Estimation of lateritic nickel resources using the Inverse Distance Weighting (IDW) method at PT Five Star Indonesia, Petasia District, Central Sulawesi Province - Stephen Stephen; Mohamad Haifan (Priviet Social Sciences Journal, 2026)
This study aims to estimate lateritic nickel resources in the Western Block of PT Five Star Indonesia and to map the spatial distribution of Ni and Fe grades to support technical decision-making. The analysis uses drillhole data with an approximate 25 m spacing. Laterite intervals are grouped into low-grade (LGO), medium-grade (MGO), and high-grade (HGO) zones based on Ni content, and a 3D block model is constructed with a 2×2×2 m block size. Grade estimation is performed using the Inverse Distance Weighting (IDW) method to generate grade and volume models. Tonnage is calculated using a bulk density of 1.6 t/m³. The resource estimation results indicate a total of 3,334,400 tonnes with average grades of 1.41% Ni and 33.59% Fe, classified as Measured Resources. Spatially, the HGO zone tends to extend toward the southeast, whereas LGO and MGO are more widely distributed in other parts of the block, highlighting grade variability across zones that is visible in cross-sections and interpolation outputs. These findings confirm laterite heterogeneity, which should be managed through grade control and more selective mining boundary definition. In addition to quantitative estimates, this study provides contour maps and sections illustrating grade changes across the block, enabling the identification of priority areas. Model outputs are evaluated by checking consistency between estimated grades and nearby drillhole data and by reviewing the continuity of grade zones along strike and dip directions. This approach helps flag uncertainty early for field verification and model updates during the initial mine planning and feasibility stages. The results provide an initial input for pit design, production scheduling, and selective-mining strategies. Further work is recommended to conduct infill drilling and compare IDW with geostatistical kriging to improve estimation robustness.