Study on the Application of Machine Learning Technology in the Oilfield Investment Decision Making Process and the Long-term Impact on Economic Benefits

[1] Alabass, H. S. H. H. (2019). The impact of corporate investment behaviour on the corporate performance: Evidence from an emerging market. Journal of Management Information and Decision Sciences, 22(1), 1-10. Search in Google Scholar

[2] Weishäupl, E., Yasasin, E., & Schryen, G. (2018). Information security investments: An exploratory multiple case study on decision-making, evaluation and learning. Computers & Security, 77, 807-823. Search in Google Scholar

[3] Junaeni, I. (2020). Analysis of Factors That Influence Decision Making Invest in Capital Markets in Millennial Generations. International Journal of Accounting & Finance in Asia Pasific (IJAFAP), 3(3), 11-24. Search in Google Scholar

[4] Pravikov, O., Stetsyuk, V., & Denisov, V. (2018). Strategic change in investment policy rationale of enterprises modernization as a key condition for getting over economic crisis. Investment Management and Financial Innovations, 15(3), 212-222. Search in Google Scholar

[5] Lenkova, O. V., Permyakov, A. S., Yakunina, O. G., & Vechkasova, M. V. (2017). Problems and prospects of innovative development of petrochemical enterprises. International Journal of Energy Economics and Policy, 7(3), 321-325. Search in Google Scholar

[6] Sharma, D., & Gundimeda, H. (2023). Structural Efficiency of the Indian Petroleum Industry: Restructuring Options for Indian National Oil Companies. In Axes of Sustainable Development and Growth in India: Essays in Honour of Professor Jyoti K. Parikh (pp. 199-219). Singapore: Springer Nature Singapore. Search in Google Scholar

[7] Borodin, A., Panaedova, G., Ilyina, I., Harputlu, M., & Kiseleva, N. (2023). Overview of the Russian Oil and Petroleum Products Market in Crisis Conditions: Economic Aspects, Technology and Problems. Energies, 16(4), 1614. Search in Google Scholar

[8] Al-Samhan, M., Al-Fadhli, J., Al-Otaibi, A. M., Al-Attar, F., Bouresli, R., & Rana, M. S. (2022). Prospects of refinery switching from conventional to integrated: An opportunity for sustainable investment in the petrochemical industry. Fuel, 310, 122161. Search in Google Scholar

[9] Prakosa, H., & Prijadi, R. (2023). RECOVERING VALUE IN DISTRESSED COMPANIES: A CASE STUDY OF TUBAN PETROCHEMICAL INDUSTRIES AND ITS SUBSIDIARIES. Journal of Syntax Literate, 8(12). Search in Google Scholar

[10] Habibi, A., Heidarpoor, F., & Tavangar Hamzeh Kolaie, A. (2021). Assessing the Effect of Abnormal Stock Returns on the Content of the Autopoietic Restructuring Theory: A Case Study of Petrochemical Industry Companies. Petroleum Business Review, 5(4), 31-51. Search in Google Scholar

[11] Davis, G. A., & Lund, D. (2018). Taxation and investment decisions in petroleum. The Energy Journal, 39(6), 189-208. Search in Google Scholar

[12] Chandramauli, A., Sapaev, B., Alzubaidi, L. H., Pathak, S., Rai, A. K., Vennila, C., & Mouli, K. C. (2023, November). Revolutionizing Reservoir Management with Machine Learning: Enhancing Efficiency and Decision-Making in the Oil and Gas Industry. In 2023 International Conference for Technological Engineering and its Applications in Sustainable Development (ICTEASD) (pp. 293-299). IEEE. Search in Google Scholar

[13] Lawal, A., Yang, Y., He, H., & Baisa, N. L. (2024). Machine Learning in Oil and Gas Exploration-A Review. IEEE Access. Search in Google Scholar

[14] Ball, K., Arbus, T., Odi, U., & Sneed, J. (2017, July). The rise of the machines, analytics, and the digital oilfield: artificial intelligence in the age of machine learning and cognitive analytics. In SPE/AAPG/SEG Unconventional Resources Technology Conference (pp. URTEC-2668073). URTEC. Search in Google Scholar

[15] Ftiti, Z., Tissaoui, K., & Boubaker, S. (2022). On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach. Annals of Operations Research, 313(2), 915-943. Search in Google Scholar

[16] Anderson, R. N. (2017, December). ‘Petroleum Analytics Learning Machine’for optimizing the Internet of Things of today’s digital oil field-to-refinery petroleum system. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4542-4545). IEEE. Search in Google Scholar

[17] Mingyu, S., Jianjun, W., Chenggao, Y., Rong, C., & Yuanyuan, H. (2021). Study of Forecasting and Estimation Methodology of Oilfield Development Cost Based on Machine Learning. Chemistry and Technology of Fuels and Oils, 56(6), 1000-1019. Search in Google Scholar

[18] 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. Search in Google Scholar

[19] Fedorov, A. E., Povalyaev, A. A., Suleymanov, B. I., Dilmuhametov, I. R., & Sergeychev, A. V. (2020, October). Decision Support System for Tight Oil Fields Development Achimov Deposits and Their Analogues Using Machine Learning Algorithms. In SPE Russian Petroleum Technology Conference? (p. D023S007R001). SPE. Search in Google Scholar

[20] Ganiev, B. G., Nasybullin, A. V., Mannanov, I. I., Khayarova, D. R., Latifullin, F. M., Sattarov, R. Z., ... & Khafizov, R. R. (2020). Long-term investment planning methodology for oilfield development efficiency. EurAsian Journal of Biosciences, 14(2). Search in Google Scholar

[21] Hajizadeh, Y. (2019). Machine learning in oil and gas; a SWOT analysis approach. Journal of Petroleum Science and Engineering, 176, 661-663. Search in Google Scholar

[22] Hanga, K. M., & Kovalchuk, Y. (2019). Machine learning and multi-agent systems in oil and gas industry applications: A survey. Computer Science Review, 34, 100191. Search in Google Scholar

[23] İbrahim Enes Saglam & Lutfiye Karasaka. (2024). Evaluation of multi-camera images in different SfMMVS based photogrammetric software and comparison of digital products in generating 3D city models. Ain Shams Engineering Journal(5),102700-. Search in Google Scholar

[24] Yu Huang Kuang. (2009). A Hybrid GRA/MV Model for the Automatic Selection of Investment Portfolios with Minimum Risk and Maximum. Return.JOURNAL OF GREY SYSTEM(2),149-166. Search in Google Scholar

[25] Shaohua Yang,Xiaoxi Cao,Zong Yao Sun & Xia Li. (2024). Global adaptive stabilization for a class of uncertain multivariable systems with nonlinear parametrization. Nonlinear Dynamics(prepubulish),1-12. Search in Google Scholar

[26] Ma Mingjie & Yang Jianhui. (2021). A novel finite-time q-power recurrent neural network and its application to uncertain portfolio model. Neurocomputing137-146. Search in Google Scholar