Multi-well placement optimisation using sequential artificial neural networks and multi-level grid system

Ilsik Jang, Seeun Oh, Hyunjeong Kang, Juhwan Na, Baehyun Min

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This study suggests a sequential artificial neural network (ANN) method coupled with a multi-level grid system to optimise multi-well placement in petroleum reservoirs. As the number of scenarios for placing wells increases exponentially with the number of wells, the difficulty in finding the global optimum increases accordingly due to the intrinsic uncertainty of ANNs. The multi-level grid system can reduce the size of the search space by allocating only one well grid block per several grid blocks in the basic grid system. A higher level of grid system consists of finer grid blocks to gradually improve the resolution of the grid system. Repetitive implementation of the sequential ANN at each level of the grid system narrows the search space, and the global optimum is determined. The proposed algorithm is validated with applications to two- and three-infill-well problems in a coal-bed methane (CBM) reservoir.

Original languageEnglish
Pages (from-to)445-465
Number of pages21
JournalInternational Journal of Oil, Gas and Coal Technology
Issue number4
StatePublished - 2020

Bibliographical note

Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Energy Efficiency and Resources program with a grant (No. 20152510101880) from the Korea Institute of Energy Technology Evaluation and Planning (KETEP) funded by the Korea Government Ministry of Trade, Industry, and Energy.

Publisher Copyright:
Copyright © 2020 Inderscience Enterprises Ltd.


  • Multi-level grid system
  • Multi-well placement
  • Optimisation
  • Sequential artificial neural network


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