Thesis Abstract

Abstract
The petroleum industry constantly faces challenges in reservoir characterization and optimization to maximize production and recovery. This study explores the potential of Artificial Intelligence (AI) techniques to enhance reservoir management practices in petroleum engineering. The application of AI in reservoir characterization and optimization has gained significant attention in recent years due to its ability to analyze vast amounts of data, predict reservoir behavior, and optimize production strategies effectively. This research aims to investigate the effectiveness of AI algorithms in improving reservoir characterization and optimization processes in petroleum engineering. Chapter One provides an introduction to the research topic, presenting the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and the definition of terms. Chapter Two comprises a comprehensive literature review that examines existing studies on AI applications in reservoir characterization and optimization. This chapter discusses key concepts, methodologies, and findings from previous research to establish a foundation for the current study. Chapter Three outlines the research methodology employed in this study, including data collection methods, AI algorithms utilized, model development, simulation techniques, and validation procedures. The chapter also discusses the selection criteria for reservoir case studies and the implementation of AI techniques in reservoir characterization and optimization. Chapter Four presents a detailed discussion of the findings obtained from applying AI algorithms in reservoir characterization and optimization processes. The chapter analyzes the results, compares different AI models, evaluates their performance, and discusses the implications for reservoir management practices in petroleum engineering. Chapter Five serves as the conclusion and summary of the research thesis, highlighting the key findings, contributions to the field, limitations, recommendations for future research, and the overall significance of applying AI in reservoir characterization and optimization in petroleum engineering. The study concludes by emphasizing the importance of integrating AI technologies into reservoir management practices to enhance decision-making, increase production efficiency, and optimize reservoir performance. In conclusion, this research contributes to the growing body of knowledge on the application of AI in reservoir characterization and optimization in petroleum engineering. By leveraging AI algorithms, petroleum engineers can improve reservoir management practices, optimize production strategies, and enhance reservoir performance, ultimately leading to increased productivity and economic benefits for the petroleum industry.

Thesis Overview

The project titled "Application of Artificial Intelligence in Reservoir Characterization and Optimization in Petroleum Engineering" aims to explore the utilization of artificial intelligence (AI) techniques in enhancing reservoir characterization and optimization processes within the field of petroleum engineering. This research seeks to investigate how advanced AI algorithms and technologies can be applied to effectively analyze and interpret complex reservoir data, leading to improved decision-making and optimized production strategies in the petroleum industry. Reservoir characterization plays a crucial role in understanding the properties and behavior of subsurface reservoirs, which is essential for successful hydrocarbon exploration and production. Traditional methods of reservoir characterization involve manual interpretation of seismic, well log, and production data, which can be time-consuming, labor-intensive, and prone to human errors. By integrating AI technologies such as machine learning, neural networks, and data analytics, this study aims to automate and enhance the reservoir characterization process, allowing for faster and more accurate identification of reservoir properties and fluid behavior. Furthermore, the project will focus on the application of AI in reservoir optimization, which involves determining the most efficient and cost-effective strategies for extracting hydrocarbons from reservoirs while maximizing production rates and minimizing operational risks. By developing AI-based models that can analyze real-time production data, predict reservoir performance, and optimize production schedules, this research aims to help petroleum engineers make informed decisions that lead to improved reservoir performance and enhanced production efficiency. Overall, this research overview highlights the significance of integrating AI technologies into reservoir characterization and optimization processes in petroleum engineering. By harnessing the power of AI to analyze vast amounts of reservoir data, this project seeks to revolutionize the way reservoir engineers approach reservoir management, leading to more sustainable and efficient hydrocarbon production practices in the petroleum industry.