Abstract
Summary Stage 2C of the CO2CRC Otway Project features a clear time-lapse seismic response from a small CO2 injection into a deep saline aquifer. The injection revealed some geological features that control the CO2 migration in the reservoir but have very subtle signatures in the baseline data. These snapshots of plume evolution may be used to optimise the seismic attribute analysis for the next CO2 injection into the same reservoir. Instead of time-consuming and somewhat subjective static and dynamic modelling, our methodology relies on a shallow artificial neural network trained to reconstruct the Stage 2C plumes based on a suite of seismic attributes. We assume that importance of a given attribute may be estimated by its effect on prediction accuracy of the neural network. We can gain insight into geological controls on the observed plume migration, by placing the test patches on the different parts of the plume map.
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