A Comprehensive Methodology for Assigning Capillary Pressure and Relative Permeability Curves to Reservoir Rock Types with Limited Laboratory Data

Abstract Accurate characterization of capillary pressure and relative permeability curves is crucial for reservoir modelling and production optimization. However, in many cases, laboratory measurements are only available for reservoir rocks of better quality, leaving those of lower quality lacking essential data. This paper proposes a methodology that combines log data and laboratory measurements to assign capillary pressure and relative permeability curves to different reservoir rock types, even when laboratory data is limited. The suggested methodology was successfully applied to sandstone reservoirs in a northern Africa field. First, all cored sample depths are converted to the corresponding log depths, and each sample is assigned to the most probable reservoir rock type (RRT) identified in the petrophysical analysis. Subsequently, Pc values are transformed into height above the free water level (HAFWL) curves, and these undergo a thorough screening process to filter out less confident samples. Higher confidence HAFWL curves are then grouped according to their respective RRTs. The subsequent step involves defining a reference synthetic Sw vs. HAFWL curve for those RRTs with assigned laboratory data. For the RRTs lacking laboratory data synthetic curves are then obtained by using log-identified irreducible water saturation (Swi) values above the transition zone. Finally, this part of the process concludes with the validation of the reference curves by comparing them to Sw profiles obtained from log interpretation. Next, attention shifts to kr data, beginning with curves pre-processing. This entails determining endpoints and Corey coefficients for all supplied curves to construct analytical kr curves. The subsequent step involves an innovative screening technique of kr curves based upon the use of cross-plots between petrophysical parameters and kr curve endpoints. This process serves two primary purposes: firstly, it allows filtering anomalous samples and categorise representative curves per RRT, and secondly, it identifies trends in the cross-plots. The latter will enable the creation of synthetic kr curves for RRTs lacking laboratory data. In summary, the proposed methodology offers a systematic approach to assign Pc and kr curves to reservoir rock types, effectively leveraging available laboratory data while addressing data gaps for less characterized rocks. Emphasis is placed on innovative screening techniques for kr curves. By incorporating synthetic curves and validating against log data, this methodology enhances the accuracy and reliability of Pc and kr curves, thereby supporting improved reservoir characterization and simulation studies for optimized hydrocarbon recovery.