Solving geosteering inverse problems by stochastic Hybrid Monte Carlo method
Abstract
The inverse problems arise in almost all fields of science where the realworld parameters are extracted from a set of measured data. The geosteering inversion plays an essential role in the accurate prediction of oncoming strata as well as a reliable guidance to adjust the borehole position on the fly to reach one or more geological targets. This mathematical treatment is not easy to solve, which requires finding an optimum solution among a large solution space, especially when the problem is nonlinear and nonconvex. Nowadays, a new generation of loggingwhiledrilling (LWD) tools has emerged on the market. The socalled azimuthal resistivity LWD tools have azimuthal sensitivity and a large depth of investigation. Hence, the associated inverse problems become much more difficult since the earth model to be inverted will have more detailed structures. The conventional deterministic methods are incapable to solve such a complicated inverse problem, where they suffer from the local minimum trap. Alternatively, stochastic optimizations are in general better at finding global optimal solutions and handling uncertainty quantification. In this article, we investigate the Hybrid Monte Carlo (HMC) based statistical inversion approach and suggest that HMC based inference is more efficient in dealing with the increased complexity andmore »
 Authors:
 Univ. of Houston, Houston, TX (United States). Dept. of Electrical and Computer Engineering
 Univ. of Houston, Houston, TX (United States). Dept. of Information and Logistics Technology
 Cyentech Consulting LLC, Cypress, TX (United States)
 Publication Date:
 Research Org.:
 Cyentech Consulting LLC, Cypress, TX (United States); University of Houston, Houston, TX (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC21)
 OSTI Identifier:
 1409490
 Grant/Contract Number:
 SC0017033
 Resource Type:
 Journal Article: Accepted Manuscript
 Journal Name:
 Journal of Petroleum Science and Engineering
 Additional Journal Information:
 Journal Volume: 161; Journal ID: ISSN 09204105
 Publisher:
 Elsevier
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; Statistical inversion; Hybrid monte carlo; Geosteering; Logging while drilling; Well logging
Citation Formats
Shen, Qiuyang, Wu, Xuqing, Chen, Jiefu, Han, Zhu, and Huang, Yueqin. Solving geosteering inverse problems by stochastic Hybrid Monte Carlo method. United States: N. p., 2017.
Web. doi:10.1016/j.petrol.2017.11.031.
Shen, Qiuyang, Wu, Xuqing, Chen, Jiefu, Han, Zhu, & Huang, Yueqin. Solving geosteering inverse problems by stochastic Hybrid Monte Carlo method. United States. doi:10.1016/j.petrol.2017.11.031.
Shen, Qiuyang, Wu, Xuqing, Chen, Jiefu, Han, Zhu, and Huang, Yueqin. 2017.
"Solving geosteering inverse problems by stochastic Hybrid Monte Carlo method". United States.
doi:10.1016/j.petrol.2017.11.031.
@article{osti_1409490,
title = {Solving geosteering inverse problems by stochastic Hybrid Monte Carlo method},
author = {Shen, Qiuyang and Wu, Xuqing and Chen, Jiefu and Han, Zhu and Huang, Yueqin},
abstractNote = {The inverse problems arise in almost all fields of science where the realworld parameters are extracted from a set of measured data. The geosteering inversion plays an essential role in the accurate prediction of oncoming strata as well as a reliable guidance to adjust the borehole position on the fly to reach one or more geological targets. This mathematical treatment is not easy to solve, which requires finding an optimum solution among a large solution space, especially when the problem is nonlinear and nonconvex. Nowadays, a new generation of loggingwhiledrilling (LWD) tools has emerged on the market. The socalled azimuthal resistivity LWD tools have azimuthal sensitivity and a large depth of investigation. Hence, the associated inverse problems become much more difficult since the earth model to be inverted will have more detailed structures. The conventional deterministic methods are incapable to solve such a complicated inverse problem, where they suffer from the local minimum trap. Alternatively, stochastic optimizations are in general better at finding global optimal solutions and handling uncertainty quantification. In this article, we investigate the Hybrid Monte Carlo (HMC) based statistical inversion approach and suggest that HMC based inference is more efficient in dealing with the increased complexity and uncertainty faced by the geosteering problems.},
doi = {10.1016/j.petrol.2017.11.031},
journal = {Journal of Petroleum Science and Engineering},
number = ,
volume = 161,
place = {United States},
year = 2017,
month =
}

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