In this study we have applied new dual-porosity flow diagnostics to a recently discovered offshore fractured carbonate reservoir undergoing charactersiation studies. Carbonate reservoirs are typically highly hetrogeneous, naturally fractured and often mixed to oil wet, all of these factors are uncertain and can negatively impact upon recovery. With few wells drilled at the time of this study significant uncertainty hinders robust decision making. A robust multi-realisation approach is rendered impractical as the run time for a single realisation of the dual-porosity model is in excess of several days. Instead of using brute force we have utilised grid based flow diagnostics as a fast screening tool to select reservoir models for further full-physics simulations. The CPU time of flow diagnostics is almost negligable. Flow diagnostics are numerical tests perfomed on the static model that provide the time-of-flight, tracer partitions, drained/swept volumes and well pairs. In addition, dual-porosity metrics link the advective flow in the fractures to transfer from the matrix, indicating regions where flow and transfer are unbalanced and hence at risk of early breakthrough.

Over 30 flow diagnostic tests were performed in under 10 minutes, the equivalent screening would take weeks using simulation. Results have shown that the fracture intensity and wettability are the most signifcant uncertainties that impact upon transfer and recovery, this effect would be missed by an equivalent single-porosity model. Well placement in this reservoir is very sensitive; the results show the proposed placement is effective for the assumed yet uncertain facies distribution. This broad sensitity screening has guided the ongoing modelling strategy, in partcular pinpointing the need for detailed characterisation of the fracture and facies distributions. Flow diagnostics are hence an excellent way to complement production forecasting workflows by providing a tool for quickly ranking and selecting scenarios for further detailed full-physics simulation, allowing us to focus more computational resources on reservoir models that are of particular interest.


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