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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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/4/1492 |
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| author | Hanita Daud Muhammad Naeim Mohd Aris Khairul Arifin Mohd Noh Sarat Chandra Dass |
| author_facet | Hanita Daud Muhammad Naeim Mohd Aris Khairul Arifin Mohd Noh Sarat Chandra Dass |
| author_sort | Hanita Daud |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T05:19:26Z |
| format | Article |
| id | doaj.art-a58318f3468e4183977ae3a3177cde39 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:19:26Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a58318f3468e4183977ae3a3177cde392023-12-03T12:42:23ZengMDPI AGApplied Sciences2076-34172021-02-01114149210.3390/app11041492A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic DataHanita Daud0Muhammad Naeim Mohd Aris1Khairul Arifin Mohd Noh2Sarat Chandra Dass3Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaDepartment of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaDepartment of Geosciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaSchool of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, MalaysiaSeabed 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.https://www.mdpi.com/2076-3417/11/4/1492seabed loggingelectromagnetic datahydrocarbon depthinverse modelingGaussian processgradient descent |
| spellingShingle | Hanita Daud Muhammad Naeim Mohd Aris Khairul Arifin Mohd Noh Sarat Chandra Dass A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data Applied Sciences seabed logging electromagnetic data hydrocarbon depth inverse modeling Gaussian process gradient descent |
| title | 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_short | 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 |
| topic | seabed logging electromagnetic data hydrocarbon depth inverse modeling Gaussian process gradient descent |
| url | https://www.mdpi.com/2076-3417/11/4/1492 |
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