Estimating flow in the reservoir is critical for production forecasting, 3D modeling and field development. Laboratory experiments are the main source of information for the permeability, but the results might be impacted by a high degree of reservoir heterogeneity, as well as by the upscaling from core to log. The studied reservoir displays a complex lithology — limestone and dolomite – where dolomitization appears to be a clear reservoir rock quality enhancer. High permeability streaks can be related to dolomitized intervals, however a simple porosity vs. permeability function cannot capture the large variation of this property (from 0.1 to 5000mD)
In this study, the volumetrics computed from a comprehensive petrophysical model that includes the integration of core descriptions, XRD, and gamma ray spectroscopy to properly estimate the fractions of limestone, dolomite, clay, and heavy minerals were used. A core to log correlation was made and it clearly showed how the dolomitization improves our reservoir properties, in particular the permeability. Trends functions were established based on the stratigraphy and on the content of limestone/dolomite. These trends were used to train a fuzzy logic model supported by other logs such as: normalized porosity and density-neutron vector angle/length. The vector angle/length are coordinate transforms to convert neutron density into linear independent vectors.
This approach addresses the large variation of permeability related to the strong diagenetic footprint onto the reservoir. The results were compared to permeability from core and KH from PTA analysis, and displayed an error of less than 5%