Application of Deep Learning in the estimation of CO2 saturation maps

CO2 saturation estimation during the monitoring of a carbon-dioxide storage project using traditional inversion techniques can become an intensive task due to the processing steps and inversion workflows involved. In our work, we propose a deep-learning algorithm based on the U‐Net architecture, to develop a method for automatic prediction of the spatial distribution of CO2 saturation from time-lapse seismic data. We suggest using continuous wavelet transform (CWT) as feature extraction from the recorded shot gathers. CWT could provide more informative and distinguishable images. The method is tested on synthetic time-lapse data generated from the use of Gassmann fluid substitution equation to calculate the post-injection P-wave velocity model used to perform the 2D finite-difference acoustic modelling. The neural network has been trained on pairs of synthetic seismic shot gathers and corresponding CO2 saturation maps, along with pairs of CWT coefficient maps and CO2 saturation maps. The results of this work indicate the prospective utility of CWT in producing more informative images for the prediction of CO2 saturation maps through deep learning algorithm like U-net.