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

Park, Gayoung; Kwon, Seoyoon; Ji, Minsoo; Min, Baehyun; Huy, Nguyễn Xuân; Kim, Kwang Hyun; Kim, Sung-Il; Lee, Kyungbook

doi:10.32390/ksmer.2021.58.2.161

Cited by 2 publications

(1 citation statement)

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“…이에 기확보한 표 본으로부터 시추공의 미지구간에 대한 물리검층 결과를 추 론해야 하는 문제에 직면하게 되었으며 최근 딥러닝(deep learning)이 발달하면서 신경망(neural network)과 같은 AI 기술의 활용사례가 증가하고 있다 (Park, 2021). 특히, 딥러 닝은 비선형 관계 추출에 탁월하다는 측면에서 저류층 인 자(reservoir parameters) 추정에 유용하여 물리검층 분야 연구에서 널리 활용되고 있다 (Maiti et al, 2007;Ji et al, 2021;Park et al, 2021). 물리검층 자료로부터 저류층 인자를 추정하는 딥러닝 연 구는 크게 합성 물리검층 자료(synthetic logs)의 생성(음파 검층, 밀도 검층 등)과 저류층 물성(reservoir properties) 추 정(공극률, 포화도 등)으로 나눌 수 있다.…”

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Generation of High-Resolution Well Log Data by Using a Deep-Learning Algorithm

Park¹,

Kwon²,

Ji³

et al. 2022

J. Korean Soc. Miner. Energy Resour. Eng.

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This study proposed a deep-learning-based approach that generates synthetic high-resolution log data from original-resolution log data for accurate reservoir characterization, where the resolution of the synthetic data is comparable to that of core data. The reliability of the proposed approach was tested with application to the Volve oil field in Norway using three deep-learning algorithms (i.e., deep neural network, convolutional neural network, and long short-term memory). These deep-learning algorithms were employed to generate high-resolution sonic log data from other log-type data. The overall performance of each algorithm was acceptable. In particular, the long short-term memory algorithm yields a coefficient of determination greater than 0.9 when the high-to-original-resolution ratios are two, five, and ten. We anticipate that the proposed model can be used to derive logging-based reservoir parameters with a resolution that is comparable to that of core-based reservoir parameters.

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Section: 서 론unclassified