Assessing Uncertainty in Reservoir Prediction by Monte Carlo Methods

Thursday, January 10, 2002 - 9:30am - 10:30am
Keller 3-180
Dean Oliver (University of Tulsa)
Monte Carlo methods provide the most general methods for quantifying uncertainty in subsurface processes. Their main disadvantage is the computational expense of generating a sufficiently large number of conditional realizations for approximation of the probability density of predictions. In this presentation, I will discuss some of the features needed for an efficient Markov chain Monte Carlo method and how minimization (or calibration or history matching) can be used to improve the efficiency of MCMC.

An approximate algorithm will then be described, along with a discussion of the difficulties of placing it into an MCMC context. Numerical experiments will show, however, that the approximate algorithm is useful for quantifying uncertainty in subsurface processes.