Date Log
The role of artificial intelligence in the Upstream of the Oil Industry: a review
Investigación y Acción,
Vol. 3 No. 1 (2023): Investigación y Acción
Abstract
This review article addresses the integration and application of Artificial Intelligence (AI) in the hydrocarbon industry, highlighting its transformative role in process optimization, sustainability, and decision-making. It delves into multiple aspects of AI, from machine learning to convolutional neural networks, used in the petroleum ‘upstream,’ covering exploration, drilling, and production. Techniques of machine learning such as SVM and ANN are examined, applied to predict reservoir properties with the aim of enhancing efficiency in oil exploration. Future perspectives are discussed, including deep learning in seismic operations and the identification and mitigation of pollution. The potential of AI in reservoir engineering to streamline calculations, enhance scaling processes, and optimize production is emphasized. Despite advancements, challenges such as large-scale data management and technological investment are acknowledged. The article concludes that collaboration between hydrocarbon experts and AI technologists will be pivotal in overcoming challenges and fully leveraging opportunities in the convergence of these fields, paving the way towards a more efficient and sustainable hydrocarbon industry.
Keywords
References
[2] Z. Jin, “Hydrocarbon accumulation and resources evaluation: Recent advances and current challenges”, Advances in Geo-Energy Research, vol. 8, núm. 1, pp. 1–4, 2023, doi: 10.46690/ager.2023.04.01.
[3] S. Bahaloo, M. Mehrizadeh, y A. Najafi-Marghmaleki, “Review of application of artificial intelligence techniques in petroleum operations”, Petroleum Research, vol. 8, núm. 2. KeAi Publishing Communications Ltd., pp. 167–182, el 1 de junio de 2023. doi: 10.1016/j.ptlrs.2022.07.002.
[4] S. Choubey y G. P. Karmakar, “Artificial intelligence techniques and their application in oil and gas industry”, Artif Intell Rev, vol. 54, núm. 5, pp. 3665–3683, jun. 2021, doi: 10.1007/s10462-020-09935-1.
[5] A. L. D’Almeida, N. C. R. Bergiante, G. de Souza Ferreira, F. R. Leta, C. B. de Campos Lima, y G. B. A. Lima, “Digital transformation: a review on artificial intelligence techniques in drilling and production applications”, International Journal of Advanced Manufacturing Technology, vol. 119, núm. 9–10. Springer Science and Business Media Deutschland GmbH, pp. 5553–5582, el 1 de abril de 2022. doi: 10.1007/s00170-021-08631-w.
[6] L. Zhang, J. Ling, y M. Lin, “Artificial intelligence in renewable energy: A comprehensive bibliometric analysis”, Energy Reports, vol. 8. Elsevier Ltd, pp. 14072–14088, el 1 de noviembre de 2022. doi: 10.1016/j.egyr.2022.10.347.
[7] M. Wu et al., “An artificial intelligence course for chemical engineers”, Education for Chemical Engineers, vol. 45, pp. 141–150, oct. 2023, doi: 10.1016/j.ece.2023.09.004.
[8] F. Flores, “‘Upstream’ de Petróleo”.
[9] PDSVA, “Exploración”, Cuadernos de Soberanía Petrolera. 2018.
[10] J. F. L. Souza, G. L. Santana, L. V. Batista, G. P. Oliveira, E. Roemers-Oliveira, y M. D. Santos, “CNN Prediction Enhancement by Post-Processing for Hydrocarbon Detection in Seismic Images”, IEEE Access, vol. 8, pp. 120447–120455, 2020, doi: 10.1109/ACCESS.2020.3005916.
[11] M. Stadtműller y J. A. Jarzyna, “Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review”, Energies, vol. 16, núm. 10. MDPI, el 1 de mayo de 2023. doi: 10.3390/en16104215.
[12] M. Ramkumar, R. Nagarajan, y M. Santosh, “Advances in sediment geochemistry and chemostratigraphy for reservoir characterization”, Energy Geoscience, vol. 2, núm. 4, pp. 308–326, oct. 2021, doi: 10.1016/j.engeos.2021.02.001.
[13] P. Solanki, D. Baldaniya, D. Jogani, B. Chaudhary, M. Shah, y A. Kshirsagar, “Artificial intelligence: New age of transformation in petroleum upstream”, Petroleum Research, vol. 7, núm. 1. KeAi Publishing Communications Ltd., pp. 106–114, el 1 de febrero de 2022. doi: 10.1016/j.ptlrs.2021.07.002.
[14] S. Elkatatny, Z. Tariq, M. Mahmoud, y A. Abdulraheem, “New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs”, Petroleum, vol. 4, núm. 4, pp. 408–418, dic. 2018, doi: 10.1016/j.petlm.2018.04.002.
[15] PDVSA, “Produccion”, Cuadernos de Soberanía Petrolera. 2018.
[16] F. I. Syed, A. AlShamsi, A. K. Dahaghi, y S. Neghabhan, “Application of ML & AI to model petrophysical and geomechanical properties of shale reservoirs – A systematic literature review”, Petroleum, vol. 8, núm. 2. KeAi Communications Co., pp. 158–166, el 1 de junio de 2022. doi: 10.1016/j.petlm.2020.12.001.
[17] A. Ogbamikhumi y J. O. Ebeniro, “Reservoir properties estimation from 3D seismic data in the Alose field using artificial intelligence”, Journal of Petroleum Exploration and Production, vol. 11, núm. 3, pp. 1275–1287, mar. 2021, doi: 10.1007/s13202-021-01105-5.
[18] M. Salam, “Data-Driven Hydrocarbon Production Forecasting Using Machine Learning Techniques Spatio-Temporal Data Analysis View project”, International Journal of Computer Science and Information Security (IJCSIS), vol. 18, 2020, [En línea]. Disponible en: https://www.researchgate.net/publication/342643691
[19] F. I. Syed, T. Muther, A. K. Dahaghi, y S. Negahban, “AI/ML assisted shale gas production performance evaluation”, J Pet Explor Prod Technol, vol. 11, núm. 9, pp. 3509–3519, sep. 2021, doi: 10.1007/s13202-021-01253-8.
[20] L. Xue, J. Wang, J. Han, M. Yang, M. S. Mwasmwasa, y F. Nanguka, “Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data”, Advances in Geo-Energy Research, vol. 8, núm. 3, pp. 159–169, jun. 2023, doi: 10.46690/ager.2023.06.03.
[21] M. M. Shawkat, A. R. Bin Risal, N. J. Mahdi, Z. Safari, M. H. Naser, y A. W. Al Zand, “Fluid Flow Behavior Prediction in Naturally Fractured Reservoirs Using Machine Learning Models”, Complexity, vol. 2023, 2023, doi: 10.1155/2023/7953967.
[22] A. A. Mahmoud, S. Elkatatny, W. Chen, y A. Abdulraheem, “Estimation of oil recovery factor for water drive sandy reservoirs through applications of artificial intelligence”, Energies (Basel), vol. 12, núm. 19, sep. 2019, doi: 10.3390/en12193671.
[23] P. Kharazi Esfahani, K. Peiro Ahmady Langeroudy, y M. R. Khorsand Movaghar, “Enhanced machine learning—ensemble method for estimation of oil formation volume factor at reservoir conditions”, Sci Rep, vol. 13, núm. 1, dic. 2023, doi: 10.1038/s41598-023-42469-4.
[24] A. Cunha, A. Pochet, H. Lopes, y M. Gattass, “Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data”, Comput Geosci, vol. 135, feb. 2020, doi: 10.1016/j.cageo.2019.104344.
[25] R. Ayass, S. Mustapha, y D. Salam, “Quantification of Hydrocarbon Contamination in Soil Using Hyperspectral Data and Deep Learning”, en World Congress on Civil, Structural, and Environmental Engineering, Avestia Publishing, 2023. doi: 10.11159/iceptp23.192.
[26] R. Patowary, A. Devi, y A. K. Mukherjee, “Advanced bioremediation by an amalgamation of nanotechnology and modern artificial intelligence for efficient restoration of crude petroleum oil-contaminated sites: a prospective study”, Environmental Science and Pollution Research, vol. 30, núm. 30. Springer Science and Business Media Deutschland GmbH, pp. 74459–74484, el 1 de junio de 2023. doi: 10.1007/s11356-023-27698-4.
[27] I. Portugal, P. Alencar, y D. Cowan, “The use of machine learning algorithms in recommender systems: A systematic review”, Expert Systems with Applications, vol. 97. Elsevier Ltd, pp. 205–227, el 1 de mayo de 2018. doi: 10.1016/j.eswa.2017.12.020.
[28] E. Kuk, J. Stopa, M. Kuk, D. Janiga, y P. Wojnarowski, “Petroleum reservoir control optimization with the use of the auto-adaptive decision trees”, Energies (Basel), vol. 14, núm. 18, sep. 2021, doi: 10.3390/en14185702.
[29] S. Hu, H. Zhang, R. Zhang, L. Jin, y Y. Liu, “Quantitative interpretation of toc in complicated lithology based on well log data: A case of majiagou formation in the eastern ordos basin, china”, Applied Sciences (Switzerland), vol. 11, núm. 18, sep. 2021, doi: 10.3390/app11188724.
[30] F. I. Syed, S. Alnaqbi, T. Muther, A. K. Dahaghi, y S. Negahban, “Smart shale gas production performance analysis using machine learning applications”, Petroleum Research, vol. 7, núm. 1. KeAi Publishing Communications Ltd., pp. 21–31, el 1 de febrero de 2022. doi: 10.1016/j.ptlrs.2021.06.003.
[31] D. Orlov y D. Koroteev, “Advanced analytics of self-colmatation in terrigenous oil reservoirs”, J Pet Sci Eng, vol. 182, nov. 2019, doi: 10.1016/j.petrol.2019.106306.