relation: https://khub.utp.edu.my/scholars/13725/
title: Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach
creator: Babasafari, A.A.
creator: Rezaei, S.
creator: Salim, A.M.A.
creator: Kazemeini, S.H.
creator: Ghosh, D.P.
description: For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or Vp/Vs ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable. © 2020, The Author(s).
publisher: Springer Science and Business Media Deutschland GmbH
date: 2020
type: Article
type: PeerReviewed
identifier:   Babasafari, A.A. and Rezaei, S. and Salim, A.M.A. and Kazemeini, S.H. and Ghosh, D.P.  (2020) Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach.  Journal of Petroleum Exploration and Production Technology.   ISSN 21900558     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092721290&doi=10.1007%2fs13202-020-01013-0&partnerID=40&md5=dcd51dc8f19c8c1ace8dd9931169b658
relation: 10.1007/s13202-020-01013-0
identifier: 10.1007/s13202-020-01013-0