Thesis Abstract

Abstract
The petroleum industry plays a vital role in the global economy, and efficient reservoir characterization and production optimization are crucial for maximizing oil and gas recovery. With advancements in technology, the application of artificial intelligence (AI) has emerged as a promising tool in addressing challenges in the petroleum engineering field. This thesis explores the application of AI in reservoir characterization and production optimization to enhance decision-making processes and improve overall efficiency in the petroleum industry. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review in Chapter 2 examines existing research on AI applications in petroleum engineering, highlighting key findings and gaps in the current literature. Chapter 3 focuses on the research methodology, detailing the research design, data collection methods, AI algorithms utilized, and evaluation criteria. Chapter 4 presents a comprehensive discussion of the research findings, analyzing how AI techniques have been applied to reservoir characterization and production optimization. The chapter explores the benefits and challenges associated with AI implementation in the petroleum industry, providing insights into best practices and areas for future research. Finally, Chapter 5 offers a conclusion and summary of the thesis, summarizing the key findings, implications, and recommendations for future research and industry practice. Overall, this thesis contributes to the growing body of knowledge on the application of AI in petroleum engineering, demonstrating the potential of AI technologies to revolutionize reservoir characterization and production optimization processes. By leveraging AI tools and techniques, petroleum engineers can make data-driven decisions, improve reservoir management strategies, and enhance overall productivity in the oil and gas sector.

Thesis Overview

The project titled "Application of Artificial Intelligence in Reservoir Characterization and Production Optimization in Petroleum Engineering" focuses on the integration of artificial intelligence (AI) techniques in the field of petroleum engineering to enhance reservoir characterization and optimize production processes. In recent years, the oil and gas industry has witnessed significant advancements in AI technologies, which have the potential to revolutionize traditional approaches to reservoir management and production optimization. Reservoir characterization plays a crucial role in understanding the geological properties of oil and gas reservoirs, such as porosity, permeability, and fluid saturation. By leveraging AI algorithms and machine learning models, petroleum engineers can analyze large volumes of data obtained from well logs, seismic surveys, and production history to create high-resolution reservoir models. These models provide valuable insights into reservoir heterogeneity, connectivity, and fluid flow dynamics, enabling engineers to make informed decisions regarding well placement, drilling strategies, and production forecasting. Furthermore, AI can be applied to optimize production processes by implementing advanced control strategies, predictive maintenance techniques, and real-time monitoring systems. AI-powered predictive analytics can help identify potential production bottlenecks, optimize well performance, and reduce operational risks. By integrating AI into production optimization workflows, petroleum engineers can enhance reservoir productivity, maximize hydrocarbon recovery, and improve overall operational efficiency. The research will involve a comprehensive review of existing literature on AI applications in reservoir characterization and production optimization, highlighting the latest trends, challenges, and opportunities in the field of petroleum engineering. The study will also include the development and implementation of AI-based algorithms and models to address specific reservoir management and production optimization problems. Overall, the project aims to demonstrate the potential benefits of integrating AI technologies in petroleum engineering practices, with a focus on improving reservoir characterization accuracy, optimizing production processes, and maximizing hydrocarbon recovery. By harnessing the power of AI, petroleum engineers can unlock new insights into reservoir behavior, enhance decision-making processes, and drive innovation in the oil and gas industry.