A Consistent Workflow to Re-evaluate the Hydrocarbon Distribution in a Giant Carbonate Reservoir in the Middle East
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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.
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.
A particular challenge inherent to carbonate reservoirs is reservoir rock typing which impacts model initialisation and saturation distributions and hence STOIIP, phase mobilities, and flow behaviours. We explore how flow diagnostics can be used best to detect subtle differences in reservoir dynamics arising from different model initialisations by comparing flow diagnostics simulations with full-physics simulations. Flow diagnostics are applied to two reservoirs, a synthetic but realistic model representing an analogue for the Arab-D formation and a giant carbonate reservoir from the Middle East. Saturation modelling and reservoir rock typing is based on uniform and heterogeneous Pc and kr distributions, and further employs a state-of-the-art software that integrates of SCAL data and log-derived saturations. Sweep efficiency and dynamic Lorenz coefficients are then derived from the flow diagnostics results to quantify and compare the dynamic behaviour of the reservoir models. The full-physics simulations, which are used to validate the flow diagnostics results, are carried out with a commercial Black Oil simulator. The flow diagnostics results can clearly distinguish between different homogenous and heterogeneous rock-type distributions, wettability trends, as well as novel saturation modelling approaches that use dedicated software tools. Flow diagnostics capture the same trends in recovery predictions as the full-physics simulations. Importantly though, the total CPU time for a single flow diagnostics calculation including model loading is on the order of seconds, compared to minutes and hours for a single full-physics simulation. These observation give confidence that flow diagnostics can be used effectively to compare and contrast the impact of reservoir rock typing, saturation modelling, and model initialisation on reservoir performance before running full-physics simulations. Flow diagnostic hence allow us to reduce the number of reservoir models from a model ensemble and select a small number of diverse yet realistic reservoir models that capture the full range of geological uncertainties which are then subjected to more detailed reservoir simulation studies. Flow diagnostics are particularly well suited for complex carbonate reservoirs which are geologically more complex than clastic reservoirs and often exhibit significant uncertainties. Giant carbonate reservoirs are also challenging to simulate using full-physics simulators due to their size, so the impact of geological uncertainty on the predicted reservoir performance is often underexplored. Flow diagnostics are hence an effective complement to quantify uncertainty in state-of-the-art reservoir modelling, history matching and optimisation workflows, particularly for giant carbonate reservoirs.
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