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

Muhammad Naeim Mohd Aris, Hanita Daud, Khairul Arifin Mohd Noh, Sarat Chandra Dass

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number935
JournalMathematics
Volume9
Issue number9
Early online date23 Apr 2021
DOIs
Publication statusPublished - 1 May 2021

Keywords

  • Electromagnetic data
  • Gaussian process
  • Gradient descent
  • Hydrocarbon resistivity
  • Inversion
  • Optimization
  • Seabed logging
  • Stochastic process

ASJC Scopus subject areas

  • Mathematics(all)

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