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.

Abstract Core samples from the subsurface can provide unambiguous direct information to guide operator decisions. Core may be acquired with drilling equipment (full-bore core) or by post-drill wireline methods (sidewall core). Both approaches have distinct profiles of cost, risk, sample type and value, and an operator must select the most appropriate to progress business in an informed way. The option to selectively core after drilling and perceptions of lower cost and risk might indicate that sidewall coring will always be the best approach. Recent developments to increase the size and quality of rotary sidewall samples would only add weight to this view. It is not all good news for sidewall core, however. Individual sample size and total volume delivered per run are tiny; weak rock or high overbalance pressure may cause poor recovery and biased datasets; time between drilling and logging allows mud invasion and borehole relaxation, so samples are often broken and pore fluids contaminated. Sidewall sample sets therefore leave a higher degree of uncertainty when compared to full-bore core. It is this operator's view that both approaches have a role to play in reducing subsurface uncertainty, and cost, risk and value should be carefully considered when deciding which to apply.

Abstract The growing importance of subsurface carbon storage for tackling carbon emissions requires an accurate characterization of potential reservoirs to understand their capabilities. In this context, the use of legacy data originally acquired in the last fifty years for scientific projects and petroleum exploration and production activities would represent a suitable cost-effective solution and help to maximize the value of this extended national asset. Core material represents the only direct observation of subsurface deposits and must be preserved from the current disposal process related to the decommissioning of hydrocarbon fields. In this contribution, a suite of samples from core material stored at national (i.e. British Geological Survey) and local (i.e. Department of Earth Sciences, Royal Holloway, University of London) core repositories, previously characterized at the micro scale using X-ray micro-computed tomographic (μCT) imaging are discussed. Using this technique, it has been possible to investigate how pore and grain geometries control crucial features of a suitable reservoir such as porosity and permeability. The aim of this contribution is to describe the methodology behind digital image analysis (DIA) following μCT imaging applied to core material. We show how DIA can be used to provide significant measures of reservoir suitability when making initial assessments of storage reservoirs, without the need for expensive and time-consuming analyses.