Advances in Machine Learning Algorithms for Predicting Reservoir Fluid Properties: A Comparative Framework

ABSTRACT

The accurate prediction of reservoir fluid properties is fundamental to optimizing reservoir management, production planning, and operational efficiency in the energy sector. Traditional methods often fail to address the complexities of fluid behavior, prompting the integration of machine learning (ML) techniques. This paper comprehensively explores ML algorithms, emphasizing their theoretical foundations, comparative performance, and practical applications in reservoir engineering. A detailed analysis highlights the strengths and limitations of commonly employed algorithms, including neural networks, support vector machines, and gradient boosting. Additionally, the paper delves into the transformative implications of ML for decision-making and operational efficiency while exploring its future potential when integrated with emerging technologies such as the Internet of Things and digital twins. This study aims to guide practitioners and researchers toward effective ML adoption and innovation in reservoir fluid property prediction, ultimately driving sustainable and cost-efficient energy practices by synthesizing key findings and providing actionable recommendations.

References

• [1] Ahmed, T. (2018). Reservoir engineering handbook: Gulf professional publishing.
• [2] Al-Zoubi, A. M., Hassonah, M. A., Heidari, A. A., Faris, H., Mafarja, M., & Aljarah, I. (2021). Evolutionary competitive swarm exploring optimal support vector machines and feature weighting. Soft Computing, 25(4), 3335-3352.
• [3] Aminu, M., Akinsanya, A., Dako, D. A., & Oyedokun, O. (2024). Enhancing cyber threat detection through real-time threat intelligence and adaptive defense mechanisms. International Journal of Computer Applications Technology and Research, 13(8), 11-27.
• [4] AMINU, M., AKINSANYA, A., OYEDOKUN, O., & TOSIN, O. (2024). A Review of Advanced Cyber Threat Detection Techniques in Critical Infrastructure: Evolution, Current State, and Future Directions.
• [5] Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., . . . Peacock, A. (2019). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and sustainable energy reviews, 100, 143-174.
• [6] Ara, A., Maraj, M. A. A., Rahman, M. A., & Bari, M. H. (2024). The Impact Of Machine Learning On Prescriptive Analytics For Optimized Business Decision-Making. International Journal of Management Information Systems and Data Science, 1(1), 7-18.
• [7] Awad, M., Khanna, R., Awad, M., & Khanna, R. (2015). Support vector machines for classification. Efficient learning machines: Theories, concepts, and applications for engineers and system designers, 39-66.
• [8] Bharadiya, J. P. (2023). The role of machine learning in transforming business intelligence. International Journal of Computing and Artificial Intelligence, 4(1), 16-24.
• [9] Cunningham, P., & Delany, S. J. (2021). K-nearest neighbour classifiers-a tutorial. ACM Computing Surveys (CSUR), 54(6), 1-25.
• Czajkowski, M., Jurczuk, K., & Kretowski, M. (2023). Steering the interpretability of decision trees using lasso regression-an evolutionary perspective. Information Sciences, 638, 118944.
• 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.
• Dindoruk, B., Ratnakar, R. R., & He, J. (2020). Review of recent advances in petroleum fluid properties and their representation. Journal of Natural Gas Science and Engineering, 83, 103541.
• Elete, T. Y., Nwulu, E. O., Omomo, K. O., & Emuobosa, A. (2022a). Data analytics as a catalyst for operational optimization: A comprehensive review of techniques in the oil and gas sector.
• Elete, T. Y., Nwulu, E. O., Omomo, K. O., & Emuobosa, A. (2022b). A generic framework for ensuring safety and efficiency in international engineering projects: Key concepts and strategic approaches.
• Elete, T. Y., Nwulu, E. O., Omomo, K. O., & Emuobosa, A. (2023). Alarm rationalization in engineering projects: analyzing cost-saving measures and efficiency gains.
• Guo, H., Dong, Y., Bastidas-Arteaga, E., & Lei, X. (2024). Life-cycle performance prediction and interpretation for coastal and marine RC structures: An ensemble learning framework. Structural Safety, 102496.
• Larestani, A., Hemmati-Sarapardeh, A., & Naseri, A. (2022). Experimental measurement and compositional modeling of bubble point pressure in crude oil systems: Soft computing approaches, correlations, and equations of state. Journal of Petroleum Science and Engineering, 212, 110271.
• Mao, J., & Ghahfarokhi, A. J. (2024). A Review of Intelligent Decision-Making Strategy for Geological CO2 Storage: Insights from Reservoir Engineering. Geoenergy Science and Engineering, 212951.
• Nami, N., & Hosseini-Motlagh, S.-M. (2022). Central robust decision-making structure for reverse supply chain: a real pharmaceutical case. Computers & Industrial Engineering, 173, 108726.
• Nwulu, E. O., Elete, T. Y., Aderamo, A. T., Esiri, A. E., & Erhueh, O. V. (2023). Promoting plant reliability and safety through effective process automation and control engineering practices.
• Nwulu, E. O., Elete, T. Y., Aderamo, A. T., Esiri, A. E., Omomo, K. O., & Nigeria, L. Optimizing shutdown and startup procedures in oil facilities: A strategic review of industry best practices.
• Nwulu, E. O., Elete, T. Y., Omomo, K. O., & Emuobosa, A. (2023). Revolutionizing turnaround management with innovative strategies: Reducing ramp-up durations post-maintenance.
• Okedele, P. O., Aziza, O. R., Oduro, P., & Ishola, A. O. (2024a). Assessing the impact of international environmental agreements on national policies: A comparative analysis across regions.
• Okedele, P. O., Aziza, O. R., Oduro, P., & Ishola, A. O. (2024b). Carbon pricing mechanisms and their global efficacy in reducing emissions: Lessons from leading economies.
• Okedele, P. O., Aziza, O. R., Oduro, P., & Ishola, A. O. (2024c). Climate change litigation as a tool for global environmental policy reform: A comparative study of international case law.
• OYEDOKUN, O., Ewim, S. E., & Oyeyemi, O. P. (2024a). A Comprehensive Review of Machine Learning Applications in AML Transaction Monitoring. Retrieved from https://www.ijerd.com/paper/vol20-issue11/2011730743.pdf
• OYEDOKUN, O., Ewim, S. E., & Oyeyemi, O. P. (2024b). Developing a conceptual framework for the integration of natural language processing (NLP) to automate and optimize AML compliance processes, highlighting potential efficiency gains and challenges Computer Science & IT Research Journal, 5(10), 2458–2484. doi:https://doi.org/10.51594/csitrj.v5i10.1675
• Oyedokun, O., Ewim, S. E., & Oyeyemi, O. P. (2024c). Leveraging advanced financial analytics for predictive risk management and strategic decision-making in global markets. Global Journal of Research in Multidisciplinary Studies, 2(02), 016-026.
• Prieto, A., Prieto, B., Ortigosa, E. M., Ros, E., Pelayo, F., Ortega, J., & Rojas, I. (2016). Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing, 214, 242-268.
• Rane, N. L., Paramesha, M., Choudhary, S. P., & Rane, J. (2024). Machine Learning and Deep Learning for Big Data Analytics: A Review of Methods and Applications. Partners Universal International Innovation Journal, 2(3), 172-197.
• Sibindi, R., Mwangi, R. W., & Waititu, A. G. (2023). A boosting ensemble learning based hybrid light gradient boosting machine and extreme gradient boosting model for predicting house prices. Engineering Reports, 5(4), e12599.
• Suryadevara, S., & Yanamala, A. K. Y. (2020). Fundamentals of Artificial Neural Networks: Applications in Neuroscientific Research. Revista de Inteligencia Artificial en Medicina, 11(1), 38-54.
• Uchendu, O., Omomo, K. O., & Esiri, A. E. The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models.
• Uchendu, O., Omomo, K. O., & Esiri, A. E. Conceptual advances in petrophysical inversion techniques: The synergy of machine learning and traditional inversion models. Engineering Science & Technology Journal, 5(11).
• Uchendu, O., Omomo, K. O., & Esiri, A. E. (2024a). Conceptual Framework for Data-driven Reservoir Characterization: Integrating Machine Learning in Petrophysical Analysis. Comprehensive Research and Reviews in Multidisciplinary Studies, 2(4), 001–013. doi:DOI:10.57219/crmms.2024.2.2.0041
• Uchendu, O., Omomo, K. O., & Esiri, A. E. (2024b). Strenghtening Workforce Stability by Mediating Labor Disputes Successfully. International Journal of Engineering Research and Development, 20(11), 98–1010.
• Uchendu, O., Omomo, K. O., & Esiri, A. E. (2024c). Theoritical Insights into Uncertainty Quantification in Reservoir Models: A Bayesian and Stochastic Approach. International Journal of Engineering Research and Development, 20(11), 987–997.
• Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361.

Download all article in PDF

WSN 211 (2026) 89-99

ADVERTISEMENT