Geostatistical modeling of the geological uncertainty in an iron ore deposit

Mery, Nadia; Emery, Xavier; Cáceres, Alejandro; Ribeiro, Diniz; Cunha, Evandro

doi:10.1016/j.oregeorev.2017.05.011

Cited by 57 publications

(22 citation statements)

References 23 publications

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“…Because of the relationship between the grades, granulometry, and rock types ( [25], but these transformations are not suitable for variables that can take zero values, as is the case in the present case study. We therefore opt for a ratio transformation that does not use logarithms, as proposed in [10], where the quantitative variables are successively normalised by the residual of the closure:…”

Section: Modelling and Simulation Of Quantitative Variablesmentioning

confidence: 99%

“…This hierarchical workflow accounts for geological controls on the distributions of the quantitative variables but produces clear-cut discontinuities in the values of the quantitative variables when crossing the domain boundaries [5,6]. Several alternatives have been proposed to mitigate these discontinuities and to account for spatial correlation across the domain boundaries [6][7][8][9][10]. Another approach to produce gradual transitions near the domain boundaries is to model the quantitative variables of interest with no previous geological domaining by considering the controlling rock types or ore types as cross-correlated covariates [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Geological Modelling and Validation of Geological Interpretations via Simulation and Classification of Quantitative Covariates

2017

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This paper proposes a geostatistical approach for geological modelling and for validating an interpreted geological model, by identifying the areas of an ore deposit with a high probability of being misinterpreted, based on quantitative coregionalised covariates correlated with the geological categories. This proposal is presented through a case study of an iron ore deposit at a stage where the only available data are from exploration drill holes. This study consists of jointly simulating the quantitative covariates with no previous geological domaining. A change of variables is used to account for stoichiometric closure, followed by projection pursuit multivariate transformation, multivariate Gaussian simulation, and conditioning to the drill hole data. Subsequently, a decision tree classification algorithm is used to convert the simulated values into a geological category for each target block and realisation. The determination of the prior (ignoring drill hole data) and posterior (conditioned to drill hole data) probabilities of categories provides a means of identifying the blocks for which the interpreted category disagrees with the simulated quantitative covariates.

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Section: Modelling and Simulation Of Quantitative Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geological Modelling and Validation of Geological Interpretations via Simulation and Classification of Quantitative Covariates

2017

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“…Due to the compositional nature of the indicator random fields, the sum of the indicator cross-variograms γ k,k for k = k is equal to the opposite of the indicator direct variogram γ k , which can be demonstrated using Equations (5) and (12):…”

Section: Link Between Indicator Direct and Cross-variogramsmentioning

confidence: 99%

“…Identifying and modeling rock type, mineralization and alteration domains in ore deposits has become a crucial step in the assessment of geological and geometallurgical regionalized variables, such as metal grades or metal recoveries, due to the controls that these domains often exert on the distribution of these variables [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Indicator Variograms as an Aid for Geological Interpretation and Modeling of Ore Deposits

2017

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Geostatistics offers a set of methods for modeling, predicting, or simulating geological domains in space. In addition of being an input of some of these methods, indicator direct and cross-variograms convey valuable information on the geometry of the domain layouts and on their contact relationships, in particular, on the surface area of a domain boundary, on the surface area of the contact between two domains, on the propensity for a domain to be in contact with, or separated from, another domain, and on the minimum and maximum distances between points from two domains. Accordingly, the indicator variograms inferred from sparse sampling data can be used to determine whether or not an interpreted model of the subsurface is consistent with the sampling information. The previous concepts are illustrated through a case study corresponding to a porphyry copper deposit.

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“…There are many varieties of geological models generated from different modeling methods and geologists (Soubeyrand 2017; Mery et al 2017). Geological modeling often refers to raster-based or vector -based models, both of which have its own features and applicability.…”

Section: Introductionmentioning

confidence: 99%

Reservoir characteristics and three-dimensional architectural structure of a complex fault-block reservoir, beach area, China

Zhou

Xie

2018

J Petrol Explor Prod Technol

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The objective area in this paper is a complex fault-block reservoir, which is provided by China Petroleum Engineering Design Competition. Reservoir characteristics which comprise of stratigraphic features, vertical changes, and section properties (thickness, percentage sand, and percentage amalgamation) are documented. Through comprehensive analysis on structural architecture and reservoir characteristics, a three-dimensional quantitative reservoir modeling is taken at a regional scale of 69 × 97 × 37 m 3 with the application of geostatistics as theoretical guidance. Then, a high-resolution hierarchical reservoir model of this field has been generated with a combination of hierarchical, structural, physical, and well trajectory data. The established three-dimensional geological model integrates all well and structural information, which can provide a basic model for subsequent sedimentary microfacies modeling and property modeling as well. Finally, a series of profiles are built successively, they are three-dimensional fence diagrams, connecting well sections and well group profiles, which can be a valuable tool for reflecting geologic body with great reality and thus achieve reservoir characterization of the complex fault-block reservoir.

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Geostatistical modeling of the geological uncertainty in an iron ore deposit

Cited by 57 publications

References 23 publications

Geological Modelling and Validation of Geological Interpretations via Simulation and Classification of Quantitative Covariates

Geological Modelling and Validation of Geological Interpretations via Simulation and Classification of Quantitative Covariates

Indicator Variograms as an Aid for Geological Interpretation and Modeling of Ore Deposits

Reservoir characteristics and three-dimensional architectural structure of a complex fault-block reservoir, beach area, China

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