Considerations When Ranking Stochastically Modeled Oil Sands Resource Models for Mining Applications

Abstract

Abstract Traditional oil sands mining operations have used deterministic techniques to create a single resource model for mine planning. Stochastic modeling, commonly used for in situ oil sands evaluation, provides more realistic geology and allows for multiple realizations, which mining operations can use to assess the variability of recoverable bitumen volume estimates and develop mine plans accordingly. The existence of multiple realizations makes it possible to measure uncertainty, but eventually detailed mine planning will proceed based on a single realization. This paper discusses the processes of stochastic modeling and of determining the appropriate single realization for mine planning as applied to an oil sands mine currently in the planning stage (Fort Hills). Geological models for mining operations have less uncertainty than models for in situ operations due to the much closer drill hole spacing and the better understood recovery process for mining, but the level of uncertainty is not zero. The same techniques that are currently being used to assess uncertainty for in situ oil sands leases can be applied to mining leases to quantify uncertainty for mine planning. In the case of Fort Hills, 100 realizations of ore grade were created using conditional simulation. Ranking solely by total bitumen in place was insufficient, so a new measure of heterogeneity related to vertical ore-waste changes was developed and is discussed in this paper. These two measures were combined to rank the realizations and to select mid, high, and low cases. The combined ranking resulted in ordering the realizations in a way that correlated with other measures of recoverable resource volumes, and lends support to the choice of the "mid" model (centrally located in the ranking) for use in detailed mine planning. The conditional simulation for Fort Hills marks the first time that stochastic modeling has been applied to full field modeling and then used for mine planning in an oil sands mine. The ranking method, including the methodology for assessing mining heterogeneity, is new and heretofore unpublished, and is the ultimate topic for discussion in this paper.