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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 289239, 10 pages
http://dx.doi.org/10.1155/2014/289239
Research Article

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

1Department of Electrical and Electronic Engineering, University of Ibadan, Nigeria
2School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, South Africa
3Department of Petroleum Engineering, University of Ibadan, Nigeria

Received 24 January 2014; Revised 23 March 2014; Accepted 8 April 2014; Published 22 May 2014

Academic Editor: Weihua Liu

Copyright © 2014 Chukwuka G. Monyei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.