가스하이드레이트 저류층 모델링을 위한 심층학습 기반 물리검층 해석의 최신 기술동향 분석
A Review on Deep Learning Applications in Logging Data to Model Gas-Hydrate-Bearing Sediments
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참고문헌
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초록
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 연구사례들을신경망이 예측하고자 하는 출력자료 유형에 따라 저류층 암상 추정, 유체유동인자 추정, 지질역학인자 추정, 합성로그 생성 등 네 가지 적용 유형으로 분류하였다. 각 사례들의 주요 특징을 기술하는한편 특징들을 요약한 종합표를 제공한다.
Abstract
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.
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