Cloud Computing and AI-Driven Type Curve Analysis for Oil Reservoirs in South-Eastern Bangladesh

Abstract

This study explores the application of cloud computing and artificial intelligence (AI) in type curve
analysis for oil reservoirs, specifically focusing on operations in South-Eastern Bangladesh. Type
curve analysis, a crucial technique for evaluating reservoir performance and predicting future
production, is enhanced through the integration of cloud-based platforms and AI algorithms. Cloud
computing facilitates scalable data storage and processing capabilities, enabling the handling of
large datasets typically involved in reservoir analysis. AI-driven methods, particularly machine
learning algorithms, are employed to refine type curve modeling, improve predictive accuracy,
and optimize reservoir management strategies. By leveraging cloud infrastructure, the study
achieves real-time data processing and advanced analytics, leading to more precise type curve
predictions and better-informed decision-making. The results demonstrate significant
improvements in forecast accuracy and operational efficiency, underscoring the potential of
combining cloud computing with AI in the oil and gas sector. This innovative approach not only
advances type curve analysis but also sets a new standard for leveraging technology in reservoir
management.