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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This research paper explores how very fast screening methods, so-called “flow diagnostics”, can help us explore how a realistic range of geological uncertainties and model scenarios impact flow behaviours in a complex carbonate reservoir. Flow diagnostics were implemented using MATLAB® Reservoir Simulation Toolbox (MRST). MRST provides the functionality to compute approximate dynamic properties (e.g. time-of-flight) directly on the grid of the reservoir model. A synthetic but geologically realistic sector-scale reservoir model, which serves as an analogue for the Arab D formation, was used to learn and establish the flow diagnostics workflow in MRST. An analysis of the uncertainties inherent to carbonate reservoir rock typing and their effect on reservoir performance is presented along with an analysis of the impact of wettability and mobility on reservoir performance. In addition a comparison of different metrics which approximate reservoir dynamics in carbonate reservoirs is given. Testing in MRST shows that flow diagnostic simulations take negligible time (i.e. in seconds), hence a large number of model scenarios and realizations can be explored and ranked based on their dynamic behaviour. This in turn will enable us to select appropriate models for full-physics simulations that capture the full uncertainty inherent in the reservoir description.@en

This study explores how very fast screening methods, so called "flow diagnostics", can help us to explore how a realistic range of geological uncertainties and model scenarios impact flow behaviours in a complex carbonate reservoir. Flow diagnostics are normally performed using streamline simulations but here we use an implementation in the Matlab Reservoir Simulation Toolbox (MRST) that allows us to compute approximate dynamic properties (e.g. time-of-flights and dynamic Lorenz coefficients) directly on the grid of the reservoir model. We show that the flow diagnostic simulations take negligible time (i.e. on the order of seconds). Hence a large number of model scenarios and realisations can be explored, which allows us to investigate for more models how geology and well placements impact reservoir behaviors, and subsequently rank and cluster the models based on their dynamic behaviors. This in turn will enable us to select appropriate models for full-physics simulations that capture the full uncertainty inherent to the reservoir description and helps us to develop better reservoir management approaches.@en