The variations of dynamic reservoir properties cause the change in seismic response. During the production phase, time-lapse seismic data can be used to monitor water saturation and pressure changes. The prediction of water saturation and pressure conditions from seismic datarequires physical model to link their changes to variations in elastic properties. The empirical models commonly used constant empirical coefficients in the reservoir. However, in first part of the work, we show that different porosity, saturation, and pressure in in-situ conditions can affect the model coefficients. We then propose a new rock physics model to compute the changes in reflectivity due to thechanges in saturation and pressure, accounting in-situ reservoir conditions. The model is then integrated in a Bayesian inversion method to predict water saturation and pressure changes directly from the amplitude difference of time-lapse seismic data. We apply the proposed method to a synthetic dataset and obtain accurate results.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 209A (Anaheim Convention Center)
Presentation Type: Oral