Geologic Storage of CO2

Dynamic imaging provides an effective way to integrate previous time-lapse data with current data in order to estimate a current model. We present two methods useful for continuous dynamic reservoir and subsurface monitoring. The first method is a stochastic method for the inversion of geophysical data. It uses the ensemble Kalman filter (EnKF) that we demonstrate using time-lapse surface seismic data. We use reflection waveform and traveltime data plus sonic log information as input to the EnKF inversion. Unlike conventional seismic methods that give relative impedance, the EnKF inversion yields absolute seismic velocity. The second method is a specific dynamic imaging algorithm we call DynaSIRT. DynaSIRT integrates data from previous surveys in an efficient way, without the reprocessing older data. It preserves state variables that store the temporally damped effective illumination of previous surveys and timestamps to track parameter updates. We show results of DynaSIRT applied to synthetic time-lapse diffraction tomography data, where it provides a clear detection of a CO2 leak even for sparse survey geometry. Lastly, we introduce the method of data evolution to supplement sparse data surveys for continuous monitoring. Data evolution may be used with any dynamic imaging stream to supplement sparse data recordings with predicted data based on spatiotemporal interpolation. And finally, we report herein a feasibility study for the use controlled-source electromagnetics (CSEM) for monitoring geological CO2 storage in coal. We conducted the CSEM study in both the frequency domain and time domain. In order to test various monitoring methods and strategies, we used relatively realistic timelapse seismic and electrical models that were constructed from flow simulation and rock physics.