Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging
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 proces...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/9/935 |
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| author | Muhammad Naeim Mohd Aris Hanita Daud Khairul Arifin Mohd Noh Sarat Chandra Dass |
| author_facet | Muhammad Naeim Mohd Aris Hanita Daud Khairul Arifin Mohd Noh Sarat Chandra Dass |
| author_sort | Muhammad Naeim Mohd Aris |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:03:45Z |
| format | Article |
| id | doaj.art-5ba0f0d1db3e427baf27558ef5ca96af |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T12:03:45Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-5ba0f0d1db3e427baf27558ef5ca96af2023-11-21T16:46:49ZengMDPI AGMathematics2227-73902021-04-019993510.3390/math9090935Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed LoggingMuhammad Naeim Mohd Aris0Hanita Daud1Khairul Arifin Mohd Noh2Sarat Chandra Dass3Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaDepartment of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaDepartment of Geosciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaSchool of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, MalaysiaThis 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.https://www.mdpi.com/2227-7390/9/9/935stochastic processGaussian processseabed loggingelectromagnetic datagradient descentinversion |
| spellingShingle | Muhammad Naeim Mohd Aris Hanita Daud Khairul Arifin Mohd Noh Sarat Chandra Dass Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging Mathematics stochastic process Gaussian process seabed logging electromagnetic data gradient descent inversion |
| title | Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging |
| title_full | Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging |
| title_fullStr | Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging |
| title_full_unstemmed | Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging |
| title_short | Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging |
| title_sort | stochastic process based inversion of electromagnetic data for hydrocarbon resistivity estimation in seabed logging |
| topic | stochastic process Gaussian process seabed logging electromagnetic data gradient descent inversion |
| url | https://www.mdpi.com/2227-7390/9/9/935 |
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