Artificial Intelligence in Mining, Petroleum and Natural Gas Extraction Process Optimization
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I1P115Keywords:
Artificial Intelligence, Deep Learning, Drilling Optimization, Machine Learning, Mineral Processing, Natural Gas Forecasting, Predictive Maintenance, Reservoir SimulationAbstract
The global transition toward sustainable energy systems and the escalating demand for critical minerals and hydrocarbons have placed unprecedented pressure on the extractive industries. Mining, petroleum, and natural gas operations face compounding challenges, including declining ore grades, complex reservoir dynamics, volatile commodity prices, and stringent environmental regulations. In response, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies capable of optimizing the entire extraction value chain. This manuscript provides a comprehensive analysis of AI applications in the extractive sectors, focusing on process optimization from geological exploration to predictive maintenance. It examines the deployment of deep learning for seismic interpretation and ore grade prediction, reinforcement learning for autonomous drilling and reservoir simulation, and data-driven models for flotation and production forecasting. Furthermore, this paper addresses the critical imperatives of operational safety and carbon footprint reduction, demonstrating how AI-enabled digital twins and hazard prediction models mitigate risks and enhance sustainability. Through a synthesis of recent technological advancements, this study underscores that AI integration is not merely an operational upgrade, but a fundamental paradigm shift necessary for the future viability of the extractive industries.
References
[1] C. C. Corrigan and S. A. Ikonnikova, “A review of the use of AI in the mining industry: Insights and ethical considerations for multi-objective optimization,” The Extractive Industries and Society, vol. 17, p. 101440, Mar. 2024.
[2] A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, “Application of machine learning and artificial intelligence in oil and gas industry,” Petroleum Research, vol. 6, no. 4, pp. 379–391, Dec. 2021.
[3] H. Rahmanifard and T. Plaksina, “Application of artificial intelligence techniques in the petroleum industry: a review,” Artificial Intelligence Review, vol. 52, pp. 2295–2318, 2019.
[4] F. Azhari, C. C. Sennersten, and C. A. Lindley, “Deep learning implementations in mining applications: a compact critical review,” Artificial Intelligence Review, vol. 56, pp. 11091–11124, 2023.
[5] F. S. Boukredera, M. R. Youcefi, and A. Hadjadj, “Enhancing the drilling efficiency through the application of machine learning and optimization algorithm,” Engineering Applications of Artificial Intelligence, vol. 123, p. 106198, 2023.
[6] A. Samnioti and V. Gaganis, “Applications of machine learning in subsurface reservoir simulation—A review—Part II,” Energies, vol. 16, no. 18, p. 6727, 2023.
[7] R. Miftakhov, A. Al-Qasim, and I. Efremov, “Deep reinforcement learning: reservoir optimization from pixels,” in Proc. International Petroleum Technology Conference (IPTC), 2020.
[8] D. Sen, K. I. Hamurcuoglu, M. Z. Ersoy, and K. M. M. Tunç, “Forecasting long-term world annual natural gas production by machine learning,” Resources Policy, vol. 80, p. 103175, 2023.
[9] A. Szmigiel, D. B. Apel, K. Skrzypkowski, L. Wojtecki, and Y. Pu, “Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review,” Minerals, vol. 14, no. 4, p. 331, 2024.
[10] D. Ali and S. Frimpong, “Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector,” Artificial Intelligence Review, vol. 53, pp. 5525–5542, 2020.
[11] L. S. Vinay, R. K. Paswan, and A. K. Verma, “Machine learning approach for the prediction of mining-induced stress in underground mines to mitigate ground control disasters,” Geomechanics and Geophysics for Geo-Energy and Geo-Resources, vol. 9, 2023.
[12] M. Bouchard, A. Desbiens, and R. del Villar, “Control of grinding mill circuits using model predictive control,” Minerals Engineering, vol. 35, pp. 43–53, 2022.
[13] International Energy Agency (IEA), “Digitalisation and Energy,” IEA Technology Report, Nov. 2017. [Online]. Available: https://www.iea.org/reports/digitalisation-and-energy.
[14] Z. Hyder, K. Siau, and F. Nah, “Artificial intelligence, machine learning, and autonomous technologies in mining industry,” in Research Anthology on Cross-Disciplinary Designs and Applications of Automation, IGI Global, 2022.
[15] R. Ghosh, “Applications, promises and challenges of artificial intelligence in mining industry: A review,” TechRxiv, 2023.
[16] A. Zeb et al., “A case study on data-driven multivariate multistep forecasting of gold concentrate grade in a flotation process,” Resources Policy, vol. 95, p. 105148, 2024.
[17] C. A. Beeche, J. M. Luxbacher, and M. Karmis, “Computational risk modeling of underground coal mines using machine learning,” Safety Science, vol. 163, p. 106133, 2023.
[18] MDPI, “Predictive Maintenance in Underground Mining Equipment Using Artificial Intelligence,” Applied System Innovation, vol. 6, no. 10, p. 261, 2023.
[19] R. Miftakhov, A. Al-Qasim, and I. Efremov, “Deep reinforcement learning for gas lift optimization,” in Proc. SPE-204006-MS, International Petroleum Technology Conference (IPTC), 2021.
[20] Y. L. Dong et al., “Deep Forest Modeling: An Interpretable Deep Learning Approach for Mineral Exploration,” Journal of Geophysical Research: Machine Learning and Computation, vol. 1, p. e2024JH000311, 2024.
[21] H. R. Hanif, “The Role of Artificial Intelligence in Optimizing Oil Exploration and Production,” European Journal of Computational Mechanics and Physics Research, vol. 3, no. 5, pp. 176–190, 2024.
[22] NTWIST, “AI-Powered Predictive Maintenance for Mining Operations,” NTWIST Technical White Paper, 2023. [Online]. Available: https://ntwist.com.
[23] S. Siami-Namini, N. Tavakoli, and A. S. Namin, “A Comparison of ARIMA and LSTM in Forecasting Time Series,” in Proc. IEEE International Conference on Big Data (Big Data), 2018, pp. 1394–1401.
[24] International Energy Agency (IEA), “Net Zero by 2050: A Roadmap for the Global Energy Sector,” IEA Special Report, May 2021. [Online]. Available: https://www.iea.org/reports/net-zero-by-2050.
