IoT and Machine Learning for Enhanced Reservoir Management: Insights from South-Eastern Bangladesh
Keywords:
IoT, Machine Learning, Reservoir Management, Predictive Analytics, Real-Time Monitoring, South-Eastern Bangladesh, Data Integration, Anomaly Detection.Abstract
In the context of South-Eastern Bangladesh, this study explores the integration of Internet of
Things (IoT) technologies and Machine Learning (ML) techniques to enhance reservoir
management. The research evaluates how IoT sensors for real-time data collection and ML
algorithms for predictive analytics can optimize reservoir operations, improve accuracy in
reservoir modeling, and enhance decision-making processes. By implementing a case study
approach, the study demonstrates the practical applications of these technologies in monitoring
and managing oil reservoirs, providing insights into their impact on operational efficiency and
resource optimization. The results indicate significant improvements in predictive accuracy and
anomaly detection, showcasing the potential of combined IoT and ML approaches in modern
reservoir management practices.
