Time-lapse Fluid Prediction Using Selection Gaussian Prior Models in a Bayesian Framework

Management of oil reservoirs rely upon trustworthy information about their condition. We develop statistical methodology for reliable characterization of the porosity and fluid distribution in a reservoir based on time -lapse seismic AVO data. Of particular interest to us is characterization of the dynamic fluid filling, which is crucial for reservoir engineering management, including the design of efficient infill well drilling programs. The porosity is assumed to be time constant, while the water saturation is dynamic. The objective of the study is to characterize the reservoir at two timepoints given the seismic data. We approach the problem in a Bayesian spatial inversion setting. The solution is then defined by a posterior probability distribution. A prerequisite to obtain a solution as such is a suitable prior model for the reservoir characteristics and a seismic likelihood model for the seismic data, which both have to be specified. The seismic likelihood model is assumed to be Gauss-linear and relies upon known geophysical relations. Since the water saturation is bimodal within the reservoir, the fluid filling being either oil or water, specifying a prior model is challenging. We propose a solution using a selection Gaussian random field prior model.