.png){kind=icon}
Return
Study on deep reinforcement learning-based multi-objective path planning algorithm for inter-well connected-channels
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2023.110761.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Defining inter-well connectivity is very important for the water injection development of carbonate fractured-vuggy reservoirs. However, most conventional methods based on logging or seismic data are subjective. In this research, multi-attribute seismic data were used to characterize the 3D search space, and an improved reward function was designed based on the law of fluid flow and appropriate heuristics. A deep reinforcement learning model based on an improved reward function was designed to search for inter-well connected channels. Selecting the shortest channel length and the largest flow volume were the optimization objectives. We propose the multi-objective evolutionary optimization algorithm with decomposition and differential evolution (MOEA/D-DE) based on an improved mutation operator to automatically obtain the optimal inter-well connected channels. The searched channels can readily show the spatial distribution of multi-scale fractures and caves. The experimental results of the Tahe oilfield show that the improved algorithm effectively enhanced the local convergence performance of the multi-objective algorithm and that the automatic search paths were consistent with the characteristics of seismic static data and tracer test results. Our model can better reflect the spatial distribution of a fracture cavity at different scales and offers important guidance for on-site development adjustment work in the water injection development stage.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Fracture-cavity reservoir
Inter-well connected channels
Deep reinforcement learning
Multi-objective optimization
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
Cited Papers
ShareSaveReconnaissance Mission Conducted by UAV Swarms Based on Distributed PSO Path Planning Algorithms.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveA hybrid differential evolution for multi-objective optimisation problemsCONNECTION SCIENCE
OAAI
CONNECTION SCIENCEIF3.4
OAAIShareSaveShareSaveShareSave
View more