eprintid: 14969
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/49/69
datestamp: 2023-11-10 03:29:34
lastmod: 2023-11-10 03:29:34
status_changed: 2023-11-10 01:58:17
type: article
metadata_visibility: show
creators_name: Mohd Aris, M.N.
creators_name: Daud, H.
creators_name: Mohd Noh, K.A.
creators_name: Chandra Dass, S.
title: Stochastic process-based inversion of electromagnetic data for hydrocarbon resistivity estimation in seabed logging
ispublished: pub
note: cited By 2
abstract: This work proposes a stochastic process-based inversion to estimate hydrocarbon resistivity based on multifrequency electromagnetic (EM) data. Currently, mesh-based algorithms are used for processing the EM responses which cause high time-consuming and unable to quantify uncertainty. Gaussian process (GP) is utilized as the alternative forward modeling approach to evaluate the EM profiles with uncertainty quantification. For the optimization, gradient descent is used to find the optimum by minimizing its loss function. The prior EM profiles are evaluated using finite element (FE) through computer simulation technology (CST) software. For validation purposes, mean squared deviation and its root between EM profiles evaluated by the GP and FE at the unobserved resistivities are computed. Time taken for the GP and CST to evaluate the EM profiles is compared, and absolute error between the estimate and its simulation input is also computed. All the resulting deviations were significantly small, and the GP took lesser time to evaluate the EM profiles compared to the software. The observational datasets also lied within the 95 confidence interval (CI) where the resistivity inputs were estimated by the proposed inversion. This indicates the stochastic process-based inversion can effectively estimate the hydrocarbon resistivity in the seabed logging. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2021
publisher: MDPI AG
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105327280&doi=10.3390%2fmath9090935&partnerID=40&md5=12e23d4b4458180d708a5e71360d59e5
id_number: 10.3390/math9090935
full_text_status: none
publication: Mathematics
volume: 9
number: 9
refereed: TRUE
issn: 22277390
citation:   Mohd Aris, M.N. and Daud, H. and Mohd Noh, K.A. and Chandra Dass, S.  (2021) Stochastic process-based inversion of electromagnetic data for hydrocarbon resistivity estimation in seabed logging.  Mathematics, 9 (9).   ISSN 22277390