Optimization of Enhanced Oil Recovery Techniques in Mature Oilfields Using Machine Learning Algorithms

Thesis Abstract

Abstract
The oil and gas industry continuously seeks innovative solutions to maximize production efficiency and recoverable reserves from mature oilfields. Enhanced Oil Recovery (EOR) techniques play a crucial role in achieving these objectives by improving the displacement of oil from reservoirs. This thesis focuses on the optimization of EOR techniques in mature oilfields through the application of Machine Learning (ML) algorithms. Machine Learning has gained significant attention in various industries for its ability to analyze complex data patterns and make data-driven decisions. In the context of EOR in mature oilfields, ML algorithms can be utilized to optimize injection strategies, predict reservoir behavior, and enhance production performance. Chapter 1 provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to EOR techniques, mature oilfields, and the application of Machine Learning algorithms in the oil and gas industry. The literature review sets the foundation for understanding the current state of research and identifies gaps that this thesis aims to address. Chapter 3 outlines the research methodology employed in this study, including data collection methods, ML algorithm selection criteria, model development processes, validation techniques, and performance evaluation metrics. The methodology section provides a detailed explanation of how Machine Learning algorithms are integrated into the optimization of EOR processes in mature oilfields. Chapter 4 presents an elaborate discussion of the findings obtained from the application of ML algorithms in optimizing EOR techniques. The discussion covers the insights gained from data analysis, the effectiveness of ML models in predicting reservoir behavior, and the impact of optimized injection strategies on production performance. This chapter also explores the challenges and limitations encountered during the research process and proposes potential solutions for future research. Finally, Chapter 5 offers a comprehensive conclusion and summary of the thesis, highlighting the key findings, contributions to the field of petroleum engineering, and recommendations for further research. The conclusion emphasizes the significance of utilizing Machine Learning algorithms in optimizing EOR techniques in mature oilfields and underscores the potential for future advancements in this area. In conclusion, this thesis contributes to the ongoing efforts to enhance oil recovery from mature oilfields by leveraging the capabilities of Machine Learning algorithms. By optimizing EOR techniques through data-driven decision-making, this research aims to improve production efficiency, increase oil reserves recovery, and support sustainable practices in the oil and gas industry.