Publications

Carbonate rocks are known as one of the principal reservoir rocks in the world due to their good porosity and permeability. However, the heterogeneity of carbonate reservoir quality is difficult to predict. Variability of diagenetic processes overprinting carbonate depositional texture has resulted in a complex carbonate pore system. As a consequence, this complexity results in a harder reservoir characterization and also a discrepancy between actual and model properties, that leads to a harder history match in reservoir simulation. By presenting a study case from the Drowning Cap Sequence in the VITA Reservoir Block Cepu, this paper will present a comprehensive approach which focusing on characterization of a carbonate pore system for optimum geomodel, simulation and surveillance. This approach utilized static data of 100 ft total of cores, ±500 thin sections, well, and image logs. The study has resulted in definition of four Carbonate Reservoir Rock Types (RRT) which were clustered using the analysis of carbonate dominant pore types and the porosity-permeability relationship. Results revealed that there are 4 RRTs observed as follows: (1) RRT 1 – Touching Vugs-dominated, with high porosity and permeability, (2) RRT 2 – Interparticle- and Moldic-dominated, with moderate to high porosity value and lower permeability than RRT 1, (3) RRT 3 – Microfracture-dominated, with very low porosity value and low to moderate permeability, and (4) RRT 4 – Minimum Dissolution, with very low porosity and permeability value, lower than RRT 3. Each RRT was integrated with well and image logs to understand its characteristics and behavior. Ultimately, all data were integrated, analyzed and successfully captured carbonate reservoir quality variation, distribution and depositional evolution along with overprinted diagenetic processes vertically and laterally. This approach successfully captured carbonate heterogeneity which ultimately will be useful to develop better geological and reservoir simulation models after being integrated with dynamic data and observations.