Machine Learning and IoT in Reservoir Performance Assessment: A Case Study from South-Eastern Bangladesh

Abstract

The integration of Machine Learning (ML) and the Internet of Things (IoT) has emerged as a
transformative approach in reservoir performance assessment, offering enhanced data analytics,
real-time monitoring, and operational efficiency. This case study explores the application of ML
algorithms and IoT technologies in evaluating reservoir performance in the south-eastern region
of Bangladesh. By leveraging IoT sensors for real-time data acquisition and ML models for
predictive analysis, the study provides a comprehensive assessment of reservoir characteristics and
behavior. The IoT infrastructure enabled continuous monitoring of key parameters such as
pressure, temperature, and flow rates, while ML algorithms processed these data to predict
reservoir performance and optimize management strategies. The study demonstrates significant
improvements in accuracy and responsiveness compared to traditional methods, with ML models
achieving lower error metrics and IoT systems facilitating proactive decision-making. The findings
highlight the potential of combining ML and IoT to revolutionize reservoir management practices,
offering a scalable and efficient solution for modern petroleum engineering challenges.