Artificial Intelligence in Iran's Petrochemical Industry: Challenges and Solutions

Document Type : Policy Note

Authors

1 Electronics Research Institute, Sharif University of Technology, Tehran, Iran

2 Electrical Engineering Department, Sharif University of Technology

3 Sharif ISDS Center, Sharif University of Technology, Tehran, Iran.

10.22034/jstp.2025.11792.1825

Abstract

The Iranian petrochemical industry, as a cornerstone of the national economy, plays a critical role in revenue generation and energy security. However, it faces significant challenges, including global oil and gas price fluctuations, international sanctions, rising production costs, aging infrastructure, and an urgent need to enhance productivity and operational efficiency. Artificial intelligence (AI), as a transformative technology, offers immense potential to address these challenges. Through big data analysis, precise forecasting, process optimization, and automation of operations, AI can significantly improve decision-making, reduce costs, and increase efficiency in the petrochemical sector. Despite its potential, the implementation of AI in Iran's petrochemical industry encounters multiple barriers, such as technical limitations, a shortage of skilled professionals, inadequate infrastructure, and resistance to organizational change. This paper investigates Iran's position within the AI maturity hierarchy in the petrochemical sector, categorizing development into four levels: real-time monitoring, event prediction, outcome simulation, and full process automation. The findings reveal that Iran primarily operates at the early stages of AI maturity. Advancing to higher levels requires substantial investments in domestic technologies, infrastructure development, workforce training, and the establishment of comprehensive legal and ethical frameworks. This study concludes by offering actionable recommendations for policymakers and industry leaders to facilitate AI adoption and fully harness its opportunities, enabling the Iranian petrochemical industry to remain competitive in an increasingly dynamic global market.

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References

[1] Ministry of Petroleum of Iran. (2022). Annual Report of Iran's Petrochemical Industry. Ministry of Petroleum of Iran. {In Persian}
[2] Ghorbani, E. (2020). Exploration, Development and Production of Iran's Fields and Reservoirs through the Iran Petroleum Contract (IPC). Journal of Energy & Natural Resources Law, 38(4), 437–454.
doi: https://doi.org/10.1080/02646811.2019.1701806
[3] Hussain, M., et al. (2024). Application of Artificial Intelligence in the Oil and Gas Industry. In Engineering Applications of Artificial Intelligence (pp. 341–373). Springer. doi: 10.1007/978-3-030-12345-6_12
[4] Rad, S., et al. (2023). Effective Factors on Improving the Technological Capabilities of Pump Manufacturers in Iran's Oil And Gas Industry. Science and Technology Policy Journal, 16(4), 61–81.
doi: 10.22034/jstp.2024.11525.1707 {In Persian}
[5] Mohaghegh, S. D., & Khazaeni, Y. (2011). Application of Artificial Intelligence in the Upstream Oil and Gas Industry. International Journal of Computer Research, 18(3/4), 231.
[6] Saran, A., et al. (2023). Aging Midstream Supply Chain in the Oil and Gas Industry. The Journal of Energy and Development, 49(1/2), 211–226. doi: https://doi.org/10.56476/jed.v49i2.25
[7] Toniato, A., Schilter, O., & Laino, T. (2023). The Role of AI in Driving the Sustainability of the Chemical Industry. Chimia, 77(3), 144–149. doi: https://doi.org/10.2533/chimia.2023.144
[8] Wari, E., Zhu, W., & Lim, G. (2023). Maintenance in the Downstream Petroleum Industry: A Review on Methodology and Implementation. Computers & Chemical Engineering, 172, 108177. doi: 10.1016/j.compchemeng.2023.108177
[9] Ebrahimi, M., et al. (2013). New Technology Assessment: A Case Study of Iran’s Petrochemical Industry. The International Journal of Humanities, 20(3), 27–51. {In Persian} doi: https://dorl.net/dor/20.1001.1.25382640.2013.20.3.3.4
[10] Talks, M. U. N. (2024). AI in Oil and Gas Market to Reach Around USD 8.2 Bn by 2033. Link: https://www.linkedin.com/pulse/ai-oil-gas-market-reach-around-usd-82-bn-2033-markets-us-ygfrc
[11] Paulk, M. C., et al. (1991). Capability Maturity Model for Software. Citeseer. doi: 10.1109/52.219617
[12] Solis Pino, A. F., et al. (2024). Analysis of Enterprise Internet of Things Maturity Models: A Review. Applied Sciences, 14(21), 9859. doi: 10.3390/app14219859
[13] Madakam, S., Ramaswamy, R., & Tripathi, S. (2015). Internet of Things (IoT): A Literature Review. Journal of Computer and Communications, 3(5), 164–173. doi: 10.4236/jcc.2015.35021
[14] Hyndman, R. (2018). Forecasting: Principles and Practice. OTexts.
[15] Montgomery, D. C. (2017). Design and Analysis of Experiments. John Wiley & Sons.
[16] Alpaydin, E. (2020). Introduction to Machine Learning. MIT Press.
[17] Lee, J., Bagheri, B., & Kao, H.-A. (2015). A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems. Manufacturing Letters, 3, 18–23. doi: http://dx.doi.org/10.1016/j.mfglet.2014.12.001
[18] Predictive Maintenance in the Oil and Gas Industry link: https://llumin.com/predictive-maintenance-oil-and-gas/
[19] Venkatasubramanian, V. (2019). The Promise of Artificial Intelligence in Chemical Engineering: Is It Here, Finally? AIChE Journal, 65(1).
DOI: https://doi.org/10.1002/aic.16489
[20] Biegler, L. T., & Grossmann, I. E. (2004). Retrospective on Optimization. Computers & Chemical Engineering, 28(8), 1169–1192.
doi: 10.1016/j.compchemeng.2003.11.003
 

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