Optimizing Drilling in Brownfield Ni-Cu Depositional Systems Based on the Integration of Geochemical, Geophysical and Drill-Hole Data

Effective drillhole placement is critical to the success of mineral exploration, particularly in brownfield settings where subsurface information remains sparse despite the availability of data from adjacent, previously explored areas. To address the challenges of uncertainty in resource estimation and the high cost of drilling, we present a drilling sequence optimization framework guided by geophysical and surface geochemical data. The framework integrates statistical learning and geostatistical simulation to construct a set of prior models of intrusion and nickel grade distribution. These models are used to quantify the expected reduction in uncertainty for each potential drillhole by evaluating their corresponding Efficacy of Information (EOI). This approach allows the sequential selection of drillhole locations that maximize information gain while managing exploration risk. We apply the methodology to a case study in the Curaçá Valley, Brazil, where prior data from a well-characterized nearby zone inform predictions in the adjacent target area. The results demonstrate that incorporating prior geological knowledge from nearby areas into the drilling strategy can significantly improve targeting efficiency and reduce uncertainty in early-stage brownfield exploration.