The future of AI in critical mineral exploration
Abstract
The energy transition through increased electrification has put the world’s attention on critical mineral exploration. Despite the promise of a growing demand, the global exploration industry is a money-losing enterprise. Even with increased investments, a decrease in new discoveries has taken place over the last two decades. In the paper, I propose a solution to this problem where AI is implemented as the enabler of a rigorous scientific method for mineral exploration that aims to reduce cognitive bias & false positives, enhances the role of domain experts, and drive down the cost of exploration. The current organization of exploration activities involving many fields of science (geology, geochemistry, geophysics) is no longer effective in discovering deposits under cover. In particular, the current approach fails to adequately quantify uncertainty, leading to suboptimal decision-making and $ spent on drilling that often result in false positives. Instead, I propose a new scientific method that is based on a philosophical approach founded on the principles of Bayesianism and falsification. In this approach, data acquisition is, in the first place, seen as a means to falsify human-generated hypotheses. The decision of what data to acquire next is quantified with verifiable metrics and based on rational decision-making. A practical protocol is provided that can be used as a template in any exploration campaign. However, in order to make this protocol practical, various forms of artificial intelligence are needed. I will argue that the most important forms are 1) novel unsupervised learning methods that collaborate with domain experts to better understand data and generate multiple competing geological hypotheses, and 2) human-in-theloop AI algorithms that can optimally plan various geological, geophysical, geochemical, and drilling data acquisition, where uncertainty reduction of geological hypotheses precedes the uncertainty reduction on grade and tonnage. The approach will be illustrated using several ongoing exploration cases.
Document Type: Original article
Cited as: Caers, J. The future of AI in critical mineral exploration. Sustainable Earth Resources Communications, 2025, 1(2): 69-82. https://doi.org/10.46690/serc.2025.02.05
DOI:
https://doi.org/10.46690/serc.2025.02.05Keywords:
Critical minerals; mineral exploration; artificial intelligence; sustainability; educationReferences
Abdelaziem, O. E., Gawish, A., Farrag, S. F. Application of computer vision in machine learning-based diagnosis of water production mechanisms in oil wells. SPE Journal, 2023, 28(5): 20.
Alqahtani, B. A., Alqahtani, M. A., Alqahtani, A. M. Electric submersible pump setting depth optimization- a field case study. Presented at the SPE Middle East Artificial Lift Conference and Exhibition, Manama, Bahrain, 25 October, 2022.
Cao, Z., Song, Q., Xing, K., et al. A novel multihead attention-enhanced convolutional neural network-long short-term memory encoder-decoder for oil production forecasting. SPE Journal, 2025, 30(3): 14.
Chen, M., Tang, J., Zhu, D. Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks. SPE Journal, 2022, 27(2): 1341-1353.
Chen, Y., Onur, M., Kuzu, N., et al. Prediction and history matching of observed production rate and bottomhole pressure data sets from in-situ crosslinked polymer gel conformance treatments using machine learning methods. SPE Journal, 2025, 30(4): 21.
Chen, Z., Jeffrey, R, G. Finite-Element Simulation of a Hydraulic Fracture Interacting with a Natural Fracture. SPE Production & Operations, 2017, 22: 219–234.
Chen, Z., Li, D., Dong, P., et al. A deep learning-based surrogate model for pressure transient behaviors in shale wells with heterogeneous fractures. Transport in Porous Media, 2023, 149(1): 345–371.
Cheng, S., Wu, B., Zhang, M., et al. Surrogate modeling and global sensitivity analysis for the simultaneous growth of multiple hydraulic fractures. Computers and Geotechnics, 2023, 162: 105709.
Dykstra, H., Parsons, R. L. The Prediction of Oil Recovery by Waterflooding in Secondary Recovery of Oil in the United States. Washington, API, 1950.
Fan, X., Wu, H., Zhang, H. Research on Neural Network Expert Systems and Their Application in Fracturing and Acidification Decision-Making. Petroleum Exploration and Development, 1998, 25(1): 73-76.
Fu, P., Johnson, S. M., Carrigan, C. R. Simulating Complex Fracture Systems in Geothermal Reservoirs Using an Explicitly Coupled Hydro-Geomechanical Model. Presented at the 45th U.S. Rock Mechanics/Geomechanics Symposium, San Francisco, California, June 2011.
Gardehansen, H. Inflation in the oil industry in the 1970-1980 decade, and its significance. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, September 1980.
Geertsma, J., De Klerk, F. A rapid method of predicting width and extent of hydraulically induced fractures. Journal of Petroleum Technology, 1969, 21(12): 1571-1581.
Gong, B., Wang, H., Song, W., et al. Integrated intelligent decision-making technology for deep coalbed methane geology and engineering based on bigdata analysis algorithms. Acta Petrolei Sinica, 2023, 44: 1949-1958.
Goswami, S., Yin, M., Yu, Y., et al. A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials. Computer Methods in Applied Mechanics and Engineering, 2022, 391: 114587.
Guo, J., Ren, W., Zeng, F., et al. Unconventional Oil and Gas Well Fracturing Parameter Intelligent Optimization: Research Progress and Future Development Prospects. Petroleum Drilling Techniques, 2023, 51: 1-7.
Hamid, O., Almani, T., Alqannas, S. Coupling Fluid Flow and Geomechanical Deformation Using AI & FEM Approaches. Presented at the International Geomechanics Symposium, Al Khobar, Saudi Arabia, October 2023.
Harris, J. C., Kuznetsov, K., Peyrard, C., et al. Simulation of wave forces on a gravity based foundation by a BEM based on fully nonlinear potential flow. Presented The 27th International Ocean and Polar Engineering Conference, San Francisco, CA, USA, Jun 2017.
Holditch, S. A., Robinson, B. M., Whitehead, W. S. Prefracture and Postfracture Formation Evaluation Necessary to Characterize the Three-Dimensional Shape of a Hydraulic Fracture. SPE Form Eval, 1987, 2(4): 523–534.
Hua, J., Du, X., Dong, Y., et al. Shale gas production prediction based on modified graph attention and memory-augmented neural networks available to purchase. SPE Journal, 2025, 30(5): 17.
Hubbert, M. K., Rubey, W. W. Role of fluid pressure in mechanics of overthrust faulting, I. mechanics of fluid-filled porous solids and its application to overthrust faulting: reply to discussion by francis birch. GSA Bulletin, 1961, 72(9): 1445-1451.
Irwin, G. R. Analysis of stresses and strains near the end of a crack traversing a plate. Journal of Applied Mechanics, 1957, 24(3): 361-364.
Jiang, T., Wang, Y., Ding, Y., et al. Expert system for economic optimization of hydraulic fracturing design. Acta Petrol Sinica, 2004, 25(1): 66-69.
Johnson, C. E. Prediction of oil recovery by waterflood - a simplified graphical treatment of the dykstra-parsons method. Journal of Petroleum Technology, 1956, 8(11): 55-56.
Khristianovich, S. A., Zheltov, Y. P. Formation of Vertical Fractures by Means of Highly Viscous Liquid. Presented at the 4th World Petroleum Congress, Rome, Italy, June 1955.
Leblanc, M., Suh, K., Machovoe, S., et al. Theory and practice of a flexible fiber-optic cable in a horizontal well used for crosswell and microseismic hydraulic fracture monitoring. SPE Journal, 2023, 28(3): 1453-1469.
Lemaitre, M.-P., Bismut, M. Reconstitution of the attitude of a probe-rocket head destined to analyse the infrared horizon. IFAC Proceedings Volumes, 1970, 3(1): 367-374.
Li, H., Aslam, B. M., Yan, B. An integrated deep learning and physics-constrained upscaling workflow for robust permeability prediction in digital rock physics. SSRN 2024, Available online: http://dx.doi.org/10.2139/ssrn.5050784 (accessed on 17 December 2025).
Liu, C., Wu, K., Wu, J., et al. An accurate and efficient fracture propagation model auto-calibration workflow for unconventional reservoirs. Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA, June 2022.
Ma, X., Zhang, K., Wang, J., et al. An efficient spatial-temporal convolution recurrent neural network surrogate model for history matching. SPE Journal, 2022, 27(2): 1160-1175.
Mohammed, S., Rezaee, A., Bazargan, M., et al. Investigating the role of temperature on thermal stress and fracture propagation in geothermal systems. Presented at the 51st U.S. Rock Mechanics/Geomechanics Symposium, San Francisco, California, USA, June 2017.
Mondal, S., Garusinghe, A., Ziman, S., et al. Efficiency and effectiveness - A fine balance: an integrated system to improve decisions in real-time hydraulic fracturing operations. Presented at the SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, Texas, USA, February 2022.
Mu, S., Zhao, L., Liu, Y. Surrogate model of shale stress based on Plackett-Burman and central composite design. SPE Journal, 2024, 29(12): 6563-6582.
National Energy Administration of China. (2020). Shale Gas Development Plan (2016-2020). Available online: https://zfxxgk.nea.gov.cn/auto86/201609/t20160930_2306.htm (accessed on 4 November 2025).
Nguyen, Q. M. A deep-learning-based reservoir surrogate for performance forecast and nonlinearly constrained life-cycle production optimization under geological uncertainty. Presented at the SPE Europe Energy Conference and Exhibition, Turin, Italy, June 2024.
Nierode, D. E. Comparison of hydraulic fracture design methods to observed field results. Journal of Petroleum Technology, 1985, 37(10): 1831–1839.
Nordgren, R. P. Propagation of a vertical hydraulic fracture. Society of Petroleum Engineers Journal, 1972, 12(4): 306-314.
Papamichos, E., Vardoulakis, I. The Coupling Effect of Surface Instabilities and Surface Parallel Griffith Cracks in Rock. Paper presented at the ISRM International Symposium, Pau, France, August 1989.
Perkins, T. K., Kern, L. R. Widths of hydraulic fractures. Journal of Petroleum Technology, 1961, 13(9): 937-949.
Popa, A., Wood, W. Application of case-based reasoning for well fracturing planning and execution. Journal of Natural Gas Science & Engineering, 2011, 3(6): 687-696.
Qiao, J., Gao, F., Wu, D., et al. Combining machine learning and physics modelling to determine the natural cave property with fracturing curves. Presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, Atlanta, Georgia, USA, June 2023.
Qu, H. Y., Zhang, J. L., Zhou, F. J. Evaluation of hydraulic fracturing of horizontal wells in tight reservoirs based on the deep neural network with physical constraints. Petroleum Science, 2023, 20(2): 1129-1141.
Ramirez, A., Iriarte, J. Event Recognition on Time Series Frac Data using Machine Learning–Part II. Presented at the SPE Liquids-Rich Basins Conference - North America, Odessa, Texas, USA, November 2019.
Rice, J. R. A path independent integral and the approximate analysis of strain concentration by notches and cracks. Journal of Applied Mechanics, 1968, 35: 379-386.
Robinson, M. E., Kurtz, W. L. Competitive patterns in tile electric utility market-the fossil fuels. Journal of Petroleum Technology, 1961, 13(11): 1071-1074.
Scott, W. H. Directional Stability and Control of Sailing Yachts. Presented at the SNAME 1st Chesapeake Sailing Yacht Symposium, Annapolis, Maryland, USA, January 1974.
Song, H., Du, S., Yang, J., et al. Evaluation of hydraulic fracturing effect on coalbed methane reservoir based on deep learning method considering physical constraints. Journal of Petroleum Science and Engineering, 2022, 212: 110360.
Sui, W., Wen, C., Sun, W. Joint application of distributed optical fiber sensing technologies for hydraulic fracturing monitoring. Natural Gas Industry, 2023, 43(2): 87-103.
Surtman, M., Rozlan, M. R., Rasid, M. F., et al. Smart fracturing system for screenless sand control strategy in cemented Monobore completions. Presented at ADIPEC, Abu Dhabi, UAE, November 2024.
Tariq, Z., Alnakhli, A., Abdulraheem, A., et al. Core scale fem modeling of thermochemical fracturing on cement cube samples. Presented at the Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, November 2021.
Wang, D., Dong, Y., Sun, D., et al. A three-dimensional numerical study of hydraulic fracturing with degradable diverting materials via CZM-based FEM. Engineering Fracture Mechanics, 2020b, 237: 107251.
Wang, D., Dong, Y., Wei, C., et al. Expansion-induced fracture propagation in deep geothermal reservoirs under alternate-temperature loading. Advances in Geo-Energy Research, 2025b, 15(3): 261-272.
Wang, D., Li, Z., Fu, Y. Production forecast of deep-coalbed-methane wells based on long short-term memory and Bayesian optimization. SPE Journal, 2024a, 29(7): 3651–3672.
Wang, D., Qin, H., Zheng, C., et al. Transport mechanism of temporary plugging agent in complex fractures of hot dry rock: a numerical study. Geothermics, 2023a, 111: 102714.
Wang, D., Qu, Z., Liu, C., et al. A numerical investigation into the propagation of acid-etched wormholes in geothermal wells. Unconventional Resources, 2024b, 4: 100083.
Wang, D., Zhou, F., Ding, W., et al. A numerical simulation study of fracture reorientation with a degradable fiber-diverting agent. Journal of Natural Gas Science and Engineering, 2015, 25: 215-225.
Wang, D., Zlotnik, S., Díez, P., et al. A numerical study on hydraulic fracturing problems via the proper generalized decomposition method. Computer Modeling in Engineering and Sciences, 2020a, 122(2): 703–720.
Wang, H., Marongiu-Porcu, M., Economides, M, J. Poroelastic and poroplastic modeling of hydraulic fracturing in brittle and ductile formations. SPE Production and Operations, 2016, 31(1): 13.
Wang, L., Chen, W., Sui, Q. Study of hydro-mechanical behaviours of rough rock fracture with shear dilatancy and asperities using shear-flow model. Journal of Rock Mechanics and Geotechnical Engineering, 2024c, 16(10): 13.
Wang, L., Yin, Z. Y., Chen, W. Characteristics of crack growth in brittle solids with the effects of material heterogeneity and multi-crack interaction. International Journal of Fracture, 2024d, 246(1): 77-99.
Wang, X., Li, P., Jia, K., et al. SPI-MIONet for surrogate modeling in phase-field hydraulic fracturing. Computer Methods in Applied Mechanics and Engineering, 2024e, 427: 117054.
Wang, Y., Zheng, L., Chen, G., et al. A genetic particle swarm optimization with policy gradient for hydraulic fracturing optimization. SPE Journal, 2025a, 30(2), 13.
Wang, Y., Zhou, F., Zhang, Y. Prediction of Diverting Performance of VES Acid Based on SVM and Coupled Rheology-Reaction Model. Presented at The 57th U.S. Rock Mechanics/Geomechanics Symposium, Atlanta, Georgia, USA, 25 June, 2023b.
Yu, Z., Yan, D., Adenutsi, C. D. (2025). Geologically constrained deep learning for lithofacies identification of mixed terrestrial shale reservoirs: permian fengcheng formation, mahu sag, junggar basin, western china available to purchase. SPE Journal, 2025, 30(5): 20.
Zhang, D., Li, H. Efficient surrogate modeling based on improved vision transformer neural network for history matching. SPE Journal, 2023, 28(6): 3046-3062.
Zhang, J., Liu, Y., Zhang, F., et al. Integrating petrophysical, hydrofracture, and historical production data with self-attention-based deep learning for shale oil production prediction. SPE Journal, 2024a, 29(12): 22.
Zhang, L., Zhang, H., Zhu, X., et al. Multistep-ahead prediction of logging-while-drilling resistivity curves based on seismic-guided seq2seq-long short-term memory. SPE Journal, 2024b, 29(10): 19.
Zhang, M., Ayala, L. The dual-reciprocity boundary element method solution for gas recovery from unconventional reservoirs with discrete fracture networks. SPE Journal, 2020, 25: 2898-2914.
Zhang, S., Zhang, C., Zhang, H., et al. Prediction of the Flow-Induced Vibration for the Power Cable of Offshore Wind Turbine Based on LSTM Model. Presented at The 35th International Ocean and Polar Engineering Conference, Seoul, Korea, 1-6 June, 2025.
Zhang, X., Sloan, S. W., Vignes, C., et al. A modification of the phase-field model for mixed mode crack propagation in rock-like materials. Computer Methods in Applied Mechanics and Engineering, 2017, 322: 123-136.
Zheng, F., Ma, M., Viswanathan, H., et al. Deep learning–assisted multiobjective optimization of geological CO2 storage performance under geomechanical risks. SPE Journal, 2025a, 30(4): 16.
Zheng, L., Liang, T., Wan, Y. Intelligent Prediction of Shale Oil Fracturing Curves Based on a SequenceTo-Sequence Model. Presented at SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA, 9-11 June, 2025b.
Zhou, Y., Tan, X., Yang, D., et al. A numerical method to consider the interaction between multiple fractures in frozen rocks based on XFEM. Computers and Geotechnics, 2024, 169: 106240.
Zhou, Z., Zhang, F., Fu, H., et al. A thermal-mechanical coupled dem model for anisotropic deep shale reservoir rocks. Presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, Atlanta, Georgia, USA, June 2023.
Zhu, H., Huang, C., Tang, X., et al. Multicluster fractures propagation during temporary plugging fracturing in naturally fractured reservoirs integrated with dynamic perforation erosion. SPE Journal, 2023, 28(4): 17.
Zhuang, X., Zhou, S., Huynh, G. D., et al. Phase field modeling and computer implementation: a review. Engineering Fracture Mechanics, 2022, 262: 108234.
文档 2（ mineral processing 主题）参考文献
Amini, S. H. Optimization of mineral processing circuit design under uncertainty. Morgantown, West Virginia University, 2017.
Amini, S. H., Noble, A. Design of cell-based flotation circuits under uncertainty: A techno-economic stochastic optimization. Minerals, 2021, 11: 459.
Arief, M., Alonso, Y., Oshiro, C., et al. Managing geological uncertainty in critical mineral supply chains: A POMDP approach with application to us lithium resources, arXiv 2025, arXiv:2502.05690. Available online: https://arxiv.org/abs/2502.05690 (accessed on December 2025).
Bai, Z., Gao, P., Chu, M., et al. Artificial intelligence of mineral processing process: A review of research progress. Journal of Environmental Chemical Engineering, 2025, 13(5): 118313.
Bascur, O. Process control and operational intelligence, in SME Mineral Processing and Extractive Metallurgy Handbook, Society for Mining, Metallurgy, and Exploration (SME), edited by R. C. Dunne and S. K. Kawatra, pp. 277–316, 2019.
Concha A, F., Bascur, O. A. The Engineering Science of Mineral Processing: A Fundamental and Practical Approach, CRC Press, 2024.
Ding, J., Chai, T., Wang, H., et al. Knowledge-based global operation of mineral processing under uncertainty. IEEE Transactions on Industrial Informatics, 2012, 8: 849–859.
Hodouin, D. Methods for automatic control, observation, and optimization in mineral processing plants. Journal of Process Control, 2011, 21: 211–225.
Hodouin, D., Jämsä-Jounela, S.-L., Carvalho, M., et al. State of the art and challenges in mineral processing control. Control Engineering Practice, 2001, 9: 995–1005.
International Energy Agency (IEA). (2021). The role of critical minerals in clean energy transitions, 2021. Available online: https://www.iea.org/reports/the-role-of-critical-minerals-in-clean-energy-transitions (accessed on December 2025).
Jiang, Y., Fan, J., Chai, T., et al. Lewis, Data-driven flotation industrial process operational optimal control based on reinforcement learning. IEEE Transactions on Industrial Informatics, 2017, 14: 1974–1989.
Jovanović, I., Miljanović, I. Contemporary advanced control techniques for flotation plants with mechanical flotation cells–A review. Minerals Engineering, 2015, 70: 228–249.
Koch, P.-H., Rosenkranz, J. Sequential decision-making in mining and processing based on geometallurgical inputs. Minerals Engineering, 2020, 149: 106262.
Kochenderfer, M. J., Wheeler, T. A., Wray, K. H. Algorithms for decision making. MIT press, 2022.
Koermer, S. C. Bayesian methods for mineral processing operations. Blacksburg, Virginia, Virginia Polytechnic Institute and State University, 2022.
Koermer, S., Noble, A. (2025). Optimization of a metallurgical process with uncertain dynamics. Available online: (accessed on December 2025).
Lee, H., Calvin, K., Dasgupta, D., et al. (2023). Synthesis report of the IPCC sixth assessment report (AR6). Available online: https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf (accessed on December 2025).
McCoy, J. T., Auret, L. Machine learning applications in minerals processing: A review. Minerals Engineering, 2019, 132: 95–109.
Shean, B., Cilliers, J. A review of froth flotation control. International Journal of Mineral Processing, 2011, 100: 57–71.
Silver, D., Veness, J. (2010) Monte-Carlo planning in large POMDPs. Available online: https://proceedings.neurips.cc/paper/2010/file/edfbe1afcf9246bb0d40eb4d8027d90f-Paper.pdf (accessed on December 2025).
Sunberg, Z., Kochenderfer, M. Online algorithms for POMDPs with continuous state, action, and observation spaces. Proceedings of the International Conference on Automated Planning and Scheduling, 2018, 28(1): 259–263.
U.S. Geological Survey. (2024). Phosphate rock. Available online: https://pubs.usgs.gov/periodicals/mcs2024/mcs2024-phosphate.pdf (accessed on January 2024).
Välikangas, H., Ohenoja, M., Brochot, S., et al. Evaluation of model uncertainty propagation in mineral process flowsheet designs. Scandinavian Simulation Society, 2025, 456–463.
World Bank. (2025). World Bank Commodities Price Data. Available online: https://thedocs.worldbank.org/en/doc/18675f1d1639c7a34d463f59263ba0a2-0050012025/related/CMO-PinkSheet-April-2025.pdf (accessed on 2 April 2025).
World Meteorological Organization (WMO). (2025). WMO confirms 2024 as warmest year on record at about 1.55 °C above pre-industrial level, 2025. Available online: https://wmo.int/news/media-centre/wmo-confirms-2024-warmest-year-record-about-155degc-above-pre-industrial-level (accessed on 10 January 2025).
Xiang, X., Foo, S. Recent advances in deep reinforcement learning applications for solving partially observable markov decision processes (POMDP) problems: Part 1-fundamentals and applications in games, robotics and natural language processing. Machine Learning and Knowledge Extraction, 2021, 3: 554–581.
Xiang, X., Foo, S., Zang, H. Recent advances in deep reinforcement learning applications for solving partially observable markov decision processes (POMDP) problems part 2-applications in transportation, industries, communications and networking and more topics. Machine Learning and Knowledge Extraction, 2021, 3: 863–878.
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