Pirot, Guillaume1 and Lindsay, Mark1 and Grose, Lachlan2 and de La Varga, Miguel3 and Jessell, Mark1
1Centre for Exploration Targeting, The University of Western Australia, Crawley, Australia, 2School of Earth, Atmosphere and Environment, Monash University, Clayton, Australia, 3Institute for Computational Geoscience and Reservoir Engineering, RWTH Aachen University, Aachen, Germany
Subsurface modelling is a challenge because we have a limited access to direct observations of the desired quantities of interest and because we have an imperfect understanding of geological processes. Thus, each model realization is quite uncertain. This is why we need to consider both our sources of errors and alternative modelling schemes. In order to avoid uncertainty underestimation when the purpose of modelling is decision-making, uncertainties related to observations, algorithms and conceptual representations should be propagated in the generation of stochastic geological realization ensembles.
Here, we focus on the sensitivity of data and algorithmic uncertainties on the resulting geological uncertainty. Indeed, it might not make sense to compare a pie with a cake or a mousse. This is why we leave conceptual uncertainty aside and in the hands of model selection techniques. While data errors can be estimated by repeating some measurements, algorithmic uncertainties might be more complex to define and are not always accessible. To handle that, we propose to rely on the use of pilot-stick perturbations, which consists in adding fictive drill-holes complying with the assumed stratigraphy and the presence or absence of surface geological information.
To illustrate the method, we perform a sensitivity analysis of the perturbations on a synthetic case, based on a Precambrian basin setting, with three different geological modelling engines. The resulting geological uncertainty is analysed with different indicators based on the cardinality, entropy, connectivity, topology and geostatistics of both lithological formations and their underlying scalar-fields. Preliminary results show the pre-dominant importance of pilot-stick perturbations and their ability to mitigate the smoothing resulting from implicit modelling, in particular at locations where no surface data is available.
This work is supported by the ARC-funded Loop: Enabling Stochastic 3D Geological Modelling consortia (LP170100985) and DECRA (DE190100431) and by the Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government’s Cooperative Research Centre Programme. This is MinEx CRC Document 2020/46.
Guillaume joined the Centre for Exploration Targeting in September 2019. He is involved in the ‘Automated 3D Modelling’ MinEx CRC project and as Work-Package 5 leader in the LOOP consortium (loop3d.org), where he develop tools to improve the characterization, the propagation and the reduction of prediction uncertainties.