Case Study: Leading Coal Mining Company improves coal seam detection and boosts recovery with DataCloud's MinePortal

Increasing Waste Material on Top of Coal Seams Magnifies Inefficiencies

DataCloud worked with a leading coal mining company that was struggling with inaccurate coal seam detection during production blast hole drilling. Under-drilling and over-drilling were causing costly redrills, backfilling, wasted material, and delays, while existing bulk density and natural gamma radiation methods were too slow and expensive to provide timely guidance.

DataCloud implemented MinePortal, a real-time coal seam detection solution that ingests drilling data and applies geostatistical and machine learning algorithms to identify the boundary between overburden and coal, recommend stand-off distances, and predict seam depths in later benches. The result was faster, more reliable seam detection that improved drilling and blasting decisions, reduced coal loss and waste dilution, and could help a typical mine recover an estimated $7.5M per year.