Carbonate reservoirs represent some of the world's largest and best known oil and gas accumulation, holding more than 50-60% of the world's oil and more than 45% of the world's gas reserves. The Middle East in particular, which has 60% of the world's proven conventional oil reserve has more than 70% of its reserves in carbonate reservoirs. And the carbonate reservoir in Southeast Asia contributes the 40% of the total recoverable hydrocarbon worldwide. It is well-known that carbonates bring its own complexity and challenge, from its variations in rock types, porosity and also permeability thus making them difficult to characterize. The heterogeneous characteristics of the carbonate reservoir are mainly influenced by their depositional environment setting and the diagenetic processes which took place on them. This involves detailed understanding of the pore space, pore effectiveness, fluid saturation and moveable fluid. By understanding the characters and properties, the strategy for future field development can then be determined.

In this paper, it will show the incorporation both of lab data and standard to advanced logs evaluation such as borehole image and nuclear magnetic resonance analysis, to solve these complex carbonate reservoir on key well. The study identified several good quality reservoirs, which are predominantly grain-supported and deposited in the high energy shallow-marine middle shelf, such as dolomitic rudistic rudstone, biomoldic bioclastic packestone, fine bioclastic packestone and fine-grained peloiodal bioclastic packestone. The rest of lithofacies are poor reservoir quality which are generally mud-supported wackestone and floatstone units. Electrofacies was derived by standard wireline logs and supervised by core data. Several image logs and NMR logs data were evaluated to get image-based rock typing and bound-free fluid components. Medium-coarse resistive to conductive mottled carbonates was determined as image based rock typing and it corresponds to rudist-floatstone to wackestones-packestones on core data at layer 2. Medium to high free fluid index was shown as T2 data distribution beyond T2 cutoff layer 2 on NMR data at this good rock quality.

On top of that, the flow contribution of the reservoir is assessed based on the well test and cased-hole data measurement. On layer 5, the high flow rate occurs on coarse-grained rudist prone carbonates which was determined as best rock quality from open-hole log data and supervised by core data. In the end, the uncertainties of the carbonate reservoir are comprehensively captured which leads to the de-risking the project assessment.

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