Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging

Muhammad Naeim Mohd Aris, Hanita Daud, Khairul Arifin Mohd Noh and Sarat Chandra Dass
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Muhammad Naeim Mohd Aris: Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Hanita Daud: Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Khairul Arifin Mohd Noh: Department of Geosciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Sarat Chandra Dass: School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, Malaysia

Mathematics, 2021, vol. 9, issue 9, 1-24

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.

Keywords: stochastic process; Gaussian process; seabed logging; electromagnetic data; gradient descent; inversion; optimization; hydrocarbon resistivity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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