Cognitive seismic data modelling based successive differential evolution algorithm for effective exploration of oil-gas reservoirs
Zhao, Jing and Ren, Jinchang and Zabalza, Jaime and Gao, Jinghuai and Xu, Xinying and Xie, Gang (2018) Cognitive seismic data modelling based successive differential evolution algorithm for effective exploration of oil-gas reservoirs. Journal of Petroleum Science and Engineering, 171. pp. 1159-1170. ISSN 0920-4105 (https://doi.org/10.1016/j.petrol.2018.08.044)
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Abstract
A cognitive modelling based new inversion method, the successive differential evolution (DE-S) algorithm, is proposed to estimate the Q factor and velocity from the zero-offset vertical seismic profile (VSP) record for oil-gas reservoir exploration. The DE algorithm seeks optimal solutions by simulating the natural species evolution processes and makes the individuals become optimal. This algorithm is suitable for the high-dimensional nonseparable model space where the inversion leads to recognition and prediction of hydrocarbon reservoirs. The viscoelastic medium is split into layers whose thicknesses equal to the space between two successive VSP geophones, and the estimated parameters of each layer span the related subspace. All estimated parameters span to a high dimensional nonseparable model space. We develop bottom-up workflow, in which the Q factor and the velocity are estimated using the DE algorithm layer by layer. In order to improve the inversion precision, the crossover strategy is discarded and we derive the weighted mutation strategy. Additionally, two kinds of stopping criteria for effective iteration are proposed to speed up the computation. The new method has fast speed, good convergence and is no longer dependent on the initial values of model parameters. Experimental results on both synthetic and real zero-offset VSP data indicate that this method is noise robust and has great potential to derive reliable seismic attenuation and velocity, which is an important diagnostic tool for reservoir characterization.
ORCID iDs
Zhao, Jing, Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194, Zabalza, Jaime ORCID: https://orcid.org/0000-0002-0634-1725, Gao, Jinghuai, Xu, Xinying and Xie, Gang;-
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Item type: Article ID code: 65188 Dates: DateEvent1 December 2018Published21 August 2018Published Online14 August 2018Accepted1 April 2018SubmittedNotes: © 2018 Elsevier B.V. All rights reserved. Jing Zhao, Jinchang Ren, Jaime Zabalza, Jinghuai Gao, Xinying Xu, Gang Xie, Cognitive seismic data modelling based successive differential evolution algorithm for effective exploration of oil-gas reservoirs, Journal of Petroleum Science and Engineering, Volume 171, 2018, Pages 1159-1170, https://doi.org/10.1016/j.petrol.2018.08.044 Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 16 Aug 2018 13:24 Last modified: 14 Oct 2024 07:09 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/65188