Journal of Mining Engineering

Journal of Mining Engineering

Locating of infill drill holes in order to reduce uncertainty in mineral reserve estimation using weighting and multivariate ranking techniques

Document Type : research - paper

Authors
1 GIS department, School of Surviving and Geospatial Engineering, College of Engineering, University of Tehran
2 School of Mining Engineering, University of Tehran, Tehran, Iran
Abstract
The topic of placement of infill drill holes and providing an optimal pattern for drilling drill hole has long been of interest in the mining process and has become an inseparable part of it. The root of this importance can be found in the financial issues related to the mining process as well as the exploration and extraction sector and issues related to classification. In order to reach an acceptable level of uncertainty, the estimation error method was used in this research to classify the blocks. Also, in order to place supplementary drill holes and weight potential drill holes, CRITIC and TOPSIS methods were used. The CRITIC method was used to assign weight and the TOPSIS method was used to select 5 of the best drill holes. At first, the drill holes were weighted by CRITIC method and with expert opinion, and then they were graded by TOPSIS method. The criteria used for the TOPSIS method include the grade of iron, weight percent grade of remaining iron (geometallurgical component), variance, error of estimation and classification. Finally, after placing the 5 wells in question, the estimation and classification process was carried out again, based on the number of definitively classified blocks, it was increased from 380 to 571, and the estimated average grade of iron also increased and 19% improvement in the classification was obtained. The improvement achieved in the number of blocks with definite classification adds to the certainty and reliability of the model.
Keywords
Subjects

D. S. F. Silva and J. B. Boisvert; 2014 “Mineral resource classification: A comparison of new and existing techniques,” J. South. African Inst. Min. Metall., vol. 114, no. 3, pp. 265–273## Fatehi, M., & Asadi, H. H. (2017). Data integration modeling applied to drill hole planning through semi-supervised learning: A case study from the Dalli Cu–Au porphyry deposit in the central Iran. Journal of African Earth Sciences, 128, 147–160## B. Sadeghi, N. Madani, and E. J. M. Carranza; 2015, “Combination of geostatistical simulation and fractal modeling for mineral resource classification,” J. Geochemical Explore., vol. 149, pp. 59–73## G. H. Blackwell, M. Anderson, and K. Ronson; 1999, “Simulated grades and open pit mine planning–resolving opposed positions,” Proc. 28th Symp. Appl. Comput. Oper. Res. to Miner. Ind. Color. Sch. Mines, Golden, Colo, pp. 205–215## D.S.F.Silva; 2015, “Mineral Resource Classification and Drill Hole Optimization Using Novel Geostatistical Algorithms with a Comparison to Traditional Techniques,” University of Alberta## X. Emery; 2007, “Conditioning simulations of Gaussian random fields by ordinary kriging,” Math. Geol., vol. 39, no. 6, pp. 607–623## Fatehi, M., Asadi, H. H., & Hossein Morshedy, A. 2020. 3D design of optimum complementary boreholes by integrated analysis of various exploratory data using a sequential-MADM approach. Natural Resources Research, 29(2), 1041-1061## Ramadhan, M. D., Marwanza, I., Nas, C., Azizi, M. A., Dahani, W., & Kurniawati, R; 2021. Drill Holes Spacing Analysis for Estimation and Classification of Coal Resources Based on Variogram and Kriging. In IOP Conference Series: Earth and Environmental Science, Vol. 819, No. 1## Ugurlu, O. F., & Kumral, M; 2020. Cost optimization of drilling operations in open-pit mines through parameter tuning. Quality Technology & Quantitative Management, 17(2), 173-185## Dailami, K., Nasriani, H. R., Sajjadi, S. A., Rafiee, M. R., Whitty, J., & Francis, J; 2020. Optimizing the ultimate recovery by infill drilling using streamline simulation. Acta Scientiarum. Technology, 42## Dirkx, R., & Dimitrakopoulos, R.; 2018, Optimizing infill drilling decisions using multi-armed bandits: Application in a longterm, multi-element stockpile. Mathematical Geosciences, 50, 35–52## C. Lantuéjoul; 2013, “Geostatistical simulation: models and algorithms,” Springer Sci. Bus. Media. M. E. Rossi; 1999. “Uncertainty and risk models for decision-making processes,” Int. Symp. Comput. Appl. Miner. Ind., vol. 28, pp. 185–195## M. E. Rossi and C. V. Deutsch, 2014, Mineral resource estimation. Springer Science & Business Media## A. Chibaya, “Geometallurgical analysis- Implications of operating flexibility; 2018 (A case for Geometallurgy for Orapa A/K1 deposit).le,” PhD Dissertation, University of the Witwatersrand## Fatehi, M., Asadi, H. H., & Hossein Morshedy, A; 2017. Designing infill directional drilling in mineral exploration by using particle swarm optimization algorithm. Arabian Journal of Geosciences## Soltani, S. and Hezarkhani, A; 2009. Additional exploratory boreholes optimization based on three-dimensional model of ore deposit, Archives of Mining Sciences, 54, 495–506## Soltani, S. and Hezarkhani, A; 2011. Determination of realistic and statistical value of the information gathered from exploratory drilling, Natural Resources Research, 20, (4), 207–216.## Soltani-Mohammadi, S. and Hezarkhani, A; 2013. A simulated annealing-based algorithm to locate additional drillholes for maximizing the realistic value of information, Natural Resources Research, 22, (3), 229–237.## [20] Soltani-Mohammadi, S., Amnieh, H. B. and Bahadori, M. 2012. Investigating ground vibration to calculate the permissible charge weight for blasting operations of Gotvand-Olya dam, underground structures, Archives of Mining Sciences, 57, (3), 687–697.## Szidarovszky, F.; 1983. “Multiobjective Observation Network Design for Regionalized Variables”, International Journal of Mining Engineering 1, pp 331- 342.## Hassanipak, A. A., & Sharafodin, M.; 2004. GET: A function for preferential site selection of additional borehole drilling. Exploration and Mining Geology, 13, 139–146.## R. D. Rein Drikx; 2017, “optimizing infill drilling decisions using multi-armed bandits: application in a long-term,multi-element stockpile.## M. A. Cuba, J. B. Boisvert, and C. V Deutsch; 2012, “Evaluation of Infill Drilling in the SLM Framework,” vol. 2012, pp. 1–8.## S. Soltani and M. Safa; 2015, “Optimally locating additional drill holes to increase the accuracy of ore/waste classification,” Trans. Institutions Min. Metall. Sect. A Min. Technol., vol. 124, no. 4, pp. 213–221.## G. Pan and A. Arik; 1993, “Restricted kriging for mixture of grade models,” Math. Geol., vol. 25, no. 6, pp. 713–736## R. Dimitrakopoulos, C. T. Farrelly, and M. Godoy, 2002, “Moving forward from traditional optimization: Grade uncertainty and risk effects in open-pit design,” Inst. Min. Metall. Trans. Sect. A Min. Technol., vol. 111, no## C.Wilde, B.J. and Deutsch; 2010, “Data Spacing and Uncertainty: Quantification and Complications,” Annu. Conf. Int. Assoc. Math. Geosci. August 29 - Sept. 2, Budapest, Hungary, 24 pages## Chou, D. and Schenk, D. E., 1983. “Optimum Locations for Exploratory Drill Holes”, International Journal of Mining Engineering 1, pp. 343-355.## Gershon, M., Allen, L.E., Manley, F., 1998. “Application of a new approach for drillholes location optimization”, International Journal of Mining, Reclamation and Environment 2, pp. 27-31.## Soltani, S. and A. Hezarkhani, 2009. “Additional exploratory boreholes optimization based on threedimensional model of ore deposit”, Archives of Mining Sciences 54: 495-506.## Kim, Y. C., Martino, F., Chopra, I., 1981.“”Application of geostatisticsin a coal deposit. Mining Engineering 33 (11), pp. 1476–1481## Walton, D.R. and Kauffman, P.W., 1982. “Some Practical Considerations in Applying Geostatistics to Coal Reserve Estimation”, SME-AIME, Dallas## Willam C, P. 1978. “Exploration And Mining Geology”, John Wiley & Sons, Inc. 430-432## Annels, Alwyn E., 1996. “Mineral Deposit Evaluation: A Practical Approach”, CHAPMAN &HALL, P. 436## Hasel A.A., 1938. “Sampling error in timber surveys”, Journal of Agricultural Research, vol. 57: 713-736## Mahalanobis P.C., 1940. “A sample survey of the acreage under jute in Bengal”, Sankhys, vol. 4: 511- 530## Quenouille M.H., 1949. “Problems in plane sampling”, Annals of Mathematical Statistics, vol. 20: 355-375## Das A.C., 1950. “Two-dimensional systematic sampling and the associated stratified and random sampling”, Sankhya, vol. 10: 95-108## Drew, k J., 1974. “”Estimation of Petroleum Exploration Success and the Effects of Resource Base Exhaustion via a Simulation Model. U.S. Geol. Survey Bull. v. 1328, 25p## Žižović, M., Miljković, B., & Marinković, D; 2020, Objective methods for determining criteria weight coefficients: A modification of the CRITIC method. Decision Making: Applications in Management and Engineering, 3(2), 149-161##‏ Akram, M., Dudek, W. A., & Ilyas, F; 2019. Group decision‐making based on pythagorean fuzzy TOPSIS method. International Journal of Intelligent Systems, 34(7), 1455-1475## Tuş, A., & Aytaç Adalı, E; 2019, The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem. Opsearch, 56(2), 528-538## Diakoulaki, D., Mavrotas, G., & Papayannakis, L; 1995, Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770## Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan & Levine, S. (2018). Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905## Park, J. H., Park, I. Y., Kwun, Y. C., & Tan, X; 2011. Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment. Applied Mathematical Modelling, 35(5), 2544-2556##‏ Ren, L., Zhang, Y., Wang, Y., & Sun, Z; 2007, Comparative analysis of a novel M-TOPSIS method and TOPSIS. Applied Mathematics Research eXpress## Çelikbilek, Y., & Tüysüz, F, 2020. An in-depth review of theory of the TOPSIS method: An experimental analysis. Journal of Management Analytics, 7(2), 281-300##‏ Roszkowska, E, 2011. Multi-criteria decision making models by applying the TOPSIS method to crisp and interval data. Multiple Criteria Decision Making/University of Economics in Katowice, 6(1), 200-230## Mücke, A., & Younessi, R. (1994). Magnetite-apatite deposits(Kiruna-type) along the Sanandaj-Sirjan zone and in the Bafq area, Iran, associated with ultramafic and calcalkaline rocks and carbonatites. Mineralogy and Petrology, 50(4), 219-244## Pourgholam, M. M., Afzal, P., Adib, A., Rahbar, K., & Gholinejad, M. (2022). Delineation of iron alteration zones using spectrum-area fractal model and TOPSIS decision-making method in tarom metallogenic zone, NW Iran. Journal of Mining and Environment (JME), 13(2), 503-525## Kousha Mining Consulting Engineers, Report on Estimation and Modeling of Anomaly No. 1 in Golgohar##
Volume 18, Issue 60
Autumn 2023
Pages 16-32

  • Receive Date 28 July 2022
  • Revise Date 25 May 2023
  • Accept Date 22 August 2023