: This study investigates the transformative role of Big Data and Artificial Intelligence (AI) in combating oil theft and enhancing petroleum resource management, with a specific focus on Nigeria. Oil theft poses significant economic, security, and environmental challenges, undermining government revenues, corporate profitability, and national stability. By leveraging Big Data analytics from sensor networks, transactional records, and satellite imagery, theft patterns can be detected early, enabling proactive interventions. AI techniques—such as machine learning, neural networks, support vector machines, and anomaly detection—facilitate predictive maintenance, resource optimization, and automated decision-making in oilfield operations. The integration of these technologies creates a synergistic effect, allowing for real-time monitoring, anomaly identification, and predictive analytics across the petroleum supply chain. Emerging innovations including the Internet of Things (IoT), blockchain platforms, and remote monitoring systems further enhance transparency, traceability, and security. However, challenges persist regarding data integration, scalability, and ethical considerations such as privacy and fairness. The study emphasizes the need for supportive policies, regulatory cooperation, and international collaboration. Overall, Big Data and AI present robust, data-driven solutions for mitigating oil theft, optimizing resource allocation, and promoting sustainability in the petroleum industry.
Keywords
Big DataArtificial IntelligenceOil TheftResource ManagementNigeriaPredictive AnalyticsBlockchain
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How to cite this article
N. E. Bello, G. Nwosu (2024). Big Data and AI in Combating Oil Theft and Resource Management. Procedure International Journal of Science and Technology, 1(12), 46–56. https://www.pijst.com/article/pijst112d24004/big-data-and-ai-in-combating-oil-theft-and-resource-management
N. E. Bello, G. Nwosu. “Big Data and AI in Combating Oil Theft and Resource Management.” Procedure International Journal of Science and Technology, vol. 1, no. 12, 2024, pp. 46–56. https://www.pijst.com/article/pijst112d24004/big-data-and-ai-in-combating-oil-theft-and-resource-management
N. E. Bello, G. Nwosu. “Big Data and AI in Combating Oil Theft and Resource Management.” Procedure International Journal of Science and Technology 1, no. 12 (2024): 46–56. https://www.pijst.com/article/pijst112d24004/big-data-and-ai-in-combating-oil-theft-and-resource-management
N. E. Bello, G. Nwosu (2024) ‘Big Data and AI in Combating Oil Theft and Resource Management’, Procedure International Journal of Science and Technology, 1(12), pp. 46–56. Available at: https://www.pijst.com/article/pijst112d24004/big-data-and-ai-in-combating-oil-theft-and-resource-management.
N. E. Bello, G. Nwosu, “Big Data and AI in Combating Oil Theft and Resource Management,” Procedure International Journal of Science and Technology, vol. 1, no. 12, pp. 46–56, 2024. https://www.pijst.com/article/pijst112d24004/big-data-and-ai-in-combating-oil-theft-and-resource-management.
N. E. Bello, G. Nwosu. Big Data and AI in Combating Oil Theft and Resource Management. Procedure International Journal of Science and Technology. 2024;1(12):46–56. https://www.pijst.com/article/pijst112d24004/big-data-and-ai-in-combating-oil-theft-and-resource-management.
N. E. Bello, G. Nwosu. Big Data and AI in Combating Oil Theft and Resource Management. Procedure International Journal of Science and Technology 2024, 1 (12), 46–56. https://www.pijst.com/article/pijst112d24004/big-data-and-ai-in-combating-oil-theft-and-resource-management.
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Bahalul Haque, A. K. M., Rifat Hasan, M., & Oahiduzzaman Mondol Zihad, M. (2021). SmartOil: Blockchain and smart contract-based oil supply chain management. arXiv. Source
Dabab, M., Craven, R., Barham, H., & Gibson, E. (2018). Exploratory strategic roadmapping framework for big data privacy issues. CORE. Source
Hua Tan, K., Ortiz-Gallardo, V. G., & Perrons, R. K. (2016). Using big data to manage safety-related risk in the upstream oil & gas industry: A research agenda. CORE. Source
Idachaba, F. E. (2014). Exception based monitoring of oil and gas pipelines using VTOL type-unmanned aerial vehicles (UAV). CORE. Source
Jani, K. (2016). The promise and prejudice of big data in intelligence community. arXiv. Source
Lopes D’Almeida, A., Carvalho Rosa Bergiante, N., de Souza Ferreira, G., Rodrigues Leta, F., Benevenuto de Campos Lima, C., & Brito Alves Lima, G. (2022). Digital transformation: A review on artificial intelligence techniques in drilling and production applications. Frontiers in Artificial Intelligence. (Online). Source
Ryan, M., Antoniou, J., Jiya, T., Macnish, K., Brooks, L., & Stahl, B. C. (2019). Technofixing the future: Ethical side effects of using AI and big data to meet the SDGs. CORE. Source
Salem, A. M., Yakoot, M. S., & Mahmoud, O. (2022). Addressing diverse petroleum industry problems using machine learning techniques: Literary methodology— Spotlight on predicting well integrity failures. Frontiers in Artificial Intelligence. Source
Sun Lim, S., & Bouffanais, R. (2022). ‘Data dregs’ and its implications for AI ethics: Revelations from the pandemic. Humanities and Social Sciences Communications. Source
Thiago, R., Souza, R., Azevedo, L., Soares, E., Santos, R., Santos, W., De Bayser, M., Cardoso, M., Moreno, M., & Cerqueira, R. (2020). Managing data lineage of O&G machine learning models: The sweet spot for shale use case. arXiv. Source
Toma, C., & Popa, M. (2018). IoT security approaches in oil & gas solution industry 4.0. CORE. Source
Wang, T., Wei, Q., Xiong, W., Wang, Q., Fang, J., Wang, X., Liu, G., Jin, C., & Wang, J. (2024). Current status and prospects of artificial intelligence technology application in oil and gas field development. Frontiers in Energy Research. Source
