Assessment of Reservoir Performance Using Type Curve Analysis in South-Eastern Bangladesh: A Cloud-Based AI Approach

Abstract

The assessment of reservoir performance is crucial for the effective management and
optimization of hydrocarbon production. This study explores the application of type curve
analysis, integrated with cloud-based artificial intelligence (AI) technologies, to evaluate reservoir
performance in the south-eastern region of Bangladesh. Traditional methods of reservoir analysis
often involve extensive manual data interpretation, which can be time-consuming and prone to
human error. By leveraging cloud computing and AI, this research aims to automate the type curve
matching process, enhance the accuracy of reservoir characterization, and provide real-time
insights into reservoir behavior. The implementation of AI algorithms allows for the efficient
processing of large datasets, facilitating the identification of patterns and trends that are critical for
making informed decisions. The findings demonstrate that the combination of type curve analysis
with cloud-based AI tools significantly improves the reliability and efficiency of reservoir
performance assessments, offering a robust framework for future applications in reservoir
management.