Real-time deep-learning inversion of seismic full waveform data for CO₂ saturation and uncertainty in geological carbon storage monitoring

Journal Article · Thu Apr 07 04:00:00 EDT 2022 · Geophysical Prospecting

DOI:https://doi.org/10.1111/1365-2478.13197· OSTI ID:1865313

Um, Evan Schankee ^[1]; Alumbaugh, David ^[1]; Lin, Youzuo ^[2]; Feng, Shihang ^[2]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO₂ saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO₂ saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO₂ saturation models and once fully trained directly estimates CO₂ saturation as a function of subsurface location. We further quantify uncertainties of CO₂ saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO₂ training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO₂ fairly well even in the presence of Gaussian random noise and (2) both CO₂ saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Fossil Energy (FE)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1865313
• Alternate ID(s): OSTI ID: 1872349
• Journal Information: Geophysical Prospecting, Journal Name: Geophysical Prospecting Journal Issue: 1 Vol. 72; ISSN 0016-8025
• Publisher: Wiley
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
full waveform
inversion
monitoring