Uncertainty is an Asset: Monte Carlo simulation for uncertainty estimation in implicit 3D geological modelling

Evren Jeremie Pakyuz-Charrier

doi:10.26182/5c5b9c2bcf738

Uncertainty is an Asset: Monte Carlo simulation for uncertainty estimation in implicit 3D geological modelling

Evren Jeremie Pakyuz-Charrier

School of Earth and Oceans

Research output: Thesis › Doctoral Thesis

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Abstract

In this thesis, practical and theoretical improvements to the Monte Carlo simulation for Uncertainty Estimation (MCUE) method applied to 3D geological modeling are made.
Pole vector Bayesian sampling is proposed as a more rigorous alternative than dip vector sampling for planar features.
A complete MCUE Markov Chain procedure for drillhole path and log uncertainty propagation is proposed and demonstrated. Topological analysis is introduced as a powerful tool to mitigate plausible model heterogeneity.
Overall, MCUE is evidenced as a robust, cost/time efficient and flexible uncertainty propagation method in 3D geological modeling with high potential for improvement.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Jessell, Mark, Supervisor Lindsay, Mark, Supervisor
Award date	19 Nov 2018
DOIs	https://doi.org/10.26182/5c5b9c2bcf738
Publication status	Unpublished - 2018

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THESIS - DOCTOR OF PHILOSOPHY - PAKYUZ-CHARRIER Evren Jeremie - 2019
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Cite this

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title = "Uncertainty is an Asset: Monte Carlo simulation for uncertainty estimation in implicit 3D geological modelling",

abstract = "In this thesis, practical and theoretical improvements to the Monte Carlo simulation for Uncertainty Estimation (MCUE) method applied to 3D geological modeling are made. Pole vector Bayesian sampling is proposed as a more rigorous alternative than dip vector sampling for planar features. A complete MCUE Markov Chain procedure for drillhole path and log uncertainty propagation is proposed and demonstrated. Topological analysis is introduced as a powerful tool to mitigate plausible model heterogeneity. Overall, MCUE is evidenced as a robust, cost/time efficient and flexible uncertainty propagation method in 3D geological modeling with high potential for improvement. ",

keywords = "3D geological modelling, spherical statistics, uncertainty, Bayesian statistics, Monte Carlo, sensitivity analysis, Topology, clustering",

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AB - In this thesis, practical and theoretical improvements to the Monte Carlo simulation for Uncertainty Estimation (MCUE) method applied to 3D geological modeling are made. Pole vector Bayesian sampling is proposed as a more rigorous alternative than dip vector sampling for planar features. A complete MCUE Markov Chain procedure for drillhole path and log uncertainty propagation is proposed and demonstrated. Topological analysis is introduced as a powerful tool to mitigate plausible model heterogeneity. Overall, MCUE is evidenced as a robust, cost/time efficient and flexible uncertainty propagation method in 3D geological modeling with high potential for improvement.

KW - 3D geological modelling

KW - spherical statistics

KW - uncertainty

KW - Bayesian statistics

KW - Monte Carlo

KW - sensitivity analysis

KW - Topology

KW - clustering

U2 - 10.26182/5c5b9c2bcf738

DO - 10.26182/5c5b9c2bcf738

M3 - Doctoral Thesis

ER -

Uncertainty is an Asset: Monte Carlo simulation for uncertainty estimation in implicit 3D geological modelling

Abstract

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