Description
Facing the current challenge of oil industry, modern and efficient reservoir management is necessary. Making the right decision of reservoir development utilizing all available data in a timely manner is the key of successful operation. For mature reservoirs, this requires high quality uncertainty assessment of long term performance forecast estimations. The most difficult component of the total uncertainty in forecast is the one that stems from the implicit uncertainty in the geological and reservoir simulation model. In fact, regardless the amount of reservoir data that we collect, there is no way to define uniquely the reservoir model. It is thus necessary to work in an integrated probabilistic framework; and incorporating production data into the reservoir model is an important step to reduce the associated uncertainty in reservoir characterization and performance forecast. The technical challenge is in obtaining probabilistic description of the reservoir models. For mature reservoirs, this implies finding not one, but a large number of reservoir models that are consistent not only with the geological data but also with the production data. Applying smart sampling techniques combined with Monte Carlo simulation within a probabilistic framework, and utilizing available high performance computing resources, it is feasible to find multiple solutions to the history matching problem that can be used to estimate uncertainty for making management decision in a realistic time frame. This presentation will demonstrate the practical approach to solve this critical problem using field examples.