Monte Carlo reservoir analysis combining seismic reflection data and informed priors

Determination of a petroleum reservoir structure and rock bulk properties relies extensively on inference from reflec-tion seismology. However, classic deterministic methods to invert seismic data for reservoir properties suffer from some limitations, among which are the difficulty of handling com-plex, possibly nonlinear forward models, and the lack of ro-bust uncertainty estimations. To overcome these limitations, we studied a methodology to invert seismic reflection data in the framework of the probabilistic approach to inverse prob-lems, using a Markov chain Monte Carlo (McMC) algorithm with the goal to directly infer the rock facies and porosity of a target reservoir zone. We thus combined a rock-physics model with seismic data in a single inversion algorithm. For large data sets, the McMCmethod may become computation-ally impractical, so we relied on multiple-point-based a priori information to quantify geologically plausible models. We tested this methodology on a synthetic reservoir model. The solution of the inverse problem was then represented by a collection of facies and porosity reservoir models, which were samples of the posterior distribution. The final product in-cluded probability maps of the reservoir properties in ob-tained by performing statistical analysis on the collection of solutions