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Inversion of the permeability of a tight gas reservoir with the combination of a deep Boltzmann kernel extreme learning machine and nuclear magnetic resonance logging transverse relaxation time spectrum data

Zhu Linqi, Zhang Chong, Wei Yang, Zhou Xueqing, Huang Yuyang and Zhang Chaomo
Inversion of the permeability of a tight gas reservoir with the combination of a deep Boltzmann kernel extreme learning machine and nuclear magnetic resonance logging transverse relaxation time spectrum data
Interpretation (Tulsa) (May 2017) 5 (3): T313-T322

Abstract

In view of the low accuracy of the existing nuclear magnetic-resonance (NMR) logging permeability model in tight sandstone reservoirs, we derive a relationship between the NMR T (sub 2) spectrum and permeability based on the transverse relaxation theory of NMR and the Kozeny-Carman equation. We determined the reasons for the low accuracy of the model through the theoretical analysis. We have developed the deep Boltzmann kernel extreme learning machine (DBKELM) to improve the deep-learning algorithm and to predict the reservoir permeability based on NMR logging with a deep Boltzmann machine (DBM). We use the permeability data of 200 rock specimens in a tight gas reservoir in a certain area and the corresponding T (sub 2) spectra from NMR logging for modeling. We apply the model to the evaluation of permeability in this area. The results show that the accuracy of the deep-learning algorithm is higher than that of the existing NMR logging permeability model and the shallow layer machine-learning model. Furthermore, the accuracy of the DBKELM that we have developed is higher than that of the DBM, which indicates that DBKELM is more suitable for the prediction of reservoir permeability. Therefore, deep-learning theory can be effectively used in oil exploration and development, and it can improve the interpretation accuracy of reservoir parameters. These findings contribute to the interpretation of reservoir parameters.


ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 5
Serial Issue: 3
Title: Inversion of the permeability of a tight gas reservoir with the combination of a deep Boltzmann kernel extreme learning machine and nuclear magnetic resonance logging transverse relaxation time spectrum data
Affiliation: Yangtze University, Ministry of Education, Key Laboratory of Exploration Technologies for Oil and Gas Resources, Wuhan, China
Pages: T313-T322
Published: 201705
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 29
Accession Number: 2017-074504
Categories: Economic geology, geology of energy sources
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 1 table, sketch maps
N20°00'00" - N53°00'00", E74°00'00" - E135°00'00"
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2017, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 201739
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