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Technical Report

A Review on Deep Learning Applications in Logging Data to Model Gas-Hydrate-Bearing Sediments

가스하이드레이트 저류층 모델링을 위한 심층학습 기반 물리검층 해석의 최신 기술동향 분석

Gayoung Park1
Seoyoon Kwon2
Minsoo Ji1
Baehyun Min3*
Xuan Huy Nguyen 4
Kwang Hyun Kim5
Sungil Kim6
Kyungbook Lee7
박 가영1
권 서윤2
지 민수1
민 배현3*
Nguyen Xuan Huy4
김 광현5
김 성일6
이 경북7
1M.S Course, Department of Climate and Energy Systems Engineering, Ewha Womans University
2All but Dissertation, Department of Climate and Energy Systems Engineering, Ewha Womans University
3Associate Professor, Department of Climate and Energy Systems Engineering, Ewha Womans University
4Assistant Professor, Faculty of Geology and Petroleum Engineering, Ho Chi Minh City University of Technology
5Assistant Researcher, Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources
6Senior Researcher, Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources
7Assistant Professor, Department of Geoenvironmental Sciences, Kongju National University
1이화여자대학교 기후‧에너지시스템공학과 석사과정
2이화여자대학교 기후‧에너지시스템공학과 박사과정수료
3이화여자대학교 기후‧에너지시스템공학과 부교수
4호치민시기술대학교 지질석유공학과 조교수
5한국지질자원연구원 석유해저연구본부 연구원
6한국지질자원연구원 석유해저연구본부 선임연구원
7공주대학교 지질환경과학과 조교수
*Corresponding Author
April 2021. pp. 161-178
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Abstract

본 연구는 국내 가스하이드레이트 저류층에 대한 심층학습 모델링 연구의 기반을 제공하고자 한다. 이를 위해 유·가스 저류층 및 가스하이드레이트 저류층에서 물리검층 자료에 신경망 기법을 적용한 최신 기술동향을 분석한다. 물리검층 분야에서 신경망 기법을 적용한 국내외 연구사례들을 신경망이 예측하고자 하는 출력자료 유형에 따라 저류층 암상 추정, 유체유동인자 추정, 지질역학인자 추정, 합성로그 생성 등 네 가지 적용 유형으로 분류하였다. 각 사례들의 주요 특징을 기술하는 한편 특징들을 요약한 종합표를 제공한다.

키워드
가스하이드레이트
물리검층
심층학습
적용 유형

This study aims to provide the fundamentals of deep learning-based modeling for gas hydrate-bearing sediments. For this purpose, we analyze state-of-the-art neural network applications in geophysical logs for conventional oil and gas reservoirs and unconventional gas-hydrate-bearing sediments. According to the type of neural network outputs, the previous studies are categorized into four application types: estimation of reservoir lithofacies, estimation of fluid flow parameters, estimation of geomechanical parameters, and generation of synthetic logs. For each type, we describe the key points of previous studies and provide summary tables.

Keywords
Gas hydrate
Geophysical logs
Deep learning
Application type
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Information
  • Publisher :The Korean Society of Mineral and Energy Resources Engineers
  • Publisher(Ko) :한국자원공학회
  • Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
  • Journal Title(Ko) :한국자원공학회지
  • Volume : 58
  • No :2
  • Pages :161-178
  • Received Date :2021. 03. 04
  • Revised Date :2021. 04. 07
  • Accepted Date : 2021. 04. 27
  • DOI :https://doi.org/10.32390/ksmer.2021.58.2.161
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