Artificial Intelligence in Reservoir Performance Analysis: Applications in South-Eastern Bangladesh

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in various sectors, including
reservoir performance analysis. This study explores the applications of AI in the assessment and
optimization of reservoir performance, focusing specifically on the oil fields in South-Eastern
Bangladesh. By leveraging advanced AI techniques such as machine learning algorithms, neural
networks, and real-time data analytics, the research aims to enhance the accuracy and efficiency
of reservoir assessments. The study integrates AI methodologies with traditional reservoir
engineering practices, including type curve analysis, to provide a comprehensive approach to
performance evaluation. Key AI applications discussed include predictive modeling of production
rates, anomaly detection in sensor data, and optimization of resource management strategies. The
results demonstrate that AI-driven approaches offer significant improvements in predictive
accuracy, operational efficiency, and decision-making capabilities. This research highlights the
potential of AI to address the complexities of reservoir management in challenging environments
and offers insights into the future directions for integrating AI technologies in reservoir
performance analysis