Remote Characterisation of the Carbon Storage and Containment Capacity of Sedimentary Basins
Carbon Storage (CS) in natural and abandoned oil and gas sedimentary basins offers a significant opportunity for reducing atmospheric carbon dioxide. There is an urgent need to develop many more sites for large-scale CS operations however the scale of the basins and the complexity of the lithological and structural analyses required to fully characterise the CO2 storage and containment capabilities using current field and laboratory based methods is extremely expensive and time-consuming. The construction of 3D sub-surface geospatial models of both onshore and offshore basins is a vital aspect of characterisation of the carbon storage and containment capabilities and is critically dependant on the information derived from the analysis of geological outcrops and borehole cores. Current analysis techniques produce very limited datasets that do not fully capture the inherent 3D nature of these geological features which result in significant gaps in structural information with consequent severe effects on the accuracy of geological interpretation. Accurate characterisation of lithology and mineralogy in rock volumes and the sedimentary architecture of a sedimentary basin is central to predicting the presence or quantifying the volumetrics of potential carbon storage capacity and containment. Geological outcrops (including cliffs, quarries and natural exposures) provide an important primary source of data for the development of conceptual and predictive geological models providing direct observations of rock bodies and their geometries, architecture and lithological heterogeneities over scales ranging from less than 1 cm to several tens of kilometres. Conventional outcrop analysis techniques (e.g. sedimentary logs, surface geological maps and cross-sections provide invaluable information on geological systems, facilitating many contemporary structural and stratigraphical and syntheses. However, field data are typically collected by one- and two-dimensional paper-based methods that do not fully capture the inherent 3D nature of geological features. Associated accuracy, precision and uncertainty are rarely defined, and it is often difficult to extract reliable quantitative information on the geometries and spatial heterogeneities of sedimentary rock bodies, data which are essential for 3D computer-based geostatistical reservoir modelling.