Innovative Advances in Core Analysis Techniques for Enhanced Understanding of Fractured Reservoir and Sustainable Resource Management

Accurately characterizing fractured reservoirs is a complex yet essential task in geosciences, with critical implications for effective energy resource management and environmental sustainability. Traditional core analysis techniques often fall short in capturing the intricate interactions between fracture networks, pore structures, and fluid-rock dynamics, leading to significant uncertainties in reservoir modeling and prediction. This research aims to bridge these gaps by integrating advanced core analysis methodologies, including high-resolution imaging techniques such as Focused Ion Beam Scanning Electron Microscopy (FIB-SEM), nano-CT, and micro-CT, which allow for detailed visualization and quantification of fractures and pore structures across multiple scales. The application of digital rock physics further enhances the understanding of pore-scale behavior, improving the accuracy of reservoir property modeling and fluid flow simulations. Additionally, the study explores the role of machine learning and real-time data integration in refining reservoir models and enabling more adaptive and proactive reservoir management. These innovations provide key improvements in hydraulic fracturing optimization, well placement, and carbon capture, thereby reducing water use, optimizing drilling, and enhancing environmental performance. The findings demonstrate that integrating advanced imaging, digital rock physics, and machine learning significantly improves the accuracy of fractured reservoir characterization, supporting more efficient extraction strategies while minimizing environmental impact. This research highlights the transformative potential of these technologies in driving sustainable energy solutions and lays a solid foundation for future advancements in fractured reservoir analysis.