Recognising the Effect of Restoration from Surface Morphology to Reduce the Uncertainty of Forward Modelling

Deirdre Duggan¹, David Waltham², Mike Krus¹, Clare Bond¹, and Stuart McLean^1
1Midland Valley Exploration, Glasgow, United Kingdom
²Department of Geology, Royal Holloway, University of London, Egham, Surrey, United Kingdom

When carrying out restoration and forward modelling, the effect of restoration on geological surfaces needs to be understood. Investigating this effect is difficult in real world examples, as the original geometry and surface morphology are unknown, having been modified by geological processes including faulting, folding and compaction.

A known surface geometry from a sandbox model was deformed and restored. The restored surface morphology was compared with the known original morphology. This allows the difference between the two surfaces to be quantified and visualised. This information was used to analyse areas of likely uncertainty created during the restoration process.

Areas of surface uncertainty have potentially large implications for further modelling such as turbidite flow models. Using a particle flux numerical model for turbidite flow, both the original and restored surfaces were exposed to identical turbidite flow deposits. Comparison of the flow deposits on the two surfaces highlights propagation of uncertainties from structural modelling into further workflows.

A combination of analogue and forward modelling enables geoscientists to reduce uncertainty by using iterative feed-back modelling processes to determine the best-fit results. However, the accuracy of modelling depends on the input model. We have shown that the cumulative effects of uncertainties introduced during restoration, have implications for sediment modelling results. Best practice in numerical modelling requires that multiple scenarios are used in the process to fully capture uncertainty and interpretational variability.

AAPG Search and Discovery Article #90078©2008 AAPG Annual Convention, San Antonio, Texas