tank trucks in the obtained feasible scheme is 11280 km. This study can guide oilfields to quickly formulate dispatching plans and minimize travel distances in non-pipeline well tanker dispatching.http://www.sciencedirect.com/science/article/pii/S2405844024173380MILPGenetic algorithmQuick solutionMinimize the driving distance |
| spellingShingle | Qiushi Li Yuze Li Haitong Sun Wei Song Honghong Li Xiaoyong Gao Chaodong Tan Yaoyun Liu Hongbing Liu Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for non-pipelined wells Heliyon MILP Genetic algorithm Quick solution Minimize the driving distance |
| title | Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for non-pipelined wells |
| title_full | Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for non-pipelined wells |
| title_fullStr | Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for non-pipelined wells |
| title_full_unstemmed | Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for non-pipelined wells |
| title_short | Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for non-pipelined wells |
| title_sort | transportation and production collaborative scheduling optimization with multi layer coding genetic algorithm for non pipelined wells |
| topic | MILP Genetic algorithm Quick solution Minimize the driving distance |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024173380 |
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