Open Pit Porphyry Copper Mine-Block Inventory Update For Production Planning

PURPOSE OF UPDATED ESTIMATE FOR MINERAL INVENTORY BLOCKS During the production stage of an open pit porphyry copper mine it was observed that the expected production grade, as determined from the block inventory estimates, often differed greatly from the head grade of ore delivered to the mill. It was determined that, for purposes of production scheduling and monthly forecasting, better in situ grade estimates for mining blocks were necessary. Because all of the bench blastholes were being sampled, and periodic holes were being drilled into the next underlying bench, much more sample data existed than was being used for estimating the grade of nearby mining blocks. It was reasoned that, by periodically updating the mineral inventory block file over the benches scheduled for mining in the next time period using all existing data, better estimates and forecasts for production grade could be made. BLASTHOLE SAMPLE DATA MANAGEMENT The collars of all blastholes in each mined bench were surveyed and assays run on the cuttings representing the full 12-m (40-ft) bench height. The blastholes range from about 5 to 6 m (16 to 19 ft) apart along the front of the bench as well as perpendicular to the front. Overbreakage often left larger spacings between successive blasts. Fig. 17-1 is a plan map of blastholes on a typical mine bench. All blasthole data were keypunched to a format resembling that of the 12-m (40-ft) composite assay data file as follows: [Collar Coordinates Compite Assoy Hole ID Northing East Elewotion Total Copper Oxide Copper] Because of the many blasthole samples available, and in order not to use excessive computer time for running kriged estimates for 30.5 X 30.5 X 12-m (100 X 100 X 40 ft) blocks adjacent to and beneath the mined area, the decision was made to average all the blastholes falling with each 15.2 X 15.2-m (50 X 50-ft) mined block, and use the mean value of the samples as a regionalized variable for purposes of assigning kriged estimates and estimation variance to adjacent unmined blocks. In other words, instead of using individual blasthole samples for making kriged estimates, the holes were grouped by blocks and assay values were averaged and assigned to the centroid of the holes within the block, which was then treated as a single regional variable for purposes of kriging. See Fig. 17-2. VARIOGRAM COMPUTATIONS AND KRlGlNG RESULTS With the many blasthole samples it was possible to compute directional and vertical experimental variograms for both sulfide and nonsulfide copper assays falling within the enriched mineral zone for the full 12-m (40-ft) sample support. Due to the close spaced drilling, excellent definition of the experimental variograms was possible, and the spherical model exhibited good fits. A three-dimensional kriging program was then run over the two or three mine benches involved in the inventory update, and estimated grades reassigned to all mining blocks falling within the range of the new blasthole assay data according to the anisotropisms of the deposit. Better confidence limits could then be assigned to scheduled mining blocks and better short- range forecasts made. An interactive kriging computer program was also applied for the purpose of determining the kriging variance or estimation of error for larger, irregular mining blocks representing the monthly production from a particular bench. The interactive program permitted the operator to enter the limits of the irregular block onto the screen of a cathode ray tube (CRT) as a series of points around the perimeter of individual gridded blocks making up the larger irregular block. The computer then was programmed to calculate the kriging variance of the larger block using all samples fall- ing within range. Thus the limits of estimated grade could be established at any confidence level. Fig. 17-3 illustrates the output from the interactive kriging program showing the sample points entering into the grade and kriging variance computations, and also the kriging coefficient assigned by the computer to each sample.