Geophysics and Geophysical Exploration (지구물리와물리탐사)

Korean Society of Earth and Exploration Geophysicists (한국지구물리·물리탐사학회)
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Thickness Estimation of Transition Layer using Deep Learning

심층학습을 이용한 전이대 두께 예측

  • 장성형 (한국지질자원연구원 해저지질에너지연구본부) ;
  • 이동훈 (한국지질자원연구원 해저지질에너지연구본부) ;
  • 김병엽 (한국지질자원연구원 해저지질에너지연구본부)
  • Received : 2023.09.21
  • Accepted : 2023.11.06
  • Published : 2023.11.30
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Abstract

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.

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.

Keywords

Acknowledgement

이 논문은 2023년도 정부(산업통상자원부)의 재원으로 한국에너지기술평가원의 지원을 받아 수행한 연구입니다(20226A10100030).

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