RESUMEN
Permeability modelling is considered a complex task in reservoir characterization and a key component of reservoir simulation. A common method for permeability modelling involves performing static rock typing (SRT) using routine core analysis data and developing simple fitting-based mathematical relations that link permeability to reservoir rock porosity. In the case of carbonate reservoirs, which are associated with high heterogeneities, fitting-based approaches may fail due to porosity-permeability data scattering. Accurate modelling of permeability using petrophysical well log data seems more promising since they comprise a vast array of information about the intrinsic properties of the geological formations. Furthermore, well log data exhibit continuity throughout the entire reservoir interval, whereas core data are discrete and limited in availability and coverage. In this research work, porosity, permeability and log data of two oil wells from a tight carbonate reservoir were used to predict permeability at un-cored intervals. Machine learning (ML) and fitting models were used to develop predictive models. Then, the developed ML models were compared to exponential and statistical fitting modelling approaches. The integrated ML permeability model based on Random Forest method performed significantly superior to exponential and statistical fitting-based methods. Accordingly, for horizontal and vertical permeability of test samples, the Root Mean Squared Error (RMSE) values were 3.7 and 4.5 for well 2, and 1.7 and 0.86 for well 4, respectively. Hence, using log data, permeability modelling was improved as it incorporates more comprehensive reservoir rock physics. The outcomes of this reach work can be used to improve the distribution of both horizontal and vertical permeability in the 3D model for future dynamic reservoir simulations in such a complex and heterogeneous reservoir system.
RESUMEN
Fracture networks inside the caprock for CO2 storage reservoirs may serve as leakage pathways. Fluid flow through fractured caprocks and bypass conduits, however, can be restrained or diminished by mineral precipitations. This study investigates precipitation of salt crystals in an artificial fracture network as a function of pressure-temperature conditions and CO2 phase states. The impact of CO2 flow rate on salt precipitation was also studied. The primary research objective was to examine whether salt precipitation can block potential CO2 leakage pathways. In this study, we developed a novel microfluidic high-pressure high-temperature vessel to house geomaterial micromodels. A fracture network was laser-scribed on the organic-rich shales of the Draupne Formation, the primary caprock for the Smeaheia CO2 storage in Norway. Experimental observations demonstrated that CO2 phase states influence the magnitude, distribution, and precipitation patterns of salt accumulations. The CO2 phase states also affect the relationship between injection rate and extent of precipitated salts due to differences in solubility of water in CO2 and density of different CO2 phases. Injection of gaseous CO2 resulted in higher salt precipitation compared to liquid and supercritical CO2. It is shown that micrometer-sized halite crystals have the potential to partially or entirely clog fracture apertures.