Thesis Abstract

Abstract
The oil and gas industry continually seeks innovative approaches to enhance oil recovery from mature fields, where conventional methods have reached their limits. One promising solution is the application of Artificial Intelligence (AI) techniques in reservoir characterization and production optimization. This thesis explores the potential of AI to revolutionize the management of mature oil fields by leveraging advanced algorithms and data analytics to improve reservoir understanding and optimize production strategies. Chapter One provides an introduction to the research topic, presenting the background of the study by highlighting the challenges faced in mature fields, stating the problem statement, objectives, limitations, scope, significance of the study, and defining key terms. Chapter Two conducts a comprehensive literature review spanning ten key areas related to AI applications in reservoir characterization and production optimization, examining existing studies, methodologies, and technologies employed in the field. Chapter Three outlines the research methodology employed in this study, detailing eight key components including data collection methods, AI algorithm selection, model development, validation techniques, and performance evaluation metrics. The chapter also discusses the implementation of AI tools and software for reservoir characterization and production optimization. In Chapter Four, the findings of the research are extensively discussed, analyzing the outcomes of applying AI techniques in reservoir characterization and production optimization for enhanced oil recovery in mature fields. The chapter presents the results of reservoir modeling, production forecasting, and optimization strategies, showcasing the effectiveness of AI in improving reservoir performance and maximizing oil recovery rates. Finally, Chapter Five presents the conclusion and summary of the thesis, highlighting the key findings, implications, and contributions of the research. The study demonstrates the significant potential of AI in transforming the oil and gas industry by enhancing reservoir characterization, optimizing production strategies, and ultimately increasing oil recovery from mature fields. Recommendations for future research directions and practical applications of AI in the industry are also provided. In conclusion, this thesis offers valuable insights into the application of Artificial Intelligence in reservoir characterization and production optimization for enhanced oil recovery in mature fields. By harnessing the power of AI technologies, the oil and gas industry can overcome challenges associated with mature fields and improve operational efficiency, leading to sustainable and cost-effective oil production practices.

Thesis Overview

The project titled "Application of Artificial Intelligence in Reservoir Characterization and Production Optimization for Enhanced Oil Recovery in Mature Fields" aims to explore the utilization of artificial intelligence (AI) technologies in the petroleum engineering domain. Specifically focusing on reservoir characterization and production optimization in mature oil fields, this research seeks to address the challenges faced in the oil and gas industry, particularly in maximizing oil recovery efficiency. In mature oil fields, where conventional extraction methods have been employed for an extended period, there is often a decline in production rates and an increase in operational costs. To counter these challenges, the integration of AI technologies offers a promising solution by enabling more accurate reservoir characterization and optimized production strategies. The research will delve into the theoretical foundations of AI, machine learning, and data analytics, highlighting their applications in reservoir engineering and oil recovery processes. By leveraging AI algorithms to analyze large datasets obtained from reservoir simulations, well logs, and production history, it becomes possible to create predictive models that can enhance reservoir characterization accuracy and identify opportunities for improved production efficiency. Furthermore, the project will investigate the implementation of AI-driven production optimization strategies, such as intelligent well placement, real-time monitoring, and adaptive control systems. These technologies can aid in dynamically adjusting production parameters to maximize oil recovery while minimizing operational costs and environmental impact. Through a comprehensive review of existing literature, case studies, and industry best practices, this research aims to provide insights into the potential benefits, challenges, and future directions of applying AI in mature oil fields. By offering a detailed research overview, this project seeks to contribute to the advancement of AI-driven solutions in the petroleum engineering sector, ultimately fostering sustainable and efficient oil recovery practices in mature fields.