A Bayesian model for gas saturation estimation using marine seismic AVA and CSEM data

We develop a Bayesian model to jointly invert marine seismic amplitude versus angle (AVA) and controlled-source electromagnetic (CSEM) data for a layered reservoir model. We consider the porosity and fluid saturation of each layer in the reservoir, the bulk and shear moduli and density of each layer not in the reservoir, and the electrical conductivity of the overburden and bedrock as random variables. We also consider prestack seismic AVA data in a selected time window as well as real and quadrature components of the recorded electrical field as data. Using Markov chain Monte Carlo (MCMC) sampling methods, we draw a large number of samples from the joint posterior distribution function. With these samples, we obtain not only the estimates of each unknown variable, but also various types of uncertainty information associated with the estimation. This method is applied to both synthetic and field data to investigate the combined use of seismic AVA and CSEM data for gas saturation estimation. Results show that the method is effective for joint inversion; the incorporation of CSEM data reduces uncertainty in fluid saturation estimation compared to inversion of seismic AVA data alone. The improvement in gas saturation estimation obtained from joint inversion for field data is less significant than for synthetic data because of the large number of unknown noise sources inherent in the field data.