High-resolution stratigraphy and physical property modelling of the Chalk
Woods, M; Newell, A; Farrant, A; Haslam, R; Clarke, SM
Stuart Clarke firstname.lastname@example.org
A fortuitous combination of events in the last 30 years has positioned the Chalk as the most eligible geological unit for high-resolution 3D modelling at national, regional and local scales, allowing site-specific predictions and enabling characte-ristics to be seen in a wider geological context. Advances in stratigraphical understanding coincided with national re-mapping by the British Geological Survey (BGS). Adoption of digital cartography generated the high-resolution outcrop data to which sub-surface data could later be related in 3D models. Add to this systematic digitization and geographical indexing of national ar-chives of borehole logs, cores, samples and geophysical data, and all the ingredients are in place for 3D modelling of the Chalk.
Modelling the Chalk is helped by its relatively simple structure and stratigraphical continuity, and, because of its importance as an aquifer, abundant borehole data. Borehole geophysical logs provide invaluable data about subsurface stratigraphy which is not normally captured by written core logs. These geophysical log interpretations are ‘ground-truthed’ against cored and geo-physically logged boreholes in the national archive, showing precisely how stratigraphy and geophysics are related. Non-confidential hydrocarbons boreholes, where appropriately sited, often allow modelling through the entire Chalk succession, providing stratigraphical context for shorter boreholes.
Recent work by the BGS has focused on combining conventional volumetric and stratigraphical surface modelling with physical property modelling. This approach uses statistical algorithms to examine what the distributions of known data might tell us about the likely properties of Chalk in data sparse areas. Initially, our modelling has focused on a range of simple properties in-cluding marl-content, hardness and presence of hardgrounds, but we are also now beginning to explore fracture distributions and flint frequency. The data sources we have used to create our model (geophysical log data) do not easily allow direct des-cription of chalk properties in terms of their engineering parameters. However, a general indication of this could be achieved by loading engineering data for the Chalk at points where these data exist within the model, and statistically interpolating values across the physical property subdivisions we have recognised.
|Acceptance Date||Oct 8, 2018|
|Publication Date||Oct 8, 2018|
|Journal||Engineering in Chalk: Proceedings of the Chalk 2018 Conference|
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