Comparison of three geostatistical approaches to quantify the impact of drill spacing on resource confidence for a coal seam (with a case example from Moranbah North, Queensland, Australia)

Three approaches to characterising the uncertainty associated with coal resource estimates are presented and compared: global estimation variance (GEV); local confidence intervals via the discrete Gaussian model (DGM); and the conditional simulation (CS) approach. The methods are applied and compared for three variables (Thickness, Yield and Sulphur) in a coal deposit at Moranbah North.All three approaches result in a broadly similar characterisation of uncertainty, but each has associated strengths and weaknesses. GEV is appropriate for 2D situations (like coal seams), is fundamentally robust (if its assumptions are respected), it is straightforward and is quick to apply. However, being global the results are somewhat limited (although in some instances a global result may still be ‘fit for purpose’), and importantly, it does not properly account for skewness and proportional effect. DGM is more sophisticated and accounts properly for skewness and proportional effect. It allows assessment of local uncertainty at the block scale whilst also being relatively computationally efficient but requires increased expertise to implement. CS also provides local results and conceptually is the most rigorous solution. However CS is the most computationally intensive solution and requires significant amounts of user input and validation. A short study on the influence of the number of realisations on the reliability of uncertainty assessments made from CS models is also documented.

► Global Estimation Variance is straightforward and quick to apply to 2D coal seams.
► It is robust for attributes that are not significantly skewed/heteroscedastic.
► Discrete Gaussian Model and Conditional Simulation handle such cases.