Abstract
As ill-posed optimization process, history matching renders multiple realizations of reservoir models that satisfy a given objective function with applicable constraints. A variety of assisted history matching (AHM) techniques is being developed with the main objective to generate statistically diverse ensembles of history matched models to capture the uncertainty in the distribution of reservoir parameters. This paper targets the outstanding questions on how to a) rigorously quantify the uncertainty in the distribution of the most prominent reservoir parameters that govern the reservoir connectivity and b) rank the history matched models and identifies the model candidates for production forecasting without compromising the notion of uncertainty. A method has been developed that integrates the modules for AHM and Dynamic Model Ranking (DMR) based on forecasted oil recovery factors. Pattern recognition based on kernel -means clustering is used to identify key reservoir models. The reduced set of models minimizes the computational load for forecast-based analysis, while retaining the knowledge of the uncertainty in the recovery factor. The comprehensive probabilistic AHM and DMR workflow was implemented at North Kuwait Integrated Digital Oilfield (KwIDF) collaboration center and delivers an optimized reservoir model for waterflood management.
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