QUALITATIVE EVALUATION OF THE UPPER PINDA RESERVOIR IN THE MIBALE FIELD USING MULTI-LOG ANALYSIS AND COMPOSITE ALTERATION INDEX

In the context of optimizing oil production, the accurate evaluation of reservoir quality is a major factor for the profitability of oil fields. The Mibale field, located in the coastal basin of the Democratic Republic of Congo, contains the Pinda reservoir, known for its strong lithological and petrophysical heterogeneity. Although the presence of hydrocarbons is confirmed, productivity remains low, with an average permeability ranging between 2 and 5 mD, raising questions about the distribution of reservoir properties and the impact of alteration on rock behavior. This study aims to determine the dominant sedimentary and lithological facies and their influence on reservoir quality, to understand the role of alteration in modifying petrophysical properties, and to identify favorable zones (sweet spots) based on GR, NPHI, RHOB logs and the Composite Alteration Index (CAI). It is based on the hypothesis that the Pinda reservoir is dominated by carbonate and sandstone facies whose petrophysical properties vary with depth, that the CAI constitutes a reliable indicator of property degradation due to argillization, and that zones with low GR and moderate to low CAI correspond to sweet spots with favorable secondary porosity. The general objective is to evaluate the quality of the Pinda reservoir based on its lithology, level of alteration, and petrophysical characteristics, through the identification of lithological facies from GR, NPHI, and RHOB logs, the calculation and interpretation of CAI, as well as the delineation of optimal reservoir zones according to diagraphic and alteration criteria. The adopted methodology relies on the interpretation of well logs from exploration well 1-X of the Mibale field, with analytical and numerical processing of the data cross-referenced with CAI calculated from vertical variations in lithofacies. The lithological facies were identified using NPHI–RHOB cross-plots, and the favorable zones were mapped using STRATER 5 software, based on the logs and cores provided by Perenco in 2020.