Assessing And Analyzing the Readiness of the Upstream Oil Industry To Adopt Artificial Intelligence

Document Type : Original Article

Authors

1 M.A. Student of Information Technology Management, Shahid Beheshti University, Tehran, Iran

2 Assistant Professor, Department of Science and Technology Policy, Institute for Basic Studies of Science and Technology, Shahid Beheshti University, Tehran, Iran.

3 M.A. Student of Information Technology Management, Shahid Beheshti University, Tehran, Iran.

Abstract

Artificial Intelligence (AI) has emerged as one of the most significant transformative technologies in recent years, profoundly impacting various industries. In the oil industry, particularly in the upstream sector, AI has the potential to enhance operational efficiency, improve decision-making, and reduce costs. Given this importance, along with the strategic significance of the oil industry in Iran's economy, this study was conducted to assess the organizational readiness of Iran's upstream oil industry for adopting AI technology.
In this study, a questionnaire was designed based on a selected framework, encompassing five main indicators: strategic alignment, resources, knowledge, culture, and data—each further divided into sub-indicators. These sub-indicators include AI business potential, customer readiness, senior management support, organizational process alignment, financial resources, skilled workforce, IT infrastructure, AI awareness, skill enhancement, AI ethics, innovation, teamwork, change management, data accessibility, data quality, and data flow. The questionnaire was completed by a group of experienced managers and experts in AI and the oil industry.
The results indicate that strategic alignment received the highest score, while the data indicator scored the lowest among the main categories. The findings highlight the necessity of developing strategic programs and strengthening data infrastructure to ensure the successful adoption of AI in the upstream oil sector.

Keywords

Main Subjects


[1] Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., & Kuusk, A. (2023). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunitiesExpert Systems with Applications216, 119456.  https://doi.org/10.1016/j.eswa.2022.119456
[2]  Singla, A., Sukharevsky, A., Yee, L., Chui, M., & Hall, B. (2024). The state of AI in early 2024: Gen AI adoption spikes and starts to generate valueMcKinsey & Company.https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
[3] Koroteev, D., & Tekic, Z. (2021). Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the futureEnergy and AI3, 100041. https://doi.org/10.1016/j.egyai.2020.100041
[4] Roustazadeh, A., Ghanbarian, B., Male, F., Shadmand, M. B., Taslimitehrani, V., & Lake, L. W. (2022). Estimating oil recovery factor using machine learning: applications of XGBoost classificationarXiv preprint arXiv:2210.16345.https://doi.org/10.48550/arXiv.2210.16345
[5] Abdelhamid, K., Ammar, T. B., & Laid, K. (2021, January). Artificial Intelligent in Upstream Oil and Gas Industry: A Review of Applications, Challenges and Perspectives. In International Conference on Artificial Intelligence and its Applications (pp. 262-271). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-96311-8_24
[6] Kanaani, F., Rasoulian, P., Hafezi, R., & Ahangari, S. S. (2023). Analysis of the artificial intelligence ecosystem in Iran and identifying institutional and functional gapsJournal of Science and Technology Policy16(2), 59-77.{In Persian}. https://doi.org/10.22034/jstp.2023.11303.1648
[7] Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical modelInformation systems frontiers21, 719-734. https://doi.org/10.1007/s10796-017-9774-y
[8] Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road aheadJournal of the association for Information Systems17(5), 328-376. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2800121
[9] Ali, W., & Khan, A. Z. (2024). Factors influencing readiness for artificial intelligence: a systematic literature reviewData Science and Management.
[10] Uren, V., & Edwards, J. S. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical studyInternational Journal of Information Management68, 102588.
[11] Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes—an interview study of organizational AI readiness factorsBusiness & Information Systems Engineering63(1), 5-20.https://doi.org/10.1007/s12599-020-00676-7
[12] Rouhani, A. A., & Mohammad Abadi, R. (2022). Investigating the artificial intelligence application in oil and gas supply chain. Farayandno17(79), 57-73.https://doi.org/10.22034/farayandno.2023.1974530.1896
[13]Lawal, A., Yang, Y., He, H., & Baisa, N. L. (2024). Machine Learning in Oil and Gas Exploration-A ReviewIEEE Access.https://doi.org/10.1109/ACCESS.2023.3349216
[14] Wang, Y. (2022, June). Heterogeneous Seismic Waves Pattern Recognition in Oil Exploration with Spectrum Imaging. In 2022 7th International Conference on Computational Intelligence and Applications (ICCIA) (pp. 190-194). IEEE. https://doi.org/10.1109/ICCIA55271.2022.9828424
[15] Chelliah, P. R., Jayasankar, V., Agerstam, M., Sundaravadivazhagan, B., & Cyriac, R. (2023). The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry: Envisaging AI-inspired Intelligent Energy Systems and Environments. John Wiley & Sons. https://www.google.com/books/edition/The_Power_of_Artificial_Intelligence_for/4e_kEAAAQBAJ?hl=en&gbpv=0
[16] Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021). Application of machine learning and artificial intelligence in oil and gas industryPetroleum Research6(4), 379-391. https://doi.org/10.1016/j.ptlrs.2021.05.009
[17] Daramola, G. O., Jacks, B. S., Ajala, O. A., & Akinoso, A. E. (2024). AI applications in reservoir management: optimizing production and recovery in oil and gas fieldsComputer Science & IT Research Journal5(4), 972-984. https://doi.org/10.51594/csitrj.v5i4.1083
[18] Jambol, D. D., Sofoluwe, O. O., Ukato, A., & Ochulor, O. J. (2024). Transforming equipment management in oil and gas with AI-Driven predictive maintenanceComputer Science & IT Research Journal5(5), 1090-1112. https://doi.org/10.51594/csitrj.v5i5.1117
[19] Wanasinghe, T. R., Wroblewski, L., Petersen, B. K., Gosine, R. G., James, L. A., De Silva, O., ... & Warrian, P. J. (2020). Digital twin for the oil and gas industry: Overview, research trends, opportunities, and challengesIEEE access8, 104175-104197. https://doi.org/10.1109/ACCESS.2020.2998723
[20] Patil, R. R., Calay, R. K., Mustafa, M. Y., & Thakur, S. (2024). Artificial Intelligence-Driven Innovations in Hydrogen SafetyHydrogen5(2), 312-326. https://doi.org/10.3390/hydrogen5020018
[21] Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western EuropeInternational journal of information management65, 102497. https://doi.org/10.1016/j.ijinfomgt.2022.102497
[22] Alsheibani, S., Cheung, Y., & Messom, C. (2018). Artificial intelligence adoption: AI-readiness at firm-level. In Pacific Asia Conference on Information Systems 2018 (p. 37). Association for Information Systems. https://research.monash.edu/en/publications/artificial-intelligence-adoption-ai-readiness-at-firm-level
[23] Holmström, J. (2022). From AI to digital transformation: The AI readiness frameworkBusiness Horizons65(3), 329-339. https://doi.org/10.1016/j.bushor.2021.03.006
[24] Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach's alphaBmj314(7080), 572. https://doi.org/10.1136/bmj.314.7080.572
[25] Adorno, O. D. A. (2020). Business process changes on the implementation of artificial intelligence (Doctoral dissertation, Universidade de São Paulo)https://doi.org/10.11606/D.12.2020.tde-08042021-011316.
[2] Sahari Rad, R., & Razavi, S. M. R. (2023). Effective Factors on Improving the Technological Capabilities of Pump Manufacturers in Iran's Oil And Gas IndustryJournal of Science and Technology Policy16(4), 61-81.{In Persian} https://doi.org/10.22034/jstp.2024.11525.1707