Lithofacies Modeling Application in Reservoir Simulation of Low Permeability Carbonates

Abstract

Nowadays there is observed a global trend of ever-increasing complexity and heterogeneity of hydrocarbon reservoirs. More challenging time makes companies take high-risk decisions based on long-term production prediction. As deterministic approach always gives a single result, it loses its relevance when we deal with risks. Generally probabilistic assessment methodology is coming to the forefront. A new approach called RE-Status (Reservoir evaluation status) has been developed in this study. The methodology is based on probabilistic assessment and along with normal uncertainty technique it includes lithofacies modeling. RE-Status is based on six key parameters that allow us to understand reservoir complexity and level of project definition in terms of reservoir engineering. Geological models were created by combining all geological and geophysical data in order to provide the highest-quality 3D representation of heterogeneous carbonate reservoirs. Evaluation of micro-macro heterogeneities and complex analysis of all core studies were used to derive lithofacies. Systematization of stratigraphic analysis, core descriptions and well logging data allowed us to locate facies in reservoir cross-section. It gave us better understanding of the reservoir and nature of filtration processes. The new approach was applied to two gas fields. Impact of reservoir parameters and lithofacies variability on the dynamics of gas production was described. In the first case RE-Status helped to improve history matching by using facies and uncertainty analysis. In the second case (green field) production prediction based on the results of heterogeneity analysis and facies modeling allowed to locate sweet zones, optimize drilling strategy, increase reliability of forecasting and reduce the uncertainty range. Novelty of the described approach is application of facies modeling to reduce key uncertainties, match the reservoir model to historical data and provide the most accurate forecast of production.