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NARROW
GeoRef Subject
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all geography including DSDP/ODP Sites and Legs
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Africa
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East African Rift (1)
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Primary terms
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A quantitative study of the microstructure of Indian Gondwana shale: a fractal and algebraic topology approach
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 analysts principally study the storage, flow and saturation properties of porous rocks and sediments. Some of the derived parameters are specific to hydrocarbon production but many have commonality with other subsurface disciplines such as hydrology and soil science. Traditional core analysis involves direct physical experimentation on core plugs to derive a range of parameters used as calibration for conventional well logs, and to predict hydrocarbon reserves and recovery. The mechanisms and processes for obtaining such data have evolved significantly during the last century, from the manual instruments of the mid-twentieth century to the accredited digital data collection and recording of the 1990s onwards. X-ray micro- and nano-scale computed tomography (CT) imaging led to the development of the digital rock physics subdiscipline in the early 2000s. This has subsequently allowed direct visualization of fluid flow at the pore scale, imaging the wetting phase and multiphase fluid mobility. Multiscale imaging workflows are being developed to overcome issues around heterogeneous rock and the limited field of view associated with the highest resolution X-ray CT images. Hybrid workflows, which combine digital rock physics with traditional core analysis, are becoming increasingly common to meet the challenges associated with some of the most difficult to constrain properties, such as relative permeability. At a larger scale, the recent development of multisensor core logging (MSCL) tools has allowed the cost-effective acquisition of essentially continuous high-resolution 1D, 2D and 3D datasets from both slabbed and unslabbed whole core. Often aided by artificial intelligence to manage and interpret these large physical and chemical datasets, both new and legacy core can be rapidly screened to allow representative subsampling for detailed laboratory experimentation. The context and data provided by the MSCL then allows effective upscaling of these time- and cost-intensive point-source measurements. In the last decade, extended reality (XR) has resulted in a step change in the ability to visualize and integrate core and core-derived information with other subsurface datasets. A very wide range of scales can be managed effectively, from micrometre- to centimetre-scale petrographical and core analysis data, to metre-scale well logs and up to kilometre-scale 3D and 4D seismic. These tools allow stakeholders to work and meet from any location in a common workspace, and efficiently scale and interrogate data in a virtual 3D environment. The various advances in core analysis and associated technologies during the early twenty-first century mean that the study of porous media to help enable the energy transition looks assured. During the coming decades, applications as diverse as carbon capture, utilization and storage (CCUS), hydrogen storage, geothermal energy generation, mining for critical minerals, palaeoclimate studies, radioactive waste management, and site surveys for windfarms will all continue to benefit from the data and understanding derived from core analysis.