Thesis Abstract

Abstract
The global demand for energy continues to rise, leading to increased focus on the efficient recovery of hydrocarbons from mature oilfields. Enhanced Oil Recovery (EOR) techniques play a crucial role in maximizing oil production from these reservoirs. This thesis investigates the optimization of EOR techniques in mature oilfields through the application of Artificial Intelligence (AI) technologies. The integration of AI algorithms and models offers the potential to enhance decision-making processes and improve the overall efficiency of EOR operations. Chapter One provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two presents a comprehensive literature review on EOR techniques, AI applications in the petroleum industry, and previous studies related to the optimization of EOR processes using AI. Chapter Three outlines the research methodology, including data collection methods, AI algorithms utilized, simulation techniques, and performance evaluation criteria. The chapter details how AI models are trained and validated using historical field data to optimize EOR strategies in mature oilfields. Furthermore, the chapter discusses the implementation of AI technologies in reservoir characterization, well placement optimization, and production forecasting. Chapter Four presents a detailed discussion of the findings obtained from the application of AI-based optimization techniques in mature oilfields. The chapter analyzes the performance improvements achieved through the integration of AI in EOR processes, highlighting the impact on oil recovery efficiency, production rates, and economic benefits. Case studies and simulation results are presented to demonstrate the effectiveness of AI-driven optimization strategies in real-world oilfield applications. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes, and providing recommendations for future studies. The conclusion highlights the potential of AI technologies to revolutionize the oil and gas industry by enhancing the efficiency and sustainability of EOR operations in mature oilfields. Overall, this thesis contributes to the advancement of EOR practices through the innovative application of Artificial Intelligence, paving the way for more effective and sustainable hydrocarbon recovery strategies in the petroleum sector.

Thesis Overview

The project titled "Optimization of Enhanced Oil Recovery Techniques in Mature Oilfields using Artificial Intelligence" aims to address the challenges faced in mature oilfields by implementing advanced artificial intelligence techniques to enhance oil recovery processes. Mature oilfields typically have lower production rates and higher operating costs due to the depletion of reservoir pressure and the presence of bypassed oil. This research project seeks to optimize the recovery of remaining oil reserves in mature fields by leveraging artificial intelligence technologies to improve reservoir characterization, production optimization, and decision-making processes. The research will focus on the application of artificial intelligence, including machine learning algorithms, neural networks, and data analytics, to analyze and interpret complex reservoir data. By integrating these technologies into existing reservoir management practices, the project aims to identify new opportunities for enhanced oil recovery and increase overall production efficiency in mature oilfields. The research overview will include a comprehensive review of existing literature on enhanced oil recovery techniques, artificial intelligence applications in the oil and gas industry, and case studies of successful implementations in mature oilfields. The project will also detail the methodology to be employed, including data collection, analysis techniques, model development, and simulation studies. Furthermore, the research will present a discussion of the anticipated findings and potential impacts of the proposed optimization strategies on oil recovery rates, production costs, and environmental sustainability. The project will conclude with a summary of key insights, recommendations for future research, and the significance of utilizing artificial intelligence in optimizing enhanced oil recovery techniques in mature oilfields.