An integrated workflow for quantifying the impact of geological uncertainty and modelling decisions on Stoiip estimates and history matching - A case study from the Middle East

An innovative multi-deterministic scenario workflow was applied to one of the giant and complex carbonate reservoirs in the Middle East. The application of this workflow had the objective to quantify how geological uncertainties and different modelling decisions impact the stock tank oil-initially-in-place (STOIIP) estimates and flow behaviour in this reservoir. In particular, we focused on the uncertainties related to the presence of fractures, reservoir rock typing, and modelling the initial hydrocarbon distribution. Based on the available static and dynamic data we considered two key scenarios, the absence of fractures and the presence of sparse, fault-controlled fractures. In the first scenario, we investigated how different reservoir rock typing methods impact permeability distributions. We further quantified changes in hydrocarbon distribution and analysed how a novel approach that combines capillary pressure and log-derived J-function affects the saturation models. In the second scenario, we used the effective medium theory to calculate permeability multipliers for the regions where fractures are expected. This enabled us to effectively represent fractures in a single-porosity reservoir model. The representativeness of the different models was analysed through blind tests using static data as well as history matching using dynamic data. The most significant findings of our work are that subtle changes in modelling decisions and reservoir rock typing have major consequences for the saturation model, leading to up to 20% change in STOIIP estimates. Such uncertainties must be carried forward in future reservoir management decisions and when estimating reserves. The blind tests showed that a saturation model based on the combination of core- and log-derived J-functions gave the most robust STOIIP estimates. These particular saturation models further led to a much-improved history match, especially for wells located in the transition zone of the reservoir. The best history matches were obtained once sparse, fault-controlled fractures were included in the reservoir model using effective medium theory. The presence of fractures specifically improved the history matching quality for wells located close to the faults; these wells were very difficult to match in the past. Our work clearly demonstrates that a multi-deterministic scenario workflow is key to explore the appropriate range of geological uncertainties, and that, equally important, the impact of different modelling decisions must be accounted for when quantifying uncertainty during reservoir modelling. This is particularly applicable to giant carbonate reservoirs where relatively minor changes in the workflow and data interpretation can have major consequences on STOIIP estimates, dynamic behaviours, and reserve estimates. Multi-stochastic modelling workflows which anchor the reservoir to a single base case are not capable of achieving this.