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Formation Estimation of Shaly Sandstone Reservoir using Joint Inversion from Well Logging Data

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Title
Formation Estimation of Shaly Sandstone Reservoir using Joint Inversion from Well Logging Data
Other Titles
복합역산을 이용한 물리검층자료로부터의 셰일성 사암 저류층의 지층 평가
Authors
Choi, Yeon Jin
Chung, Woo-Keen
Ha, Jiho
Ryul, Shin Sung
Subject
Engineering
Keywords
joint inversionrock physics modelwell logging
Issue Date
2019-02
Citation
Choi, Yeon Jin, et al. 2019. "Formation Estimation of Shaly Sandstone Reservoir using Joint Inversion from Well Logging Data". Geophysics and Geophysical Exploration, 22(1): 1-11.
Abstract
Well logging technologies are used to measure the physical properties of reservoirs through boreholes. These technologies have been utilized to understand reservoir characteristics, such as porosity, fluid saturation, etc., using equations based on rock physics models. The analysis of well logs is performed by selecting a reliable rock physics model adequate for reservoir conditions or characteristics, comparing the results using the Archie's equation or simandoux method, and determining the most feasible reservoir properties. In this study, we developed a joint inversion algorithm to estimate physical properties in shaly sandstone reservoirs based on the pre-existing algorithm for sandstone reservoirs. For this purpose, we proposed a rock physics model with respect to shale volume, constructed the Jacobian matrix, and performed the sensitivity analysis for understanding the relationship between well-logging data and rock properties. The joint inversion algorithm was implemented by adopting the least-squares method using probabilistic approach. The developed algorithm was applied to the well-logging data obtained from the Colony gas sandstone reservoir. The results were compared with the simandox method and the joint inversion algorithms of sand stone reservoirs.
URI
https://repository.kopri.re.kr/handle/201206/10838
DOI
http://dx.doi.org/10.7582/GGE.2019.22.1.001
Type
Article
Station
해당사항없음
Indexed
KCI등재
Appears in Collections  
2018-2019, Investigation of submarine resource environment and seabed methane release in the Arctic (18-19) / Jin, Young Keun (PM18050)
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