The role of core in twenty-first century reservoir characterization: an introduction

Abstract

Abstract The first two decades of the twenty-first century have seen significant advances across a wide range of reservoir characterization techniques, from microscale digital rock physics to macroscale 3D and 4D seismic. At the same time, industry downturns and the requirements of the energy transition have demanded improved understanding of the value and impact of subsurface data to justify their acquisition and commercial relevance. Despite changing technologies and demands, the acquisition, description and analysis of core remains a fundamental tool in managing subsurface uncertainty and associated risk. Value continues to be created in relation to the reservoir property, sedimentological, diagenetic and structural characterization of subsurface reservoirs, and these are the focus of the Core Values volume. The enduring business impact of core reflects advances in acquisition methods and laboratory-based core analysis (Theme 1 of the volume); the recent development of multi-sensor core scanning and associated artificial intelligence (AI) tools that allow unprecedented high-resolution data collection and visualization (Theme 2); the integration of core-derived data with new complementary technologies, leading to improved characterization of both cored and uncored intervals (Theme 3); the changing nature and role of legacy core collections due to digitization and improved data access (Theme 4). These are complemented by the need to better understand both existing hydrocarbon resources and other subsurface energy-related systems, particularly CCUS (carbon capture, utilization and storage), geothermal energy and the long-term storage of nuclear waste (Theme 5). Through the energy transition core will remain the ground truth foundation to any subsurface understanding and evaluation. At the same time, the technologies available to maximize the applied value of core will continue to develop and evolve, with the integration of diverse and complex core-derived and core-related datasets becoming the norm. Even in the face of AI's impact and value in handling such datasets, those earth scientists who can effectively analyse, interpret and integrate core will still be best placed to meet the subsurface challenges of the future.