This paper presents practical approaches to deal with the complex problem of the uncertainty assessment in the performance forecast using reservoir simulation models with extensive production history. The complexity and difficulty of this type of problem arises mainly from the necessity of finding a large number of simulation models that are consistent not only with the geological data but also with the observed production history. In simpler terms this means finding an appropriate number of multiple solutions to the history match problem that can be used to estimate uncertainty in the forecasts. The rigorous solution to this kind of problem involves the application of methods based on Monte Carlo simulation; but they are not routinely applied because of the computational cost associated to the necessary large number of simulations for real field problems.
Advances in computing technology in recent years, especially in the areas of CPU speed and of high performance computing affordability with medium to large CPU clusters, indicate that now is, probably the appropriate time to explore and revisit the practical aspects of performing a more comprehensive history match and forecast uncertainty analysis with Monte Carlo simulation methods.
The approaches presented in this work take advantage of the availability of a medium size 256 CPU Linux cluster that allowed the coupling of distributed high performance computing with efficient sampling techniques to solve the history match and the associated forecast uncertainty problem under a probabilistic inverse problem framework, and to present the results of both history match and forecast in the form of probability density functions (PDF). Prior probabilistic model information is incorporated in the process.
The tests performed with data from a real field indicated that our approaches provide one practical way to address, more comprehensively than current existing approaches, the non-uniqueness issue of the history matching problem and the associated uncertainties in performance forecasts in real fields. Since the results are accomplished in a very short time, significant changes in reservoir management paradigms may result.