Well-placement optimisation using sequential artificial neural networks

• Main Authors: Ilsik Jang, Seeun Oh, Yumi Kim, Changhyup Park, Hyunjeong Kang
• Format: Article
• Language: English
• Published: SAGE Publishing 2018-05-01
• Series: Energy Exploration & Exploitation
• Online Access: https://doi.org/10.1177/0144598717729490

In this study, a new algorithm is proposed by employing artificial neural networks in a sequential manner, termed the sequential artificial neural network, to obtain a global solution for optimizing the drilling location of oil or gas reservoirs. The developed sequential artificial neural network is used to successively narrow the search space to efficiently obtain the global solution. When training each artificial neural network, pre-defined amount of data within the new search space are added to the training dataset to improve the estimation performance. When the size of the search space meets a stopping criterion, reservoir simulations are performed for data in the search space, and a global solution is determined among the simulation results. The proposed method was applied to optimise a horizontal well placement in a coalbed methane reservoir. The results show a superior performance in optimisation while significantly reducing the number of simulations compared to the particle-swarm optimisation algorithm.