Application of Artificial Intelligence in Reservoir Characterization for Improved Oil Recovery in Unconventional Reservoirs

Thesis Abstract

Abstract
This thesis explores the application of artificial intelligence (AI) in reservoir characterization to enhance oil recovery processes in unconventional reservoirs. The oil and gas industry has seen significant advancements in technology over the years, with AI emerging as a powerful tool for optimizing reservoir management strategies. Unconventional reservoirs present unique challenges due to their complex geological characteristics, requiring innovative approaches for efficient oil recovery. This study focuses on leveraging AI techniques to improve reservoir characterization, leading to enhanced production rates and recovery efficiencies. The introduction provides an overview of the motivation behind using AI in reservoir characterization and outlines the objectives of the study. The background of the study discusses the evolution of AI technologies in the oil and gas industry and highlights the significance of applying AI in unconventional reservoirs. The problem statement identifies the challenges faced in conventional reservoir characterization methods and the need for AI-driven solutions. The objectives of the study aim to investigate the effectiveness of AI in characterizing unconventional reservoirs and optimizing oil recovery processes. Limitations of the study are acknowledged, including data availability, computational resources, and the complex nature of unconventional reservoirs. The scope of the study defines the boundaries within which the research will be conducted, focusing on specific AI techniques and reservoir types. The significance of the study emphasizes the potential impact of AI-driven reservoir characterization on improving oil recovery rates, reducing operational costs, and maximizing resource utilization. The structure of the thesis outlines the organization of chapters and the flow of content. Chapter Two presents a comprehensive literature review covering ten key aspects related to AI applications in reservoir characterization and oil recovery. Relevant studies, methodologies, and technologies are analyzed to provide a solid foundation for the research. Chapter Three details the research methodology, including data collection, AI algorithms selection, model development, and validation procedures. The chapter also discusses the selection criteria for the case studies and the evaluation metrics used to assess the performance of AI models. Chapter Four presents an in-depth discussion of the findings derived from applying AI techniques to reservoir characterization in unconventional reservoirs. The results of the case studies are analyzed, highlighting the impact of AI on improving reservoir understanding, identifying optimal drilling locations, and enhancing production forecasting accuracy. The implications of the findings on oil recovery strategies and operational decision-making are discussed. Finally, Chapter Five presents the conclusions drawn from the study and summarizes the key findings, contributions, and limitations. Recommendations for future research directions are provided, focusing on further advancements in AI applications for reservoir characterization and oil recovery optimization in unconventional reservoirs. Overall, this thesis contributes to the growing body of knowledge on the integration of AI in the oil and gas industry to address the challenges of reservoir management and enhance hydrocarbon production efficiency in unconventional reservoirs.