4D Facies Inversion for CCS Monitoring

Abstract

Summary Accurate characterisation of CCS seismic 4D datasets is important to enable operational adjustments during CO2 injection schemes and provide assurance for the long-term geological storage of CO2. We use a monitoring technique normally used for oil and gas reservoirs and apply it to detecting CO2 presence and saturation. The facies-based Bayesian inversion used offers advantages over traditional simultaneous pre-stack inversion, primarily avoiding the construction of low-frequency models, especially when any existing well data will not contain any in-situ/measured CO2 saturations before CO2 injection commences. To implement a facies-based inversion method, we adjust the model parameterization to the ratio of monitor to baseline elastic properties for any given pair of baseline and monitor surveys. The set of facies is reduced to those corresponding to specific changes in CO2 saturation between the monitor and baseline acquisitions. The elastic properties are modelled through rock physics relationships. The inversion operates on the difference of the angle stacks directly, and hence requires properly calibrated and registered baseline and monitor data. Using seismic differences also reduces the complexity of the inversion problem and improves the signal-to-noise and detectability of the CO2. We demonstrate the technique on a synthetic example based on the UK Endurance CCS site.