Uncertainty Quantification of Forecasted Oil Recovery using Dynamic Model Ranking with Application to a ME Carbonate Reservoir

History matching, being an ill-posed optimization problem, attempts to render multiple realizations of reservoir models that satisfy a given objective function with applicable constraints. A variety of assisted history-matching (AHM) techniques is currently being developed and used 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 of 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 identify the model candidates for production forecasting without compromising the notion of uncertainty. A workflow has been developed that integrates the modules for AHM and dynamic model ranking (DMR) based on forecasted oil recovery factors (ORFs). A pattern recognition methodology based on a kernel k-means clustering algorithm is used to identify key reservoir models. The reduced set of models is used to minimize 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 the operator’s North Kuwait Integrated Digital Oilfield (KwIDF) collaboration center. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with geological, production, and completion information.