History Matching and Uncertainty Quantification Assisted by Global Optimization Techniques

Ranges in production forecast provide critical information for reservoir management decisions. Well developed methodologies exist for handling subsurface uncertainties for new field developments. The task is more challenging for fields that have been produced for several years as all models need to be conditioned to available production data in order to deliver reliable predictions. The computing cost associated with the exhaustive search of models that reproduce historical data is in general prohibitive. This paper describes an efficient method that combines the strength of various techniques, including optimization algorithms, experimental design and non-linear response surfaces. It is applied to an off-shore field in the Philippines for which multiple history-matched models are obtained in a reasonable timeframe.