Machine Learning-based 4-D Seismic Data Integration and Characterization of Channelized Anticline Aquifer for Geological Carbon Sequestration

Hyunmin Kim; Namhwa Kim; Hyundon Shin; Honggeun Jo

doi:10.32390/ksmer.2024.61.1.001

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2024 Vol.61, Issue 1 Next Page

Research Paper

Machine Learning-based 4-D Seismic Data Integration and Characterization of Channelized Anticline Aquifer for Geological Carbon Sequestration 이산화탄소 지중저장을 위한 기계학습 기반 4-D 탄성파자료 통합 및 배사구조 채널대수층 특성화

28 February 2024. pp. 1-14

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Abstract

This study introduces a 4-D seismic data integration approach based on Pix2Pix to quantify uncertainty and enhance reliability in subsurface CO₂ storage within channelized aquifers. We address geological uncertainties, such as the azimuth of channel direction, using multi-point statistics to generate diverse channelized aquifer models, to form the Pix2Pix training dataset. After successfully training the Pix2Pix, we realize the channelized aquifer models by assimilating 4-D seismic data changes from a reference channelized aquifer. The Pix2Pix dataset is crafted with (1) disconnections in artificial neural networks using dropout and (2) the addition of random noise to the input layer, yielding an ensemble of models that align with the provided data for quantifying uncertainty. Generating channelized aquifers using the suggested method improves prediction of channel direction, facies distribution, and accurate forecasts of future CO₂ plume movement. As an advanced monitoring technology, our method integrates 4-D seismic data for effective CO₂ storage in channelized aquifers.

Keywords

carbon capture and sequestration

time-lapse seismic image

channelized aquifer characterization

Pix2Pix

본 연구에서는 채널대수층의 이산화탄소 지중저장에서 4-D 탄성파자료를 통합해 불확실성을 정량화하고 신뢰도를 향상하기 위해 기계학습의 하나인 Pix2Pix 기반의 4-D 탄성파자료 통합기법을 제안한다. 다점지구통계 기법으로 다양한 지질학적 시나리오를 따르는 2차원 채널대수층 모델을 바탕으로 Pix2Pix을 학습시킨 후, 참조 채널대수층의 4-D 탄성파 변화를 입력하여 채널대수층 모델을 특성화한다. Pix2Pix을 구성하는 인공신경망 모델의 일부 연결을 끊는 (1) dropout을 이용하는 방법과 (2) 인공신경망의 입력층에 랜덤 노이즈 맵을 추가하는 방법을 이용해 주어진 자료를 만족하며 지질학적 사실성을 보전하는 다양한 모델을 생성하고 이들로부터 불확실성을 정량화 한다. Pix2Pix을 이용해 생성된 채널대수층은 참조 모델의 채널 방향성과 암상분포의 예측을 개선하였고, 나아가 미래 이산화탄소 액적군의 이동을 정확하게 예측한다. 제안한 방법은 채널대수층에서 이산화탄소 지중저장을 하는 경우 4-D 탄성파자료를 통합해 불확실성을 평가함으로 이산화탄소 지중저장의 모니터링기술로 활용될 수 있다.

키워드

이산화탄소 포집 및 지중저장

시간경과 탄성파 이미지

채널대수층 특성화

Pix2Pix

References

Ahn, Y. and Choe, J., 2022. Reliable channel reservoir characterization and uncertainty quantification using variational autoencoder and ensemble smoother with multiple data assimilation, Journal of Petroleum Science and Engineering, 209, 109816. 10.1016/j.petrol.2021.109816

Bennion, D.B. and Bachu, S., 2005. Relative permeability characteristics for supercritical CO₂ displacing water in a variety of potential sequestration zones in the western Canada sedimentary basin, In SPE Annual Technical Conference and Exhibition, SPE-95547. 10.2523/95547-MS

Bennion, D.B. and Bachu, S., 2007. Permeability and relative permeability measurements at reservoir conditions for CO₂-water systems in ultralow-permeability confining caprocks, In SPE Europec featured at EAGE Conference and Exhibition, SPE-106995. 10.2523/106995-MSPMC2258660

Cheong, S., Kim, B., Park, Y., Park, Y., Kim, D., and Kim, H., 2022. Test processing of seismic monitoring using Sleipner 4D data, Journal of the Korean Society of Mineral and Energy Resources Engineers, 59(1), p.31-41. 10.32390/ksmer.2022.59.1.031

Computer Modelling Group, 2020. GEM user's guide version 2020, CMG, Canada.

Deutsch, C.V. and Journel, A.G., 1992. Geostatistical software library and user's guide, New York, 119(147), p.578.

Furre, A.K., Eiken, O., Alnes, H., Vevatne, J.N., and Kiær, A.F., 2017. 20 years of monitoring CO₂-injection at Sleipner, Energy procedia, 114, p.3916-3926. 10.1016/j.egypro.2017.03.1523

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y., 2014. Generative adversarial nets, In Neural Information Processing Systems, p.2672-2680.

He, K., Zhang, X., Ren, S., and Sun, J., 2016. Deep residual learning for image recognition, In Proceedings of the IEEE conference on computer vision and pattern recognition, p.770-778. 10.1109/CVPR.2016.9026180094

Hovorka, S.D., Doughty, C.K., Sakurai, S., and Holtz, M., 2005. Frio brine pilot: field validation of numerical simulation of CO₂ storage, In Abstract, AAPG Annual Convention, Calgary, Alberta, Canada, p.1-23.

International Energy Agency (IEA), 2023. Net zero roadmap a global pathway to keep the 1.5°C goal in reach, IEA, Paris, France, 226p.

Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A., 2017. Image-to-image translation with conditional adversarial networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.1125-1134. 10.1109/CVPR.2017.632

Jo, H., Cho, Y., Pyrcz, M., Tang, H., and Fu, P., 2022. Machine-learning-based porosity estimation from multifrequency poststack seismic data, Geophysics, 87(5), p.217-233. 10.1190/geo2021-0754.1

Jo, H., Jung, H., Ahn, J., Lee, K., and Choe, J., 2017. History matching of channel reservoirs using ensemble Kalman filter with continuous update of channel information, Energy Exploration & Exploitation, 35(1), p.3-23. 10.1177/0144598716680141

Jo, H., Pan, W., Santos, J.E., Jung, H., and Pyrcz, M.J., 2021. Machine learning assisted history matching for a deepwater lobe system, Journal of Petroleum Science and Engineering, 207, 109086. 10.1016/j.petrol.2021.109086

Kim, J.W. and Nam, M.J., 2012. A review on monitoring the behavior and saturation of CO₂ at a CO₂ injection field, Nagaoka, Japan, Journal of the Korean Society for Geosystem Engineering, 49(5), p.677-688.

Kim, S., Lee, K., Lim, J., Jeong, H., and Min, B., 2020. Development of ensemble smoother-neural network and its application to history matching of channelized reservoirs, Journal of Petroleum Science and Engineering, 191, 107159. 10.1016/j.petrol.2020.107159

LeCun, Y. and Bengio, Y., 1995. Convolutional networks for images, speech, and time series, The handbook of brain theory and neural networks, 3361(10), p.255-258.

Lee, K.B., Jung, S.P., and Choe, J.G., 2015. Uncertainty quantification of channelized reservoirs using ensemble smoother with a distance-based method, Journal of the Korean Society of Mineral and Energy Resources Engineers, 52(2), p.139-147. 10.12972/ksmer.2015.52.2.139

Lee, K., Kim, S., Choe, J., Min, B., and Lee, H.S., 2019. Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image, Petroleum Science, 16, p.127-147. 10.1007/s12182-018-0254-x

Market Report Worlds, 2022. Global carbon capture and storage (CCS) market insights, forecast to 2028, Pune, India, 115p.

Pan, W., Chen, J., Mohamed, S., Jo, H., Santos, J.E., and Pyrcz, M.J., 2023. Efficient subsurface modeling with sequential patch generative adversarial neural networks, In SPE Annual Technical Conference and Exhibition, SPE-214985-MS 10.2118/214985-MS

Pan, W., Jo, H., Santos, J.E., Torres-Verdín, C., and Pyrcz, M.J., 2022. Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling, AAPG Bulletin, 106(11), p. 2163-2186. 10.1306/05162221022

Park, H. and Jin, J., 2023. Carbon neutrality/green growth national strategy and the first national basic plan and carbon neutrality in the urban sector (in Korean), Urban planners, 10(3), p.5-8.

Pyrcz, M.J., Jo, H., Kupenko, A., Liu, W., Gigliotti, A.E., Salomaki, T., and Santos, J., 2021. GeostatsPy python package. Python Package Index, https://pypi.org/project/geostatspy

Radford, A., Metz, L., and Chintala, S., 2015. Unsupervised representation learning with deep convolutional generative adversarial networks, arXiv preprint arXiv:1511.06434.

Ronneberger, O., Fischer, P., and Brox, T., 2015. Convolutional networks for biomedical image segmentation, In International Conference on Medical Image Computing and Computer-assisted Intervention, p.234-241. 10.1007/978-3-319-24574-4_28

Sato, K., Mito, S., Horie, T., Ohkuma, H., Saito, H., Watanabe, J., and Yoshimura, T., 2011. Monitoring and simulation studies for assessing macro-and meso-scale migration of CO₂ sequestered in an onshore aquifer: experiences from the Nagaoka pilot site, Japan, International Journal of Greenhouse Gas Control, 5(1), p.125-137. 10.1016/j.ijggc.2010.03.003

Sharma, S., Cook, P., Berly, T., and Anderson, C., 2007. Australia's first geosequestration demonstration project-the CO₂CRC Otway basin pilot project, The APPEA Journal, 47(1), p.259-270. 10.1071/AJ06017

Statista, 2023.11.29., https://www.statista.com/statistics/726624/large-scale-carbon-capture-and-storage-projects-worldwide-by-status/#:~:text=There%20were%20approximately%20265%20projects,are%20located%20in%20North%20America

Tang, H., Fu, P., Jo, H., Jiang, S., Sherman, C.S., Hamon, F., Azzolina, N.A., and Morris, J.P., 2022. Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR, International Journal of Greenhouse Gas Control, 120, 103765. 10.1016/j.ijggc.2022.103765

Tang, H., Fu, P., Sherman, C.S., Zhang, J., Ju, X., Hamon, F., Azzolina, N.A., Kurton-Kelly, M., and Morris, J.P., 2021. A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage, International Journal of Greenhouse Gas Control, 112, 103488. 10.1016/j.ijggc.2021.103488

Wang, Z., Bai, B., Long, Y., and Wang, L., 2019. An investigation of CO₂-responsive preformed particle gel for conformance control of CO₂ flooding in reservoirs with fractures or fracture-like channels, SPE Journal, 24(05), p.2398-2408. 10.2118/197046-PA

Xue, Z., Tanase, D., and Watanabe, J., 2006. Estimation of CO₂ saturation from time-lapse CO₂ well logging in an onshore aquifer, Nagaoka, Japan, Exploration Geophysics, 37(1), p.19-29. 10.1071/EG06019

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 : 61
No :1
Pages :1-14
Received Date : 2023-12-01
Revised Date : 2024-01-17
Accepted Date : 2024-02-27
DOI :https://doi.org/10.32390/ksmer.2024.61.1.001

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

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