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Oil Well Characterization and Artificial Gas Lift Optimization Using Neural Networks Combined with Genetic Algorithm

Author

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  • Chukwuka G. Monyei
  • Aderemi O. Adewumi
  • Michael O. Obolo

Abstract

This paper examines the characterization of six oil wells and the allocation of gas considering limited and unlimited case scenario. Artificial gas lift involves injecting high-pressured gas from the surface into the producing fluid column through one or more subsurface valves set at predetermined depths. This improves recovery by reducing the bottom-hole pressure at which wells become uneconomical and are thus abandoned. This paper presents a successive application of modified artificial neural network (MANN) combined with a mild intrusive genetic algorithm (MIGA) to the oil well characteristics with promising results. This method helps to prevent the overallocation of gas to wells for recovery purposes while also maximizing oil production by ensuring that computed allocation configuration ensures maximum economic accrual. Results obtained show marked improvements in the allocation especially in terms of economic returns.

Suggested Citation

  • Chukwuka G. Monyei & Aderemi O. Adewumi & Michael O. Obolo, 2014. "Oil Well Characterization and Artificial Gas Lift Optimization Using Neural Networks Combined with Genetic Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-10, May.
    • Handle: RePEc:hin:jnddns:289239
    DOI: 10.1155/2014/289239
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    Cited by:

    1. Monyei, C.G. & Adewumi, A.O., 2017. "Demand Side Management potentials for mitigating energy poverty in South Africa," Energy Policy, Elsevier, vol. 111(C), pages 298-311.
    2. Hera Khan & Ayush Srivastav & Amit Kumar Mishra & Tien Anh Tran, 2022. "Machine learning methods for estimating permeability of a reservoir," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2118-2131, October.

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