Complex carbonate reservoirs provide many challenges for characterization and modeling not least because diagenetic overprints may lead to increases in heterogeneities on a small scale. This study examines a complex carbonate reservoir from onshore Abu Dhabi where diagenetic overprints have led to the development of high permeability streaks. Additional complication is the presence of a low resistivity pay (LRP), where analysis of resistivity logs has resulted in the calculation of high water saturation which contradicts production tests that confirm dry oil.
This study used a combination of core, thin section, MICP, well logs and dynamic data to develop a holistic and robust reservoir characterization and reservoir model. A methodology was developed specifically to characterize and model the subsurface conditions identified in this field due to the simultaneous existences of high permeability streaks and LRP intervals. The methodology included:
detailed core, thin section and lithofacies description;
high resolution sequence stratigraphy (HRSS) interpretation;
reservoir rock typing (RRT);
assessment of the relationship between lithofacies, diagenetic processes, and RRT;
saturation height function (SHF);
integrated static model building, and;
flow simulation and history match validation.
Three lithofacies were identified using faunal content, texture, sedimentary structures and Dunham Classification. The depositional setting varied from lagoon to shoal. Reservoir Rock Typing (RRT) defined seven rock types based on capillary pressure trend, pore throat distribution and porosity-permeability. HRSS interpretation recognized three 5th order highstand sequences that separated by two transgressive sequences. This has then allowed the identification of the origin of the high permeability streaks and the spatial distribution within the sequence stratigraphic framework. Detailed geological modeling was then integrated with dynamic data to provide a robust dynamic simulation validation. The combination of static and dynamic modeling can then be used to more accurately calculate OOIP and optimize the current reservoir management plan ensuring optimal sweep efficiency and recovery.