Numerical simulation models have been used to optimize oil recovery since the 1960's. Typical steps to create a simulation model include 1) building a static model based on all available geological and petrophysical data; 2) history matching the static model to tune it to the available production and measured data; and 3) using the conditioned model to design drill-wells, predict pressure evolution, and forecast flow streams for business decisions. Today it is generally accepted that the best models result from using interdisciplinary teams to ensure geologic consistency is maintained during the history matching modifications – e.g. Sibley (1997), Landis (2005). We discuss herein a unique approach to efficiently manage and expedite the history matching of a giant carbonate reservoir using a team of simulation engineers. The workflow was developed based on domain decomposition principles to divide the problem into manageable sector models with coordinated updates between the sector and full field model to obtain the full-field history match. With this approach, dynamic history matching is divided among many simulation engineers in a way that maintains the base geologic model, vets regional modifications, and transmits local lessons to the full-field model. The workflow relies on fast extraction, creation, and management of sector models (or subdomains) from the full field. Each sector is linked to the full field by incorporating flux boundary conditions obtained from either a larger sector model or the full-field model. The proposed approach allows for the acceleration of the history match by effectively dividing the work among a team of simulation engineers. The "sector" approach of making smaller models from one large model speeds up the model build and run times making it convenient to have the multiple iterations necessary to achieve a satisfactory history match in a complex, high-contrast flow strata geologic environment. The goal of the work was to reduce the time to achieve the history match to one year, as opposed to the previous experience requiring several years for similarly sized models. Some critical advantages and lessons from the workflow will be discussed through its application using actual history matching examples from a giant offshore, Middle Eastern, carbonate reservoir having over four decades of production history.

Several giant carbonate reservoirs have undergone decades of waterflooding, and are now transitioning to EOR recovery processes. Simulation models that were calibrated via history matching while undergoing a waterflood (i.e.two phase flow performance) are utilized now to predict three phase flow performance encountered with EOR processes. How reliable are these predictions? Are they accurate enough to be used for business decisions? In this work, validity and reliability of simulation models, that has been history matched by two-phase flow processes of water flooding, to predict the performance of three-phase flow of WAG processes was assessed. To accomplish study objective, fine grid of two 5-spot sectors model was built and then upscaled. Upscaled model was then history matched to the results obtained from the fine model using water flooding data and utilizing pseudo functions data. The resulted cases as well as the fine model were then taken to prediction to estimate the performance of three-phase flow of gas and WAG processes. Results of fine and coarse models were then analyzed and compared to draw conclusions on the reliability of the coarse models to match the predicted results of the reference model of the fine simulation model.