A novel methodology for hydrocarbon depth prediction in seabed logging: Gaussian process-based inverse modeling of electromagnetic data

Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source-receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex g...

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Main Authors: Daud, H., Mohd Aris, M.N., Mohd Noh, K.A., Dass, S.C.
Format: Article
Published: MDPI AG 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100842547&doi=10.3390%2fapp11041492&partnerID=40&md5=c3c8adda9b88afe635a490c91d1c5151
http://eprints.utp.edu.my/23787/
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Institution: Universiti Teknologi Petronas
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spelling my.utp.eprints.237872021-08-19T13:10:04Z A novel methodology for hydrocarbon depth prediction in seabed logging: Gaussian process-based inverse modeling of electromagnetic data Daud, H. Mohd Aris, M.N. Mohd Noh, K.A. Dass, S.C. Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source-receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at �untried� depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and �untried� computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. MDPI AG 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100842547&doi=10.3390%2fapp11041492&partnerID=40&md5=c3c8adda9b88afe635a490c91d1c5151 Daud, H. and Mohd Aris, M.N. and Mohd Noh, K.A. and Dass, S.C. (2021) A novel methodology for hydrocarbon depth prediction in seabed logging: Gaussian process-based inverse modeling of electromagnetic data. Applied Sciences (Switzerland), 11 (4). pp. 1-20. http://eprints.utp.edu.my/23787/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source-receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at �untried� depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and �untried� computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author Daud, H.
Mohd Aris, M.N.
Mohd Noh, K.A.
Dass, S.C.
spellingShingle Daud, H.
Mohd Aris, M.N.
Mohd Noh, K.A.
Dass, S.C.
A novel methodology for hydrocarbon depth prediction in seabed logging: Gaussian process-based inverse modeling of electromagnetic data
author_facet Daud, H.
Mohd Aris, M.N.
Mohd Noh, K.A.
Dass, S.C.
author_sort Daud, H.
title A novel methodology for hydrocarbon depth prediction in seabed logging: Gaussian process-based inverse modeling of electromagnetic data
title_short A novel methodology for hydrocarbon depth prediction in seabed logging: Gaussian process-based inverse modeling of electromagnetic data
title_full A novel methodology for hydrocarbon depth prediction in seabed logging: Gaussian process-based inverse modeling of electromagnetic data
title_fullStr A novel methodology for hydrocarbon depth prediction in seabed logging: Gaussian process-based inverse modeling of electromagnetic data
title_full_unstemmed A novel methodology for hydrocarbon depth prediction in seabed logging: Gaussian process-based inverse modeling of electromagnetic data
title_sort novel methodology for hydrocarbon depth prediction in seabed logging: gaussian process-based inverse modeling of electromagnetic data
publisher MDPI AG
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100842547&doi=10.3390%2fapp11041492&partnerID=40&md5=c3c8adda9b88afe635a490c91d1c5151
http://eprints.utp.edu.my/23787/
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