Thesis Abstract

Abstract
The utilization of Artificial Intelligence (AI) technology in the field of petroleum engineering has significantly transformed reservoir characterization and production optimization processes. This thesis explores the application of AI in enhancing the understanding of subsurface reservoir properties and optimizing hydrocarbon production. The research focuses on the development and implementation of AI algorithms and techniques to analyze complex reservoir data and make informed decisions for efficient reservoir management. The introduction provides a comprehensive overview of the background of the study, highlighting the significance of applying AI in petroleum engineering, particularly in reservoir characterization and production optimization. The problem statement identifies the challenges faced in traditional reservoir management methods and emphasizes the need for advanced AI solutions to address these issues effectively. The objectives of the study include investigating various AI technologies, such as machine learning, deep learning, and data analytics, to analyze reservoir data and improve decision-making processes. The limitations and scope of the study are outlined, defining the boundaries and constraints within which the research is conducted. The significance of the study is emphasized, emphasizing the potential impact of AI on enhancing reservoir management practices and maximizing hydrocarbon recovery. The literature review presents a comprehensive analysis of existing research and developments in the field of AI applied to reservoir characterization and production optimization. Key topics covered include AI algorithms for seismic interpretation, reservoir modeling, production forecasting, and well optimization. The review also explores case studies and real-world applications of AI in petroleum engineering, highlighting the benefits and challenges associated with these technologies. The research methodology outlines the approach and techniques used to conduct the study, including data collection, analysis, and implementation of AI models. Various methodologies such as data preprocessing, feature selection, model training, and evaluation are discussed in detail. The chapter also addresses the challenges and considerations in implementing AI solutions in the petroleum industry. The discussion of findings chapter presents the results and outcomes of applying AI in reservoir characterization and production optimization. The analysis includes the performance evaluation of AI models, comparison with traditional methods, and interpretation of key insights derived from the data. The findings provide valuable insights into the effectiveness and efficiency of AI technologies in improving reservoir management practices. In conclusion, the thesis summarizes the key findings and contributions of the study, highlighting the advancements in AI technology for reservoir characterization and production optimization. The implications of the research on the petroleum industry are discussed, emphasizing the potential for AI to revolutionize reservoir management strategies and enhance hydrocarbon recovery rates. Recommendations for future research and practical applications of AI in petroleum engineering are also provided. Overall, this thesis contributes to the growing body of knowledge on the application of Artificial Intelligence in reservoir characterization and production optimization, offering valuable insights and recommendations for industry professionals, researchers, and policymakers in the field of petroleum engineering.

Thesis Overview