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.

Thesis Overview

The project titled "Optimization of Enhanced Oil Recovery Techniques in Mature Oilfields Using Machine Learning Algorithms" aims to explore the application of machine learning algorithms in enhancing oil recovery from mature oilfields. Mature oilfields are characterized by declining production rates and the challenge of extracting the remaining oil reserves efficiently. Traditional oil recovery methods often fall short in maximizing the extraction of oil from these mature fields. Therefore, the utilization of advanced technologies such as machine learning algorithms presents a promising solution to optimize the recovery process. The research will delve into the background of enhanced oil recovery (EOR) techniques, focusing on the challenges faced in mature oilfields and the potential benefits of applying machine learning algorithms in this context. By leveraging machine learning models, the project seeks to analyze complex reservoir data, production histories, and field characteristics to identify patterns and optimize the decision-making process for enhanced oil recovery operations. The methodology employed in this research will involve data collection from mature oilfields, including reservoir properties, production data, and historical records of EOR operations. Machine learning algorithms such as neural networks, support vector machines, and decision trees will be utilized to develop predictive models that can optimize EOR techniques based on the available data. The findings of this study are expected to provide insights into the effectiveness of machine learning algorithms in optimizing enhanced oil recovery techniques in mature oilfields. By integrating data-driven approaches with traditional reservoir engineering practices, the research aims to improve production efficiency, increase oil recovery rates, and extend the economic lifespan of mature oilfields. Overall, this research project seeks to contribute to the advancement of oil recovery technologies by demonstrating the potential of machine learning algorithms in optimizing EOR operations in mature oilfields. The outcomes of this study have the potential to significantly impact the oil and gas industry by offering innovative solutions to overcome the challenges associated with extracting oil from mature reservoirs."