Application of Artificial Intelligence in Reservoir Characterization and Production Optimization in Petroleum Engineering

Thesis Abstract

Abstract
The petroleum industry is constantly seeking innovative technologies to enhance reservoir characterization and production optimization processes. One such technology that has gained significant attention in recent years is Artificial Intelligence (AI). This thesis explores the application of AI in reservoir characterization and production optimization in petroleum engineering. The study aims to investigate the effectiveness of AI algorithms in improving the accuracy and efficiency of reservoir modeling, as well as optimizing production strategies to maximize recovery rates. Chapter one provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The literature review in chapter two explores ten key studies related to the application of AI in reservoir characterization and production optimization. This section provides a comprehensive overview of the current state-of-the-art technologies and methodologies in the field. Chapter three outlines the research methodology, detailing the approach taken to implement AI algorithms in reservoir characterization and production optimization. This chapter includes discussions on data collection, data preprocessing, AI model selection, training and validation processes, and performance evaluation metrics. The methodology section also discusses the limitations and challenges encountered during the research process. In chapter four, the findings of the study are extensively discussed, highlighting the outcomes of applying AI algorithms in reservoir characterization and production optimization tasks. The results are analyzed in detail, discussing the impact of AI on improving reservoir modeling accuracy, optimizing production strategies, and enhancing decision-making processes in petroleum engineering operations. The chapter also includes comparative analyses with traditional methods to showcase the advantages of AI technologies. Lastly, chapter five presents the conclusion and summary of the thesis, drawing key insights from the research findings and discussing their implications for the petroleum industry. The conclusions highlight the potential of AI in revolutionizing reservoir characterization and production optimization practices, providing recommendations for future research directions and practical applications. Overall, this thesis contributes to the growing body of knowledge on the application of AI in petroleum engineering, offering valuable insights for industry professionals, researchers, and policymakers.