[논문]심층학습을 이용한 전이대 두께 예측

장성형; 이동훈; 김병엽

doi:10.7582/gge.2023.26.4.199

[국내논문] 심층학습을 이용한 전이대 두께 예측
Thickness Estimation of Transition Layer using Deep Learning 원문보기

지구물리와 물리탐사 = Geophysics and geophysical exploration, v.26 no.4, 2023년, pp.199 - 210

장성형 (한국지질자원연구원 해저지질에너지연구본부) , 이동훈 (한국지질자원연구원 해저지질에너지연구본부) , 김병엽 (한국지질자원연구원 해저지질에너지연구본부)

초록
AI-Helper

CO₂ 주입 후 저류층은 암석물리 특성이 변하므로 이 연구에서는 저류층을 물성이 선형으로 변하는 전이대 지층모델로 구성한다. 울프 반사계수 함수는 전이대 상하지층의 속도비, 주파수, 전이대 두께 함수로 구성되어 있어 저류층 두께나 해저면 전이대 두께를 추정하는데 활용할 수 있다. 이 연구에서는 심층학습을 이용하여 전이대 두께를 예측 방법을 제안한다. 심층학습을 적용하기 위해 사암 저류층, 셰일 덮개암으로 구성한 인공 전이대 지층모델에 두께에 따른 울프 반사계수 모델링을 수행하고 시간-스펙트럼 영상자료를 확보하였다. 두께별 시간-주파수 스펙트럼 영상과 중합단면도 트레이스에서 구한 시간-주파수 스펙트럼 비교로부터 구한 두께 추정결과는 항상 정확하게 전이대의 두께를 제시하지는 못하였다. 그러나 다양한 환경에서 학습자료를 확보하고 정확도를 높이면 현장자료적용이 가능할 것으로 본다.

Abstract ▼ AI-Helper

The physical properties of rocks in reservoirs change after CO2 injection, we modeled a reservoir with a transition zone within which the physical properties change linearly. The function of the Wolf reflection coefficient consists of the velocity ratio of the upper and lower layers, the frequency, and the thickness of the transition zone. This function can be used to estimate the thickness of a reservoir or seafloor transition zone. In this study, we propose a method for predicting the thickness of the transition zone using deep learning. To apply deep learning, we modeled the thickness-dependent Wolf reflection coefficient on an artificial transition zone formation model consisting of sandstone reservoir and shale cap rock and generated time-frequency spectral images using the continuous wavelet transform. Although thickness estimation performed by comparing spectral images according to different thicknesses and a spectral image from a trace of the seismic stack did not always provide accurate thicknesses, it can be applied to field data by obtaining training data in various environments and thus improving its accuracy.

Keyword

표/그림 (13)

그림 Fig. 1. Transition layer model. V is the velocity of the transition layer, and V₁ and V₂ are the velocities above and below the transition layer.
그림 Fig. 2. Wolf reflectivity modeling at a 10 m thick transition zone for a water bottom model. Each arrow indicates the zero-crossing frequency, which is the phase-change point.
그림 Fig. 3. Wolf reflectivity modeling at a 50 m thick transition zone for a water bottom model. Each arrow indicates the zero-crossing frequency, which is the phase-change point.
그림 Fig. 4. Wolf reflectivity modeling at a 10 m thick transition zone for a reservoir model.
그림 Fig. 5. Wolf reflectivity modeling at a 50 m thick transition zone for a reservoir model.
그림 Fig. 6. Time-frequency spectrum for different thicknesses of transition layers with a water bottom model. Each arrow shows the estimattedcutoff frequency.
그림 Fig. 7. VGGNet-19 model architecture comprised of 16 convolutional layers, seven max poolings, and three fully connected layers.
그림 Fig. 8. Time-frequency spectrum for transition layers with different thickness. The number on the top of each image is the transition layer thickness within the reservoir model.
그림 Fig. 9. Time-frequency spectrum for different signal-to-noise ratio levels when the transition thickness is 30 m.
그림 Fig. 10. Distribution of the time-frequency spectrum images. The dataset is divided with a training, validation, test ratio of 64%, 16%, and 20%, respectively. The total number of images with 23 classes is 2,231.
그림 Fig. 12. Loss and accuracy function after 400 epochs.
그림 Fig. 11. Confusion matrix: The vertical axis is each thickness's true label, and the horizontal axis is for the predicted label.
표 Table 1. Results of training using deep learning, VGGNet-19.

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표제어: PCR

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