TY - JOUR
T1 - Estimating CO2 saturation maps from seismic data using deep convolutional neural networks
AU - Leong, Zi Xian
AU - Zhu, Tieyuan
AU - Sun, Alexander Y.
N1 - Funding Information:
We thank the Department of Energy for the supporting this research.
Publisher Copyright:
© 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.
PY - 2022/8/15
Y1 - 2022/8/15
N2 - Estimating CO2 saturation from seismic data can be laborious due to the extensive processing steps and inversion workflows involved. We propose and develop a convolutional neural network (CNN) based inversion algorithm, in which the CNN takes in raw seismic data, and directly outputs the CO2 saturation map. This method is efficient and effective during inference. Our CO2 saturation dataset is crafted based on Cranfield CO2 pilot site geological model. We use viscoacoustic seismic modeling to include seismic velocity and attenuation dynamics caused by the replacement of brine by CO2 fluids in the generated seismic data. These steps encourage the CNN to learn from dataset which mimics field settings. Our CO2 saturation prediction results show high accuracies. In the future, we plan to test this method on a field dataset.
AB - Estimating CO2 saturation from seismic data can be laborious due to the extensive processing steps and inversion workflows involved. We propose and develop a convolutional neural network (CNN) based inversion algorithm, in which the CNN takes in raw seismic data, and directly outputs the CO2 saturation map. This method is efficient and effective during inference. Our CO2 saturation dataset is crafted based on Cranfield CO2 pilot site geological model. We use viscoacoustic seismic modeling to include seismic velocity and attenuation dynamics caused by the replacement of brine by CO2 fluids in the generated seismic data. These steps encourage the CNN to learn from dataset which mimics field settings. Our CO2 saturation prediction results show high accuracies. In the future, we plan to test this method on a field dataset.
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U2 - 10.1190/image2022-3746727.1
DO - 10.1190/image2022-3746727.1
M3 - Conference article
AN - SCOPUS:85146425775
SN - 1052-3812
VL - 2022-August
SP - 510
EP - 514
JO - SEG Technical Program Expanded Abstracts
JF - SEG Technical Program Expanded Abstracts
T2 - 2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022
Y2 - 28 August 2022 through 1 September 2022
ER -