eprintid: 18565
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/85/65
datestamp: 2024-06-04 14:10:53
lastmod: 2024-06-04 14:10:53
status_changed: 2024-06-04 14:03:37
type: article
metadata_visibility: show
creators_name: Ishak, M.A.
creators_name: Abdul Latiff, A.H.
creators_name: Ho, E.T.W.
creators_name: Fuad, M.I.A.
creators_name: Tan, N.W.
creators_name: Sajid, M.
creators_name: Elsebakhi, E.
title: Advanced Elastic and Reservoir Properties Prediction through Generative Adversarial Network
ispublished: pub
note: cited By 3
abstract: The prediction of subsurface properties such as velocity, density, porosity, and water saturation has been the main focus of petroleum geosciences. Advanced methods such as Full Waveform Inversion (FWI), Joint Migration Inversion (JMI) and ML-Rock Physics are able to produce better predictions than their predecessors, but they still require tedious manual interpretation that is prone to human error. The research on these methods remains open as they suffer from technical limitations. As computing resources are becoming cheaper, the use of a single deep-generative adversarial network is feasible in predicting all these properties in a completely data-driven manner. In our proposed method of multiscale pix2pix applied to SEG SEAM salt data, we have managed to map from one input, which is seismic post-stack data, to several outputs of reservoir and elastic properties such as porosity, velocity, and density by using only one trained model and without having to manually interpret or pre-process the input data. With 90 accuracy of the results in the synthetic data testing, the method is worthy of being explored by the petroleum geoscience fraternity. © 2023 by the authors.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160862032&doi=10.3390%2fapp13106311&partnerID=40&md5=f059b4c1d9db89d8c8a51053761931fb
id_number: 10.3390/app13106311
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 13
number: 10
refereed: TRUE
citation:   Ishak, M.A. and Abdul Latiff, A.H. and Ho, E.T.W. and Fuad, M.I.A. and Tan, N.W. and Sajid, M. and Elsebakhi, E.  (2023) Advanced Elastic and Reservoir Properties Prediction through Generative Adversarial Network.  Applied Sciences (Switzerland), 13 (10).