Thin-bedded reservoirs of sand and shale, which contains hydrocarbons was determined in Abu Gharadig Basin, however the hydrocarbon-in-place is not properly quantified which is reflected on the volumetric calculations. The net pay and hydrocarbon saturation assessment is challenging in Shaly sand reservoir, with low resistivity and low contrast log response.
The scope of this study is to overcome the petrophysical challenges in reservoir evaluation and reach more accurate volumetric calculations using advanced unconventional evaluation modules.
Integrating the available data lead to a more accurate petrophysical evaluation. At first, the core data was used to divide the reservoir into different rock types by calculating the (FZI). The next step was integrating the core petrographic analysis with the available electric logging data to identify the clay types and shale distribution in the reservoir and their properties by following Thomas-Stieber diagrams.
The results of the previously stated methodology showed that, two types of Shaly distribution are present in the reservoir; the first one is a laminated Shaly sand reservoir for which the low resistivity pay evaluation module was used for the saturation calculations. The second type of clays was dispersed clays within the sand which had a major effect on the reservoir porosity and hence a different evaluation module was used. Waxman-Smitts method was used to calculate the saturation for the reservoir affected by this shale distribution.
The third step was using both the capillary pressure measurements form the SCAL core data and the FWL derived from the pressure data analysis to construct a saturation height function model which was used to calibrate the saturation calculations from the Shaly sand analysis modules.
In order to effectively address uncertainties within the reservoir as well as model the heterogeneities variations across the field, Electrofacies model was used to identify rock facies distribution which helped create conceptual model scenarios to be used in the Static model realizations. This model integrated sedimentary, structure and texture information interpreted from standard logs and core data. This integration provides a powerful tool for the distribution of reservoir characterizations.