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Original Article | Open Access

Combination of sonic wave velocity, density and electrical resistivity for joint estimation of gas-hydrate reservoir parameters and their uncertainties

State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, P. R. China
School of Ocean and Earth Science, Tongji University, Shanghai 200092, P. R. China
Center for Marine Resources, Tongji University, Shanghai 20092, P. R. China
State Key Laboratory of Offshore Natural Gas Hydrates, CNOOC, Beijing 100027, P. R. China
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Abstract

Gas-hydrate saturation and porosity are the most crucial reservoir parameters for gas-hydrate resource assessment. Numerous academics have put forward elastic and electrical petrophysical models for calculating the saturation and porosity of gas-hydrate. However, owing to the limitations of a single petrophysical model, the estimation of gas-hydrate saturation and porosity using single elastic or electrical measurement data appears to be inconsistent and uncertain. In this study, the sonic wave velocity, density and resistivity well log data are combined with a Bayesian linear inversion method for the simultaneous estimation of gas-hydrate saturation and porosity. The sonic wave velocity, density and resistivity data of the Shenhu area in the South China Sea are used to estimate the gas-hydrate saturation and porosity. To validate the accuracy of this method, the estimation results are compared with the saturation obtained from pore water chemistry and porosity obtained from density logs. The well log data examples show that the joint estimation method not only provides a rapid estimation of the gas-hydrate reservoir parameters but also improves the accuracy of results and determines their uncertainty.

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Advances in Geo-Energy Research
Pages 133-140

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Cite this article:
Zhang X, Li Q, Li L, et al. Combination of sonic wave velocity, density and electrical resistivity for joint estimation of gas-hydrate reservoir parameters and their uncertainties. Advances in Geo-Energy Research, 2023, 10(2): 133-140. https://doi.org/10.46690/ager.2023.11.07

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Received: 17 September 2023
Revised: 02 October 2023
Accepted: 24 October 2023
Published: 28 October 2023
© The Author(s) 2023.

This article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.