Application of Artificial Intelligence in Reservoir Characterization and Management

Thesis Abstract

Abstract
The petroleum industry has witnessed significant advancements in technology over the years, with a growing emphasis on leveraging artificial intelligence (AI) for improved reservoir characterization and management. This thesis explores the application of AI techniques in reservoir engineering to enhance the understanding of subsurface reservoirs and optimize production strategies. The research focuses on the development and implementation of AI-based models and algorithms to analyze complex reservoir data and provide valuable insights for decision-making processes. Chapter One provides an introduction to the study, highlighting the background of reservoir characterization, the problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definitions of key terms. The chapter sets the foundation for understanding the importance of applying AI in reservoir engineering and management. Chapter Two presents a comprehensive literature review that examines existing studies, methodologies, and technologies related to AI applications in reservoir characterization and management. The review covers topics such as machine learning algorithms, neural networks, data analytics, and their relevance in reservoir engineering practices. Chapter Three discusses the research methodology employed in this study, outlining the data collection process, AI models utilized, software tools, simulation techniques, and evaluation criteria. The chapter provides insights into the methodology adopted to analyze reservoir data and optimize production strategies using AI techniques. Chapter Four presents a detailed discussion of the findings derived from the application of AI in reservoir characterization and management. The chapter includes case studies, model simulations, data analysis results, and performance evaluations to demonstrate the effectiveness of AI in enhancing reservoir understanding and optimizing production efficiency. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and highlighting the potential future directions in the field of AI-enabled reservoir engineering. The chapter emphasizes the significance of AI in transforming traditional reservoir management practices and outlines recommendations for further research and industry applications. In conclusion, this thesis contributes to the growing body of knowledge on the application of artificial intelligence in reservoir characterization and management. The research demonstrates the potential of AI technologies to revolutionize the petroleum industry by enabling more accurate reservoir modeling, predictive analytics, and enhanced decision-making processes. The findings of this study have implications for reservoir engineers, geoscientists, and industry stakeholders seeking innovative solutions to optimize hydrocarbon production and maximize reservoir performance in a dynamic and challenging environment.