Research on Modeling Method for Optimal Allocation of Wellhead Targets in Large Well Clusters
The paper proposes a genetic ant colony algorithm that integrates genetic and ant colony algorithms, enhancing the heuristic function of the latter, to address target point distribution issues in large well clusters. This algorithm utilizes genetic algorithms for initial pheromone distribution and e...
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| Published in | Processes Vol. 12; no. 8; p. 1705 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.08.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2227-9717 2227-9717 |
| DOI | 10.3390/pr12081705 |
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| Abstract | The paper proposes a genetic ant colony algorithm that integrates genetic and ant colony algorithms, enhancing the heuristic function of the latter, to address target point distribution issues in large well clusters. This algorithm utilizes genetic algorithms for initial pheromone distribution and employs the ant colony algorithm to achieve rapid convergence. Introducing genetic operators in each iteration addresses the ant colony system’s drawbacks, including scarcity of initial pheromones, susceptibility to local optima, and slow convergence speed. The model aims to minimize the sum of horizontal displacement and intersections in line connections from wellheads to target points as its dual-objective function. It validates the effectiveness of the genetic ACO algorithm in optimizing target point allocation at wellheads through a case study, highlighting its advantages over traditional methods in reducing displacement, ensuring result stability, and preventing collisions. |
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| AbstractList | The paper proposes a genetic ant colony algorithm that integrates genetic and ant colony algorithms, enhancing the heuristic function of the latter, to address target point distribution issues in large well clusters. This algorithm utilizes genetic algorithms for initial pheromone distribution and employs the ant colony algorithm to achieve rapid convergence. Introducing genetic operators in each iteration addresses the ant colony system’s drawbacks, including scarcity of initial pheromones, susceptibility to local optima, and slow convergence speed. The model aims to minimize the sum of horizontal displacement and intersections in line connections from wellheads to target points as its dual-objective function. It validates the effectiveness of the genetic ACO algorithm in optimizing target point allocation at wellheads through a case study, highlighting its advantages over traditional methods in reducing displacement, ensuring result stability, and preventing collisions. |
| Author | Liu, Zhikun Liu, Xuyang Wang, Liupeng Duan, Haonan Peng, Yuanchao |
| Author_xml | – sequence: 1 givenname: Liupeng surname: Wang fullname: Wang, Liupeng – sequence: 2 givenname: Haonan orcidid: 0009-0007-0479-3668 surname: Duan fullname: Duan, Haonan – sequence: 3 givenname: Zhikun surname: Liu fullname: Liu, Zhikun – sequence: 4 givenname: Yuanchao surname: Peng fullname: Peng, Yuanchao – sequence: 5 givenname: Xuyang surname: Liu fullname: Liu, Xuyang |
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| Cites_doi | 10.3390/pr12071412 10.1109/ICBASE53849.2021.00061 10.1016/S1876-3804(12)60026-3 |
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| Copyright | 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Algorithms Ant colony optimization Behavior Clusters Convergence Efficiency Feedback Genetic algorithms Heuristic Heuristic methods Mutation Optimization techniques Pheromones Principles Traveling salesman problem Wellheads |
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| Title | Research on Modeling Method for Optimal Allocation of Wellhead Targets in Large Well Clusters |
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