Resource and Grade Control Model Updating for Underground Mining Production Settings

Abstract

A key requirement for the mining industry is the characterization of the spatial distribution of geometallurgical properties of the ore and waste in a mineral deposit. Due to geological uncertainty, resource models are crude representations of reality, and their value for forecasting is limited. Information collected during the production process is therefore of high value in the mining production chain. Models for mine planning are usually based on exploration information from an initial phase of the mineral extraction process. The integration of data with different supports into the resource or grade control model allows for continuous updating and is able to provide estimates that are more accurate locally. In this paper, an updating algorithm is presented that integrates two types of sensor information: sensors characterizing the exposed mine face, and sensors installed in the conveyor belt. The impact of the updating algorithm is analysed through a case study based on information collected from Reiche-Zeche, a silver–lead–zinc underground mine in Freiberg, Germany. The algorithm is implemented for several scenarios of a grade control model. Each scenario represents a different level of conditioning information prior to extraction: no conditioning information, conditioning information at the periphery of the mining panel, and conditioning information at the periphery and from boreholes intersecting the mining panel. Analysis is performed to compare the improvement obtained by updating for the different scenarios. It becomes obvious that the level of conditioning information before mining does not influence the updating performance after two or three updating steps. The learning effect of the updating algorithm kicks in very quickly and overwrites the conditioning information.

A Machine Learning Workflow to Support the Identification of Subsurface Resource Analogs

“…Analogs are comparable scale-dependent systems with similar characteristics and properties to support less well-constrained study intervals. Analog datasets for subsurface resource models include well cores and logs, drill holes cores, drill cuttings, seismic and other forms of remotes sensing and production such as fluid flow rates and run-of-mine mineral grades (Howell et al, 2014; Gravey et al, 2019; Bouayad et al, 2020; Prior et al, 2021). The similarity between these systems is predicated on predefined geological and engineering exploitation attributes.…”

“…Thus, subsurface resource analog selection, the choice of what analog to use across various scales and study intervals, is important to constrain subsurface resource model input univariate, multivariate, and spatial distributions. Although there are numerous uses of analogs in the subsurface, a couple prominent ones are the use of production data from existing wells as analogs for future wells to generate a type-curve for well hydrocarbon production over time during prospect evaluation and field estimation (Sidle and Lee, 2010; Rodríguez et al, 2014; Sun and Pollitt, 2022), and mining mineral deposit analogs help identify new prospecting targets (Prior et al, 2021). Accurate subsurface resource analog selection is crucial because an inappropriate analog can lead to erroneous information to populate models and become a source of imprecise and inaccurate subsurface uncertainty model predictions.…”