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Minerals 2018, 8(7), 276; https://doi.org/10.3390/min8070276

Near Real-Time Classification of Iron Ore Lithology by Applying Fuzzy Inference Systems to Petrophysical Downhole Data

1
Department Exploration Geophysics, Western Australian School of Mines, Curtin University, Bentley 6102, WA, Australia
2
BHP Billiton, Perth 6000, WA, Australia
*
Author to whom correspondence should be addressed.
Received: 1 June 2018 / Revised: 26 June 2018 / Accepted: 26 June 2018 / Published: 28 June 2018
(This article belongs to the Special Issue Geometallurgy)
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Abstract

Fluctuating commodity prices have repeatedly put the mining industry under pressure to increase productiveness and efficiency of their operations. Current procedures often rely heavily on manual analysis and interpretation although new technologies and analytical procedures are available to automate workflows. Grade control is one such issue where the laboratory assay turn-around times cannot beat the shovel. We propose that for iron ore deposits in the Pilbara geophysical downhole logging may provide the necessary and sufficient information about rock formation properties, circumventing any need for real-time elemental analysis entirely. This study provides an example where petrophysical downhole data is automatically classified using a neuro-adaptive learning algorithm to differentiate between different rock types of iron ore deposits and for grade estimation. We exploit a rarely used ability in a spectral gamma-gamma density tool to gather both density and iron content with a single geophysical measurement. This inaccurate data is then put into a neural fuzzy inference system to classify the rock into different grades and waste lithologies, with success rates nearly equal to those from laboratory geochemistry. The steps outlined in this study may be used to produce a workflow for current logging tools and future logging-while-drilling technologies for real-time iron ore grade estimation and lithological classification. View Full-Text
Keywords: classification; petrophysical; iron ore; fuzzy inference systems; spectral gamma-gamma classification; petrophysical; iron ore; fuzzy inference systems; spectral gamma-gamma
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kitzig, M.C.; Kepic, A.; Grant, A. Near Real-Time Classification of Iron Ore Lithology by Applying Fuzzy Inference Systems to Petrophysical Downhole Data. Minerals 2018, 8, 276.

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