Thesis Abstract

Abstract
The application of artificial intelligence (AI) in reservoir characterization for improved oil recovery has garnered significant interest in the petroleum engineering field. This thesis explores the utilization of AI techniques to enhance reservoir characterization processes and ultimately optimize oil recovery operations. The study begins by providing a comprehensive introduction to the research topic, highlighting the background of study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter two presents a detailed literature review that examines existing studies on AI applications in reservoir characterization and oil recovery. The review covers various AI techniques such as machine learning, neural networks, and data analytics, emphasizing their effectiveness in enhancing reservoir modeling, fluid flow simulations, and production optimization. Chapter three outlines the research methodology employed in this study, including data collection methods, AI algorithm selection, model development, and evaluation criteria. The methodology section also discusses the integration of AI technologies with traditional reservoir engineering techniques to achieve more accurate and efficient results. In chapter four, the findings of the research are extensively discussed, showcasing the outcomes of applying AI in reservoir characterization for improved oil recovery. The discussion covers the impact of AI on reservoir property estimation, fluid behavior prediction, and well placement optimization, highlighting the benefits of incorporating AI in oil field development projects. Finally, chapter five presents the conclusion and summary of the thesis, emphasizing the key findings, contributions, and implications of the research. The study concludes by highlighting the potential of AI technologies to revolutionize reservoir characterization practices and drive significant improvements in oil recovery efficiency and profitability. In conclusion, this thesis provides valuable insights into the application of artificial intelligence in reservoir characterization for improved oil recovery. The findings underscore the transformative potential of AI technologies in optimizing oil field operations and maximizing hydrocarbon production. This research contributes to the ongoing efforts to leverage cutting-edge technologies for sustainable and efficient oil exploration and production practices.

Thesis Overview

The project titled "Application of Artificial Intelligence in Reservoir Characterization for Improved Oil Recovery" focuses on utilizing artificial intelligence (AI) techniques to enhance the process of reservoir characterization in the field of petroleum engineering. Reservoir characterization plays a crucial role in understanding the subsurface reservoirs, which is essential for optimizing oil recovery processes. By incorporating AI technologies, such as machine learning algorithms and data analytics, into reservoir characterization, this project aims to improve the efficiency and accuracy of oil recovery operations. The research will begin with a comprehensive literature review to explore the existing methodologies and technologies used in reservoir characterization and oil recovery processes. This review will provide a solid foundation for understanding the current challenges and opportunities in the field and will guide the development of the AI-based approach proposed in this project. In the subsequent chapters, the research methodology will be detailed, outlining the data sources, AI algorithms, and techniques to be employed for reservoir characterization. The project will involve the collection and analysis of data from various sources, including well logs, seismic data, and production history, to build predictive models that can help optimize oil recovery strategies. The findings and results of the research will be discussed in detail in Chapter Four, highlighting the effectiveness of the AI-based approach in enhancing reservoir characterization and improving oil recovery outcomes. The discussion will include comparisons with traditional methods, showcasing the advantages and limitations of the proposed AI solution. Finally, the project will conclude with Chapter Five, summarizing the key findings, implications, and recommendations for future research and practical applications. The research overview underscores the significance of leveraging AI in reservoir characterization to achieve improved oil recovery rates, reduce operational costs, and enhance overall productivity in the petroleum industry.