Thesis Abstract

Abstract
The petroleum industry continues to face challenges in optimizing oil recovery from mature fields, where conventional methods have reached their limits. This study explores the integration of artificial intelligence (AI) in reservoir characterization and modeling to enhance oil recovery in mature fields. The research focuses on leveraging AI techniques such as machine learning, neural networks, and data analytics to improve reservoir understanding, optimize production strategies, and increase oil recovery rates. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter also defines key terms relevant to the study, setting the foundation for subsequent chapters. Chapter 2 presents a comprehensive literature review on AI applications in reservoir characterization and modeling for enhanced oil recovery. The review covers key concepts, methodologies, and case studies related to the integration of AI in the petroleum industry, emphasizing its potential benefits and challenges. Chapter 3 outlines the research methodology employed in this study, including data collection methods, AI techniques utilized, reservoir simulation tools, model validation processes, and sensitivity analyses. The chapter details the step-by-step approach taken to integrate AI into reservoir characterization and modeling for enhanced oil recovery in mature fields. Chapter 4 presents the findings and results of the research, highlighting the impact of AI integration on reservoir performance, production optimization, and oil recovery enhancement. The chapter discusses key insights, trends, and implications derived from the application of AI techniques in mature field reservoirs. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications for the petroleum industry, and suggesting potential areas for future research. The chapter also provides recommendations for industry practitioners and policymakers on leveraging AI technologies for improved reservoir management and enhanced oil recovery in mature fields. Overall, this thesis contributes to the growing body of knowledge on the application of artificial intelligence in reservoir characterization and modeling for enhanced oil recovery. By harnessing the power of AI, this research offers new insights and strategies to optimize oil production, extend the life of mature fields, and maximize economic benefits for the petroleum industry.

Thesis Overview

The project titled "Integration of Artificial Intelligence in Reservoir Characterization and Modeling for Enhanced Oil Recovery in Mature Fields" aims to explore the application of advanced Artificial Intelligence (AI) techniques in the field of petroleum engineering to enhance oil recovery from mature fields. The oil and gas industry faces the challenge of declining production rates in mature oil fields, which necessitates the implementation of innovative technologies to maximize oil recovery. By integrating AI into reservoir characterization and modeling processes, this project seeks to optimize production strategies and improve overall reservoir performance in mature oil fields. The research will begin with a comprehensive literature review to examine existing studies on the use of AI in reservoir engineering, oil recovery techniques, and the challenges associated with mature fields. This review will provide a solid foundation for understanding the current state of the art and identifying gaps in knowledge that can be addressed through the proposed research. The project will then focus on developing a methodology for integrating AI algorithms, such as machine learning and data analytics, into reservoir characterization processes. By leveraging AI technologies, the research aims to improve the accuracy of reservoir models, optimize production strategies, and identify new opportunities for enhanced oil recovery. Through the implementation of AI-based reservoir modeling techniques, the project seeks to enhance the understanding of complex reservoir behaviors, identify untapped reserves, and improve the efficiency of oil recovery operations in mature fields. By utilizing advanced data analytics and predictive modeling, the research aims to provide valuable insights that can help operators make informed decisions to maximize oil production and extend the economic life of mature oil fields. Overall, this project represents a significant step towards leveraging cutting-edge AI technologies to address the challenges faced by the oil and gas industry in mature fields. By integrating AI in reservoir characterization and modeling processes, the research aims to optimize oil recovery operations, increase production rates, and ultimately contribute to the sustainable development of oil and gas resources."