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 logoORCID: https://orcid.org/0000-0001-6116-3194, Zabalza, Jaime ORCID logoORCID: https://orcid.org/0000-0002-0634-1725, Gao, Jinghuai, Xu, Xinying and Xie, Gang;
  • Item type:Article
    ID code:65188
    Dates:
    Date
    Event
    1 December 2018
    Published
    21 August 2018
    Published Online
    14 August 2018
    Accepted
    1 April 2018
    Submitted
    Notes:© 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:21 Nov 2024 01:15
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/65188
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