Publications : Online Learning

Uncertainty Quantification in Non-Linear Systems

Online Learning
Published in 2018

Presenter: Ilnur Minniakhmetov, Principal Geoscientist Data & Numeric Modelling, Geoscience Centre of Excellence, BHP

Ilnur is Principal Geoscientist in Numerical Modelling and Data in BHP, Perth, WA. He finished his PhD in Applied Mathematics and Physics at Moscow Institute of Physics and Technology. During his study, Ilnur worked on developing and implementing basic geostatistical algorithms for oil-gas modelling software. In 2013 Ilnur started his postdoc in COSMO Lab, McGill University. During the following three years he developed high-order simulation methods for optimisation of mining complexes. Ilnur joined BHP in 2017 and is currently working on the improvement of resource estimation practices and quantification of uncertainty. His area of interest is stochastic simulations, multi-dimensional approximation, and machine learning.

Webinar Overview:
Uncertainty quantification is critical for informed decision-making processes in the mining and oil-gas industry. The conventional deterministic approach uses assumption of Gaussianity of input variables and linearity of value-transfer function, e.g. value chain in mining, hydrodynamic system in sub-surface reservoirs. However, in reality, these value-transfer functions are quite non-linear and comprise multiple sources of uncertainty (geological, geometallurgical, geotechnical, financial, etc). This presentation demonstrates limitations of the deterministic approach on several examples of non-linear systems and highlights advantages of the stochastic approach in uncertainty quantification processes. This talk will provide you with a better understanding of the uncertainty quantification process and the importance of using conditional simulations.

Duration: 00:51:40

PD Hours: 1

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