Bayesian seismic 4D inversion for lithology and fluid prediction

Seismic data acquired at different times over the same area can provide insight into changes in an oil/gas reservoir. Probabilities for pore fluid will typically change while the lithology remains stable over time. This implies significant correlations across the vintages. We have developed a methodology for Bayesian prediction of joint probabilities for discrete lithology-fluid classes (LFCs) for two vintages, simultaneously considering the seismic AVO data of both vintages. By taking into account cross-vintage correlations of elastic and seismic properties, the simultaneous invertion ensures that the individual results for both vintages as well as their differences are consistent and constrained by the seismic data of both vintages. The method relies on prior geological knowledge of stratigraphic layering, possible lithologies and fluids within each layer, and possible cross-vintage changes in lithology and pore fluid. Multiple LFCs can be used to represent different strengths of dynamic cross-vintage changes. We have tested the algorithm on a synthetic data set and on data from the Edvard Grieg field in the central North Sea. Synthetic results demonstrate that the algorithm is able to use dual-vintage data together with a prior model specifying their correlations to calculate joint LFC posterior probabilities for both vintages with a lower degree of uncertainty than independent single-vintage inversions. The Edvard Grieg results indicate that the underlying model is sufficiently general to explain 4D variations in seismic data using a reasonably simple prior model of 4D LFC changes.