Geological modelling from field data and geological knowledge

Calcagno, Philippe; Chilès, Jean‐Paul; Courrioux, Gabriel; Guillén, A.

doi:10.1016/j.pepi.2008.06.013

Cited by 376 publications

(134 citation statements)

References 18 publications

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“…Conversely, the dispersion function of interpreted measurements (using geophysics) would be expected to be dependent on the sensitivity of the intermediary method. Additionally, dispersion functions may be probabilistic as well as deterministic (Bucher, 2012). Determinism is a strong assumption when no metrological study was conducted beforehand to assess its plausibility.…”

Section: Discussionmentioning

confidence: 99%

“…Scedasticity is essentially an untouched subject in geological 3-D modeling and it was pointed out to make the geological 3-D modeling community aware of this fact and its potentially nefarious influence on MCUE outputs. However, standard metrological studies can determine scedasticity and include it in a dispersion function to be a parameter of the prior uncertainty distributions (Bewoor and Kulkarni, 2009;Bucher, 2012). The evidence brought at the theoretical and practical levels allows us to strongly advocate for the use of pole vectors over dip vectors.…”

Section: Discussionmentioning

confidence: 99%

“…Statistics derived from samples (e.g., mean, median) or the actual sample are expected to lead to less dispersed disturbance distributions compared to single observations (Patel and Read, 1996;Bewoor and Kulkarni, 2009;Bucher, 2012;Sivia and Skilling, 2006;Davis, 2003). Therefore, making multiple measurements at each location of interest is recommended and field operators should not group, dismiss or otherwise alter these kinds of data.…”

Section: Disturbance Distribution Parameterizationmentioning

confidence: 99%

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Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

et al. 2018

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Abstract. Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters; they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the mining industry, and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3-D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed.The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of incorrect disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e., scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminish the occurrence of artifacts.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Disturbance Distribution Parameterizationmentioning

confidence: 99%

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Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

et al. 2018

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“…This choice is motivated by critical differences between the three approaches, both at the conceptual and practical level (Aug 2004). Geometric modeling engines interpolate features from sparse structural data and topological assumptions (Aug et al 2005;Jessell et al 2014a); they require prior knowledge of topology and are computationally affordable (Lajaunie et al 1997;Calcagno et al 2008). More specifically, explicit geometric engines require full expert 10 knowledge while implicit ones are based on observed field data and topological constraints (Jessell et al 2014a).…”

Section: Mcue Methodsmentioning

confidence: 99%

Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization

Pakyuz-Charrier¹,

Lindsay²,

Ogarko

et al. 2017

Preprint

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Abstract. Three-dimensional (3D) geological modeling aims to determine geological information in a 3D space using structural data (foliations and interfaces) and topological rules as inputs. They are necessary in any project where the properties of the subsurface matters, they express our understanding of geometries in depth. For that reason, 3D geological models have a wide range of practical applications including but not restrained to civil engineering, oil and gas industry, mining industry 10 and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) combined with input uncertainty (observational-, conceptual-and technical errors). Because 3D geological models are often used for impactful decision making it is critical that all 3D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data uncertainty propagation in implicit 3D geological modeling using GeoModeller API. This aim is achieved using Monte 15Carlo simulation uncertainty estimation (MCUE), a heuristic stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution 20 selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of inappropriate disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e. scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate 25 that the proposed workflows improve the reliability of uncertainty estimation and diminishes the occurrence of artefacts.Solid Earth Discuss., https://doi

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“…The regional 3D geological models [32], as well as fundamental structures have been simulated by these methods, including folds, faults [33], reservoir structure inverted from dynamic fluid flow response [34], domes [35], and multilayered structures. Some software packages, such as the TProGS package, produce stochastic geological realizations through Sequential Indicator Simulation [36].…”

Section: Related Workmentioning

confidence: 99%

Towards Automatic and Topologically Consistent 3D Regional Geological Modeling from Boundaries and Attitudes

Guo

Zhou

et al. 2016

IJGI

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Three-dimensional (3D) geological models are important representations of the results of regional geological surveys. However, the process of constructing 3D geological models from two-dimensional (2D) geological elements remains difficult and is not necessarily robust. This paper proposes a method of migrating from 2D elements to 3D models. First, the geological interfaces were constructed using the Hermite Radial Basis Function (HRBF) to interpolate the boundaries and attitude data. Then, the subsurface geological bodies were extracted from the spatial map area using the Boolean method between the HRBF surface and the fundamental body. Finally, the top surfaces of the geological bodies were constructed by coupling the geological boundaries to digital elevation models. Based on this workflow, a prototype system was developed, and typical geological structures (e.g., folds, faults, and strata) were simulated. Geological modes were constructed through this workflow based on realistic regional geological survey data. The model construction process was rapid, and the resulting models accorded with the constraints of the original data. This method could also be used in other fields of study, including mining geology and urban geotechnical investigations.

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Geological modelling from field data and geological knowledge

Cited by 376 publications

References 18 publications

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization

Towards Automatic and Topologically Consistent 3D Regional Geological Modeling from Boundaries and Attitudes

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