Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Ahmed Deif, Thejas Vivek

Abstract


The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined.

 

Doi: 10.28991/HIJ-SP2022-03-01

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Keywords


Artificial Intelligence; Development; Supply Chain; Oil and Gas; Petroleum Industry, Energy.

References


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.


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DOI: 10.28991/HIJ-SP2022-03-01

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