Implementation of Automatic Fluid Interpretation for Hydrocarbon Identification in Reservoirs with Various Water Salinities in the Alf Field

Resistivity logs are vital for distinguishing hydrocarbon-bearing from water-bearing reservoir zones, as formation water's conductivity results in lower resistivity than hydrocarbons. However, variations in formation of water salinity can bias interpretations, as salinity affects resistivity. In the ALF field, located in the South Sumatra Basin, varying resistivity values across reservoir intervals pose challenges in distinguishing hydrocarbon zones from water-bearing zones. This variability is attributed to diverse salinity levels influenced by depositional environments, including fluvial complex, transitional, and shallow marine settings. Formation water salinity in the study area ranges from 9 to 19 kppm, with fluctuations correlating to depositional environments. This study focuses on the Automated Fluid Interpretation (Autofluid) method to improve fluid typing accuracy in reservoirs with complex salinity profiles. The Autofluid approach employs multiple resistivity thresholds, including a waterline and a hydrocarbon line, integrating these cutoffs with an autofluid module to reduce errors from salinity variations. Adjustments to the module's algorithms were applied across wells in the ALF field, achieving a 93.1% success rate in fluid identification across 5 wells (39 perforation datasets). Blind tests on 2 wells (22 perforation datasets) further validated the method's robustness, yielding a 90.9% accuracy. These results demonstrate AutoFluid's reliability in adapting to new data and accurately identifying reservoir fluids, even under variable salinity conditions.