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Oil well characterization and artificial gas lift optimization using neural networks combined with genetic algorithm

Monyei, Chukwuka; Adewumi, Aderemi O.; Obolo, Michael O.

Authors

Chukwuka Monyei Chukwuka.Monyei@uwe.ac.uk
Research Fellow - Demand Side Energy Management & Big Data Analytics

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. © 2014 Chukwuka G. Monyei et al.

Journal Article Type Article
Publication Date Jan 1, 2014
Journal Discrete Dynamics in Nature and Society
Print ISSN 1026-0226
Electronic ISSN 1607-887X
Publisher Hindawi
Peer Reviewed Peer Reviewed
Volume 2014
Pages 289239
APA6 Citation Monyei, C., Adewumi, A. O., & Obolo, M. O. (2014). Oil well characterization and artificial gas lift optimization using neural networks combined with genetic algorithm. Discrete Dynamics in Nature and Society, 2014, 289239. https://doi.org/10.1155/2014/289239
DOI https://doi.org/10.1155/2014/289239
Publisher URL https://doi.org/10.1155/2014/289239

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