Just-in-time learning for the prediction of oil sands ore characteristics using GPS data in mining applications

For the mining based oilsands industry, it is desirable to determine the quality of the ore delivered to the extraction processes in real-time to make optimal operational decisions such as optimal ore blending to achieve maximal bitumen recovery. Currently, the industry determines the real-time ore characteristics for any given shovel Global Positioning System (GPS) location by first determining the shovel elevation from the topological mine map and then using the determined geological coordinates in the 3D geological block model. It should be noted that the block model is built based on the widely spaced core hole samples, and it is updated only on a yearly basis due to high cost of narrower core hole sampling. Thus, the block model predictions are often inaccurate in between the core hole spacing. On the other hand, mining operations data are available that contain accurate ore characteristics information in the already mined area. Therefore, in this work, we present a just-in-time based data-driven modelling strategy that utilizes the recently available mining operations data to obtain reliable ore characteristics given the GPS data. The prediction capability of ore characteristics using the proposed modelling strategy is validated at core hole locations. Further, the prediction of ore characteristics at non-core hole points demonstrate promising results.