In many parts of the world, especially the UK, Carboniferous shale plays are of particular interest, but there is a lack of high-quality, non-proprietary datasets for these successions. The JARR project is therefore focussing on Jurassic shales, particularly the Late Jurassic Kimmeridge Clay Formation of southern and eastern Britain and adjacent North Sea, as analogues for shales more broadly. The Kimmeridge Clay Formation provides a world-class, data-rich example on which to test emerging models of sweet spot development and characterization. Jurassic shale plays in regions such as the USA, Argentina, China and Australia demonstrate the specific relevance of the project.
The primary scientific challenges associated with shale gas exploration and production lie in understanding, on a basin-scale, the controls that determine the spatial distribution and internal heterogeneities of black shale and how these subsequently affect geomechanical properties and potential for gas production. An emerging consensus from geological studies is that orbital-scale fluctuations in climate affect the distribution and intensity of continental run-off and ocean upwelling. In turn, these drive changes in sediment supply, organic productivity, organic richness and – ultimately – variations in hydrocarbon resource potential. This hypothesis has been largely developed for subtropical-tropical ocean basins, which were primarily affected by atmospheric Hadley Cell dynamics.
Consequently, a conceptual framework emerges which causally links the locations and properties of hydrocarbon source rock deposition with the Hadley Cell dynamics and tectonics. The interaction between environment controls and local to regional tectonic activity has been shown to lead to additional but often small scale lateral variations in black shale thickness, bed- to pore-scale heterogeneities which in combination control natural fracture development. The geological expression of these multi-scale interactions is not well-constrained at the basin-scale, but is crucial to improving sweet spot prediction and characterization.