Integration of Uncertain Data in Geostatistical Modelling

Soares, Amílcar; Nunes, Rúben; Azevedo, Leonardo

doi:10.1007/s11004-016-9667-5

Cited by 25 publications

(20 citation statements)

References 20 publications

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“…In the second step, after all uncertain locations were visited and a set of N d values was generated from the local distributions, the stochastic sequential procedure visits the remaining locations of the model using the conventional direct sequential simulation methodology. The resulting models reproduced the spatial covariances, and the global and local distributions [15], as inferred from the existing certain and uncertain data.…”

Section: Methodsmentioning

confidence: 66%

“…This study proposed direct sequential simulation with local probability distributions [15] as a modeling technique to include uncertain experimental data consistently during the geo-modeling workflow.…”

Section: Methodsmentioning

confidence: 99%

“…where the kriging weights, λ α and λ β , are associated with the hard data and the previously simulated uncertain experimental data, respectively, and the local variance is represented by the simple kriging variance. From this auxiliary distribution, a value, z s (x 0 ), is drawn at the location of the uncertain experimental data [15].…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we propose an alternative way to integrate uncertain soft data into the natural resource modeling procedure through stochastic sequential simulation with local probability distribution functions [15]. This approach uses the soft data as a probabilistic model by transforming the existing soft data into local probability distribution functions that represent the existing local uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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A Geostatistical Simulation of a Mineral Deposit using Uncertain Experimental Data

et al. 2019

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In the geostatistical modeling and characterization of natural resources, the traditional approach for determining the spatial distribution of a given deposit using stochastic sequential simulation is to use the existing experimental data (i.e., direct measurements) of the property of interest as if there is no uncertainty involved in the data. However, any measurement is prone to error from different sources, for example from the equipment, the sampling method, or the human factor. It is also common to have distinct measurements for the same property with different levels of resolution and uncertainty. There is a need to assess the uncertainty associated with the experimental data and integrate it during the modeling procedure. This process is not straightforward and is often overlooked. For the reliable modeling and characterization of a given ore deposit, measurement uncertainties should be included as an intrinsic part of the geo-modeling procedure. This work proposes the use of a geostatistical simulation algorithm to integrate uncertain experimental data through the use of stochastic sequential simulations with local probability functions. The methodology is applied to the stochastic modeling of a benchmark mineral deposit, where certain and uncertain experimental data co-exist. The uncertain data is modeled by assigning individual probability distribution functions to each sample location. Different strategies are proposed to build these local probability distributions. Each scenario represents variable degrees of uncertainty. The impacts of the different modeling approaches on the final deposit model are discussed. The resulting models of these proposed scenarios are also compared against those retrieved from previous studies that use conventional geostatistical simulation. The results from the proposed approaches showed that using stochastic sequential simulation with local probability functions to represent local uncertainties decreased the estimation error of the resulting model, producing fewer misclassified ore blocks.

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Section: Methodsmentioning

confidence: 66%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Geostatistical Simulation of a Mineral Deposit using Uncertain Experimental Data

et al. 2019

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“…Stochastic optimizers integrate and manage space dependent geological uncertainty (grades, material types, metal, and pertinent rock properties) in the scheduling process, based on its quantification with geostatistical or stochastic simulation methods (e.g. Goovaerts 1997;Soares et al 2017;Zagayevskiy and Deutsch 2016). Such scheduling optimizers have been long shown to increase the net present value of an operation, while providing a schedule that defers risk and has a high probability of meeting metal production and cash flow targets (Godoy 2003;Ramazan and Dimitrakopoulos 2005;Jewbali 2006;Kumral 2010;Albor and Dimitrakopoulos 2010;Goodfellow 2014;Montiel 2014;Gilani and Sattarvand 2016;and others).…”

Section: Introductionmentioning

confidence: 99%

A stochastic optimization formulation for the transition from open pit to underground mining

MacNeil

Dimitrakopoulos

2017

Optim Eng

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As open pit mining of a mineral deposit deepens, the cost of extraction may increase up to a threshold where transitioning to mining through underground methods is more profitable. This paper provides an approach to determine an optimal depth at which a mine should transition from open pit to underground mining, based on managing technical risk. The value of a set of candidate transition depths is calculated by optimizing the production schedules for each depth's unique open pit and underground operations which provide yearly discounted cash flow projections. By considering the sum of the open pit and underground mining portion's value, the most profitable candidate transition depth is identified. The optimization model presented is based on a stochastic integer program that integrates geological uncertainty and manages technical risk. The proposed approach is tested on a gold deposit. Results show the benefits of managing geological uncertainty in long-term strategic decision-making frameworks. Additionally, the stochastic result produces a 9% net present value increase over a similar deterministic formulation. The risk-managing stochastic framework also produces operational schedules that reduce a mining project's susceptibility to geological risk. This work aims to approve on previous attempts to solve this problem by jointly considering geological uncertainty and describing the optimal transition depth effectively in 3-dimensions.

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Predicting Subsurface Properties from Seismic Data

Tylor‐Jones

Azevedo²

2022

A Practical Guide to Seismic Reservoir Characterization

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Integration of Uncertain Data in Geostatistical Modelling

Cited by 25 publications

References 20 publications

A Geostatistical Simulation of a Mineral Deposit using Uncertain Experimental Data

A Geostatistical Simulation of a Mineral Deposit using Uncertain Experimental Data

A stochastic optimization formulation for the transition from open pit to underground mining

Predicting Subsurface Properties from Seismic Data

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