Integrated petrophysical and reservoir characterization workflow to enhance permeability and water saturation prediction

Abstract

Accurate estimation of permeability is essential in reservoir characterization and in determining fluid flow in porous media which greatly assists optimize the production of a field. Some of the permeability prediction techniques such as Porosity-Permeability transforms and recently artificial intelligence and neural networks are encouraging but still show moderate to good match to core data. This could be due to limitation to homogenous media while the knowledge about geology and heterogeneity is indirectly related or absent. The use of geological information from core description as in Lithofacies which includes digenetic information show a link to permeability when categorized into rock types exposed to similar depositional environment.The objective of this paper is to develop a robust combined workflow integrating geology and petrophysics and wireline logs in an extremely heterogeneous carbonate reservoir to accurately predict permeability. Permeability prediction is carried out using pattern recognition algorithm called multi-resolution graph-based clustering (MRGC). We will bench mark the prediction results with hard data from core and well test analysis.As a result, we showed how much better improvements are achieved in the permeability prediction when geology is integrated within the analysis. Finally, we use the predicted permeability as an input parameter in J-function and correct for uncertainties in saturation calculation produced by wireline logs using the classical Archie equation. Eventually, high level of confidence in hydrocarbon volumes estimation is reached when robust permeability and saturation height functions are estimated in presence of important geological details that are petrophysically meaningful.