Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective
Abstract
Doi: 10.28991/HIJ-SP2022-03-01
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Lu, H., Guo, L., Azimi, M., & Huang, K. (2019). Oil and Gas 4.0 era: A systematic review and outlook. Computers in Industry, 111, 68–90. doi:10.1016/j.compind.2019.06.007.
Li, H., Yu, H., Cao, N., Tian, H., & Cheng, S. (2021). Applications of Artificial Intelligence in Oil and Gas Development. In Archives of Computational Methods in Engineering 28(3), 937–949. doi:10.1007/s11831-020-09402-8.
Evensen, O., Lin, S., Piotrowski, D., Womack, D., & Zaheer, A. (2020). Energizing the oil and gas value chain with AI. IBM Corporation. Available online: https://www.ibm.com/downloads/cas/Z79PALP0 (accessed on May 2021).
Shafiee, M., Animah, I., Alkali, B., & Baglee, D. (2019). Decision support methods and applications in the upstream oil and gas sector. Journal of Petroleum Science and Engineering, 173, 1173–1186. doi:10.1016/j.petrol.2018.10.050.
Shukla, A., & Karki, H. (2016). Application of robotics in onshore oil and gas industry-A review Part i. Robotics and Autonomous Systems, 75, 490–507. doi:10.1016/j.robot.2015.09.012.
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. doi:10.1016/j.cosrev.2019.08.002.
International Energy Agency. (2017). Digitization & Energy. International Energy Agency. Available online: https://iea.blob.core.windows.net/assets/b1e6600c-4e40-4d9c-809d-1d1724c763d5/DigitalizationandEnergy3.pdf (accessed on May 2021).
Biscardini, G., Rasmussen, E., Geissbauer, R., & Del Maestro, A. (2018). Drilling for data: Digitizing upstream oil and gas. In Strategy and PWC. Available online: https://www.strategyand.pwc.com/gx/en/insights/2018/drilling-for-data.html (accessed on May 2021).
Evensen, O., & Womack, D. (2020). How AI can pump new life into oilfields. IBM Corporation. Available online: https://www.ibm.com/downloads/cas/5BNKGNLE (accessed on December 2021).
Brun, A., Trench, M., & Vermaat, T. (2017). Why oil and gas companies must act on analytics. In McKinsey & Company (Issue October 2017), 1–5. Available online: https://www.mckinsey.com/industries/oil-and-gas/our-insights/why-oil-and-gas-companies-must-act-on-analytics (accessed on December 2021).
Molen, O. Van Der, Maximenko, A., & Verre, F. (2017). An analytical approach to maximizing reservoir production. (September 2017). Available online: https://mckinsey.com/~/media/mckinsey/industries/oil and gas/our insights/an analytical approach to reservoir production/an-analytical-approach-to-maximizing-reservoir-production-final.pdf?shouldIndex=false (accessed on December 2021).
Koroteev, D., & Tekic, Z. (2021). Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Energy and AI, 3, 2666–5468. doi:10.1016/j.egyai.2020.100041.
Osarogiagbon, A. U., Khan, F., Venkatesan, R., & Gillard, P. (2021). Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations. Process Safety and Environmental Protection, 147, 367–384. doi:10.1016/j.psep.2020.09.038.
Mohamadian, N., Ghorbani, H., Wood, D. A., Mehrad, M., Davoodi, S., Rashidi, S., Soleimanian, A., & Shahvand, A. K. (2021). A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning. Journal of Petroleum Science and Engineering, 196, 107811. doi:10.1016/j.petrol.2020.107811.
Ossai, C. I., & Duru, U. I. (2021). Applications and theoretical perspectives of artificial intelligence in the rate of penetration. Petroleum. doi:10.1016/j.petlm.2020.08.004.
Agwu, O. E., Akpabio, J. U., & Dosunmu, A. (2021). Modeling the downhole density of drilling muds using multigene genetic programming. Upstream Oil and Gas Technology, 6, 100030. doi:10.1016/j.upstre.2020.100030.
Agwu, O. E., Akpabio, J. U., Alabi, S. B., & Dosunmu, A. (2018). Artificial intelligence techniques and their applications in drilling fluid engineering: A review. Journal of Petroleum Science and Engineering, 167, 300–315. doi:10.1016/j.petrol.2018.04.019.
Ahmadi, M. A., Ebadi, M., & Yazdanpanah, A. (2014). Robust intelligent tool for estimating dew point pressure in retrograded condensate gas reservoirs: Application of particle swarm optimization. Journal of Petroleum Science and Engineering, 123, 7–19. doi:10.1016/j.petrol.2014.05.023.
Hourfar, F., Bidgoly, H. J., Moshiri, B., Salahshoor, K., & Elkamel, A. (2019). A reinforcement learning approach for waterflooding optimization in petroleum reservoirs. Engineering Applications of Artificial Intelligence, 77, 98–116. doi:10.1016/j.engappai.2018.09.019.
Rashid, S., Ghamartale, A., Abbasi, J., Darvish, H., & Tatar, A. (2019). Prediction of Critical Multiphase Flow through Chokes by Using a Rigorous Artificial Neural Network Method. Flow Measurement and Instrumentation, 69. doi:10.1016/j.flowmeasinst.2019.101579.
Aminu, K. T., McGlinchey, D., & Chen, Y. (2019). Optimal design for real-time quantitative monitoring of sand in gas flowline using computational intelligence assisted design framework. Journal of Petroleum Science and Engineering, 177, 1059–1071. doi:10.1016/j.petrol.2019.03.024.
Marins, M. A., Barros, B. D., Santos, I. H., Barrionuevo, D. C., Vargas, R. E. V., de M. Prego, T., de Lima, A. A., de Campos, M. L. R., da Silva, E. A. B., & Netto, S. L. (2021). Fault detection and classification in oil wells and production/service lines using random forest. Journal of Petroleum Science and Engineering, 197, 107879. doi:10.1016/j.petrol.2020.107879.
WANG, H., MU, L., SHI, F., & DOU, H. (2020). Production prediction at ultra-high water cut stage via Recurrent Neural Network. Petroleum Exploration and Development, 47(5), 1084–1090. doi:10.1016/S1876-3804(20)60119-7.
Naseri, S., Tatar, A., & Shokrollahi, A. (2016). Development of an accurate method to prognosticate choke flow coefficients for natural gas flow through nozzle and orifice type chokes. Flow Measurement and Instrumentation, 48, 1–7. doi:10.1016/j.flowmeasinst.2015.12.003.
Ayala H., L. F., Alp, D., & Al-Timimy, M. (2009). Intelligent design and selection of natural gas two-phase separators. Journal of Natural Gas Science and Engineering, 1(3), 84–94. doi:10.1016/j.jngse.2009.06.001.
Sadi, M., & Shahrabadi, A. (2018). Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation. Journal of Petroleum Science and Engineering, 171, 1211–1222. doi:10.1016/j.petrol.2018.08.041.
Syed, F. I., Alshamsi, M., Dahaghi, A. K., & Neghabhan, S. (2022). Artificial lift system optimization using machine learning applications. Petroleum. doi:10.1016/j.petlm.2020.08.003.
Al-Shabandar, R., Jaddoa, A., Liatsis, P., & Hussain, A. J. (2021). A deep gated recurrent neural network for petroleum production forecasting. Machine Learning with Applications, 3, 100013. doi:10.1016/j.mlwa.2020.100013.
Sheremetov, L. B., González-Sánchez, A., López-Yáñez, I., & Ponomarev, A. V. (2013). Time series forecasting: Applications to the upstream oil and gas supply chain. IFAC Proceedings Volumes (IFAC-PapersOnline), 46(9), 957–962. doi:10.3182/20130619-3-RU-3018.00526.
Garcia, C. A., Naranjo, J. E., Ortiz, A., & Garcia, M. V. (2019). An Approach of Virtual Reality Environment for Technicians Training in Upstream Sector. IFAC-PapersOnLine, 52(9), 285–91. doi:10.1016/j.ifacol.2019.08.222.
Wu, X., Li, C., Jia, W., & He, Y. (2014). Optimal operation of trunk natural gas pipelines via an inertia-adaptive particle swarm optimization algorithm. Journal of Natural Gas Science and Engineering, 21, 10–18. doi:10.1016/j.jngse.2014.07.028.
MohamadiBaghmolaei, M., Mahmoudy, M., Jafari, D., MohamadiBaghmolaei, R., & Tabkhi, F. (2014). Assessing and optimization of pipeline system performance using intelligent systems. Journal of Natural Gas Science and Engineering, 18, 64–76. doi:10.1016/j.jngse.2014.01.017.
Ben Seghier, M. E. A., Keshtegar, B., Taleb-Berrouane, M., Abbassi, R., & Trung, N. T. (2021). Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines. Process Safety and Environmental Protection, 147, 818–833. doi:10.1016/j.psep.2021.01.008.
Saade, M., & Mustapha, S. (2020). Assessment of the structural conditions in steel pipeline under various operational conditions – A machine learning approach. Measurement: Journal of the International Measurement Confederation, 166, 108262. doi:10.1016/j.measurement.2020.108262.
Neuroth, M., MacConnell, P., Stronach, F., & Vamplew, P. (2000). Improved modelling and control of oil and gas transport facility operations using artificial intelligence. Knowledge-Based Systems, 13(2), 81–92. doi:10.1016/S0950-7051(00)00049-6.
Nazari, A., Rajeev, P., & Sanjayan, J. G. (2015). Modelling of upheaval buckling of offshore pipeline buried in clay soil using genetic programming. Engineering Structures, 101, 306–317. doi:10.1016/j.engstruct.2015.07.013.
Shukla, A., & Karki, H. (2016). Application of robotics in offshore oil and gas industry-A review Part II. Robotics and Autonomous Systems, 75, 508–524. doi:10.1016/j.robot.2015.09.013.
Ibrahimov, B. (2018). A cost-oriented robot for the Oil Industry. IFAC-PapersOnLine, 51(30), 204–209. doi:10.1016/j.ifacol.2018.11.287.
Eze, P. C., & Masuku, C. M. (2018). Vapour–liquid equilibrium prediction for synthesis gas conversion using artificial neural networks. South African Journal of Chemical Engineering, 26, 80–85. doi:10.1016/j.sajce.2018.10.001.
Zaranezhad, A., Asilian Mahabadi, H., & Dehghani, M. R. (2019). Development of prediction models for repair and maintenance-related accidents at oil refineries using artificial neural network, fuzzy system, genetic algorithm, and ant colony optimization algorithm. Process Safety and Environmental Protection, 131, 331–348. doi:10.1016/j.psep.2019.08.031.
Arce-Medina, E., & Paz-Paredes, J. I. (2009). Artificial neural network modeling techniques applied to the hydrodesulfurization process. Mathematical and Computer Modelling, 49(1–2), 207–214. doi:10.1016/j.mcm.2008.05.010.
Al-Fattah, S. M. (2021). Application of the artificial intelligence GANNATS model in forecasting crude oil demand for Saudi Arabia and China. Journal of Petroleum Science and Engineering, 200, 108368. doi:10.1016/j.petrol.2021.108368.
Li, J., Wang, R., Wang, J., & Li, Y. (2018). Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms. Energy, 144, 243–264. doi:10.1016/j.energy.2017.12.042.
Silva, R. P., Fleury, A. T., Martins, F. P. R., Ponge-Ferreira, W. J. A., & Trigo, F. C. (2015). Identification of the state-space dynamics of oil flames through computer vision and modal techniques. Expert Systems with Applications, 42(5), 2421–2428. doi:10.1016/j.eswa.2014.10.030.
Sattari, F., Macciotta, R., Kurian, D., & Lefsrud, L. (2021). Application of Bayesian network and artificial intelligence to reduce accident/incident rates in oil & gas companies. Safety Science, 133, 104981. doi:10.1016/j.ssci.2020.104981.
Sakib, N., Ibne Hossain, N. U., Nur, F., Talluri, S., Jaradat, R., & Lawrence, J. M. (2021). An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network. International Journal of Production Economics, 235, 108107. doi:10.1016/j.ijpe.2021.108107.
Rachman, A., & Ratnayake, R. M. C. (2019). Machine learning approach for risk-based inspection screening assessment. Reliability Engineering and System Safety, 185, 518–532. doi:10.1016/j.ress.2019.02.008.
Single, J. I., Schmidt, J., & Denecke, J. (2020). Knowledge acquisition from chemical accident databases using an ontology-based method and natural language processing. Safety Science, 129. doi:10.1016/j.ssci.2020.104747.
DOI: 10.28991/HIJ-SP2022-03-01
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