Quantifying Uncertainties Associated With Reservoir Simulation Studies Using a Surrogate Reservoir Model

Reservoir simulation is routinely used as a reservoir management tool. The static model that is used as the basis for simulation is the result of an integrated effort that usually includes the latest geological, geophysical and petro-physical measurements and interpretations. As such, it is inherently a model with some uncertainty. Analysis of these uncertainties and quantification of their effects on oil production and water cut using a new and efficient technique is the subject of this paper. Typical uncertainty analysis techniques require many realizations and runs of the reservoir model. In the day and age that reservoir models are getting larger and more complicated, making hundreds or sometimes thousands of simulation runs can put considerable strain on the resources of an asset team. This paper summarizes the results of uncertainty analysis on a giant oil field in the Middle East using a new technique that incorporates a Surrogate Reservoir Model (SRM). A Surrogate Reservoir Model that runs and provides results in real-time is developed to mimic the capabilities of a full field model that includes about one million grid blocks and takes 10 hours to run on a cluster of twelve 3.2 GHz CPUs. This Surrogate Reservoir Model is used as the objective function of a Monte Carlo Simulation to study the impact of the uncertainties associated with several parameters on the model outcome, i.e. oil production and water cut is analyzed. The analysis can be performed individually on each of the 165 horizontal wells. During the analyses of uncertainty, the Surrogate Reservoir Model will serve as an objective function for the Monte Carlo Simulation. In this study, uncertainties associated with several reservoir parameterts and their quantitative effect on cumulative oil production and stantaneous water cut are examined.