Application of the JORC code to a complex Mozambique coal deposit

Classifying coal deposits in recently explored basins to internationally recognised reporting codes requires close attention to data quality and a comprehensive understanding of the geology.

The coal basins and sub-basins of the Zambezi coalfield are structurally complex, affected in varying degrees by intrusive bodies, with centimetre scale inter-bedded coal and shale comprising the target coal zones which are between 5 and 30m thick.

A recent Coal Resource Estimate of a Mozambique deposit was sampled with boreholes spaced between 100 and 1000m apart. In well sampled areas, the coal zones could be correlated between drillholes and faulted blocks and major structures could be modelled confidently. Intrusions and the extent of burning were modelled, extrapolated between drillhole intersections through the coal zones. However, in poorly drilled areas there is high ambiguity and little confidence in the interpretation.

Structural interpretation was primarily based on remote sensing data and recognisable coal zones. Intrusions could not be confidently correlated between fault blocks or the quality and thickness of contained clean coal estimated.

SRK used density, ash and thickness cut-offs to define the “clean” coal within the zone. This approach assumes that although the drill spacing does not support the delineation of individual coal bands, selective mining can separate them from the waste. Hence, the coal qualities and densities were only counted on the composited clean coal bands.

Clean coal quality grids show significant variations laterally across coal zones, indicating that coal quality cannot be inferred across sparsely drilled areas. In terms of sedimentology, the coal zones have high variability vertically but are less variable laterally. Where the whole coal zone was not sampled, a high level of variability introduces significant bias in the resource estimate.

SRK classified the deposit by considering each coal zone individually, assessing the quantity and quality of the data influencing the coal quality and thickness within and between individual fault zones, including availability of downhole geophysical logs and full seam intersection and analysis. Where intrusions appeared in the coal zones, SRK downgraded the area’s classification.

The coal model was tested for economic potential with a Pit Optimisation using a range of coal prices for high level fixed costs to ensure the resource was economically viable. The model used the ratio of clean coal within the zone to washability yield (processing recovery) to quantify the saleable coal. This excluded coal from depth, at the licence boundaries and in areas where the strip ratio was too high to support extraction.

SRK reported the clean coal (with associated partings) that fell within the pit optimisation shell as the Coal Resource.

Anna Fardell:


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