Yuanchao Peng
Research Subjects
Yuanchao Peng has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Hao-Nan Duan
Xi’an shiyou University
1 shared publication - Liupeng Wang
College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
1 shared publication - Xuyang Liu
College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
1 shared publication - Zhikun Liu
College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
1 shared publication
Latest Publications
- Open Access
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 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.
Open AccessResearch 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 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.