[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 opportunities.
Expert Systems with Applications,
216, 119456.
https://doi.org/10.1016/j.eswa.2022.119456
[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 classification.
arXiv 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 gaps.
Journal of Science and Technology Policy,
16(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 model.
Information systems frontiers,
21, 719-734.
https://doi.org/10.1007/s10796-017-9774-y
[9] Ali, W., & Khan, A. Z. (2024). Factors influencing readiness for artificial intelligence: a systematic literature review. Data Science and Management.
[10] Uren, V., & Edwards, J. S. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management, 68, 102588.
[11] Jöhnk, J., Weißert, M., & Wyrtki, K. (2021).
Ready or not, AI comes—an interview study of organizational AI readiness factors.
Business & Information Systems Engineering,
63(1), 5-20.
https://doi.org/10.1007/s12599-020-00676-7
[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
[16] Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021).
Application of machine learning and artificial intelligence in oil and gas industry.
Petroleum Research,
6(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 fields.
Computer Science & IT Research Journal,
5(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 maintenance.
Computer Science & IT Research Journal,
5(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 challenges.
IEEE access,
8, 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 Safety.
Hydrogen,
5(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 Europe.
International journal of information management,
65, 102497.
https://doi.org/10.1016/j.ijinfomgt.2022.102497
[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 Industry.
Journal of Science and Technology Policy,
16(4), 61-81.{In Persian}
https://doi.org/10.22034/jstp.2024.11525.1707