Publications

Carbonate reservoirs are of complex lithology and present a challenge to characterize in petrophysical evaluations because of the pore heterogeneity. This paper presents the integrated petrophysical study carried out in the carbonate reservoir of Kujung I Formation, ABC Field in Indonesia. The Kujung Formation was trapped in east to west built up carbonate in shelf margin during Oligo-Miocene period. Extensive data acquisitions such as Nuclear Magnetic Resonance (NMR), Full-bore Formation Micro imager (FMI), Dipole Shear Sonic Imager (DSI), and rock characterization (RCA / SCAL) have been used to capture static and dynamic properties in this complex carbonate buildup. The study specifically addresses permeability issues related to the identification of different rock typing methods (Lithofacies / Reservoir Quality Index (RQI) / Flow Zone Indicator (FZI) / Winland / Lucia) and the permeability correlations based on different approaches such as core data, NMR permeability (K) against Pressure Transient Analysis (PTA). In the study, an integrated petrophysical evaluation was done using an internal petrophysical workflow for reservoir characterization. A full dataset of core, FMI, NMR, and conventional logs were assembled to develop an improved generalized permeability-porosity relationship (single regression and rock typing approach) to characterize Kujung I Formation, which was then applied to un-cored wells. Kujung I reservoirs are inherently heterogeneous carbonate rocks. Three (3) different permeability prediction approaches were applied in this study: Method 1 was a conventional method based on simple regression by evaluating permeability from the core, a K/Phi relationship; Method 2 used Hydraulic Flow Unit (RQI/FZI); and Method 3 used NMR logs, to seek the parameters reflecting a good correlation with core permeability. Each independent variable was tested in the NMR Timor Coates equation against core permeability. The variables yielding the highest correlation coefficient (CC) were included in multiple regression analysis.