A large proportion of Rio Tinto’s Pilbara iron ore resources occur below the groundwater table. Mathematical groundwater modelling is, therefore, an important tool to support decision making and inform dewatering strategies. The orebody geometry of the Pilbara is such that management of mine dewatering can be undertaken on a pit by pit basis. Until now, however, models used to advise dewatering operations are deposit, or regional scale, multi-pit numerical models. These models invariably fail to meet the needs of the business in the operational space due to issues with the scale of conceptualisation, and in the complexity of the model design that results in lengthy run times. As a consequence, these types of models are unable to readily contribute to ‘on the run’ decision making processes.
A different approach to managing operational mine dewatering has been successfully rolled out across Rio Tinto’s Pilbara iron ore operations. Large scale models have been replaced with small scale, simple analytical superposition model solutions that focus on individual pits. The approach recognises a ‘fit for purpose’ modelling strategy (Brown & Trott, 2014). Analytical superposition models provide an ideal platform to quickly address queries such as operational borefield management. Calibration standards are maintained and predictions have also proven to be just as reliable as more ‘sophisticated’ models. The new approach meets business imperatives, making groundwater more relevant to decision making in the mine dewatering space. Furthermore, critical conceptual assumptions of the hydrogeological system, such as aquifer properties or boundary conditions can be tested quickly and easily, to provide further insights into the uncertainties associated with a particular prediction.