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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2021
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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/ |
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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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