Application of Artificial Intelligence in Reservoir Characterization and Production Optimization

Thesis Abstract

Abstract
The petroleum industry has been increasingly adopting technological advancements to enhance efficiency and productivity. In this context, the application of Artificial Intelligence (AI) has gained significant attention for its potential to revolutionize reservoir characterization and production optimization processes. This thesis explores the utilization of AI techniques in the petroleum engineering domain, specifically focusing on reservoir characterization and production optimization. The introduction section provides a comprehensive overview of the research topic, highlighting the significance of incorporating AI in petroleum engineering practices. The background of the study delves into the evolution of AI technology and its relevance to reservoir management. The problem statement identifies existing challenges in traditional reservoir characterization and production optimization techniques, underscoring the need for AI-based solutions. The objectives of the study aim to investigate the effectiveness of AI in improving reservoir characterization accuracy and optimizing production strategies. The limitations of the study acknowledge potential constraints and constraints that may impact the research outcomes. The scope of the study delineates the specific boundaries and focus areas within reservoir characterization and production optimization that will be explored. The significance of the study lies in its potential to contribute to the advancement of petroleum engineering practices by leveraging AI capabilities. The structure of the thesis outlines the organization of the research work, providing a roadmap for the reader to navigate through the various chapters seamlessly. Additionally, the definition of terms clarifies key concepts and terminology used throughout the thesis. The literature review chapter critically examines existing studies and research findings related to AI applications in reservoir characterization and production optimization. Ten key areas of focus are identified, providing a comprehensive understanding of the current state of research in the field. The research methodology chapter elucidates the approach and methods employed in conducting the study. Eight distinct components, including data collection, AI algorithm selection, and model validation, are detailed to ensure the rigor and validity of the research outcomes. The discussion of findings chapter presents an in-depth analysis of the results obtained from the application of AI in reservoir characterization and production optimization. Various case studies and scenarios are explored to showcase the practical implications of using AI technologies in real-world petroleum engineering applications. Finally, the conclusion and summary chapter encapsulate the key findings, implications, and contributions of the research study. The conclusions drawn from the study provide insights into the effectiveness of AI in enhancing reservoir characterization accuracy and optimizing production strategies. In conclusion, this thesis contributes to the growing body of knowledge on the application of AI in petroleum engineering, particularly in the domains of reservoir characterization and production optimization. The research findings highlight the transformative potential of AI technologies in revolutionizing traditional practices and improving operational efficiencies in the petroleum industry.