Thesis Abstract

Abstract
The petroleum industry has witnessed significant advancements in technology, with a growing emphasis on the integration of artificial intelligence (AI) to enhance reservoir characterization and production optimization processes. This thesis explores the application of AI techniques in addressing the challenges faced in petroleum engineering, specifically focusing on reservoir characterization and production optimization. The primary objective of this research is to investigate how AI algorithms can be leveraged to improve the efficiency, accuracy, and cost-effectiveness of reservoir characterization and production optimization in the petroleum industry. The study begins with a comprehensive review of the background of reservoir characterization and production optimization in petroleum engineering, highlighting the existing challenges and limitations associated with conventional methods. The literature review delves into various AI techniques such as machine learning, neural networks, and data analytics, emphasizing their potential applications in reservoir characterization and production optimization. The research methodology section outlines the approach taken to collect, analyze, and interpret data related to the application of AI in petroleum engineering. The methodology includes data collection through case studies, simulations, and field experiments, as well as the implementation of AI algorithms for data processing and analysis. The findings of the study reveal the significant impact of AI in improving reservoir characterization accuracy, identifying optimal production strategies, and optimizing field development plans. The discussion section elaborates on the key findings, highlighting the benefits of AI integration in petroleum engineering processes and addressing potential challenges and limitations. In conclusion, this thesis underscores the importance of integrating AI technologies in reservoir characterization and production optimization to enhance decision-making, increase operational efficiency, and maximize resource recovery in the petroleum industry. The study contributes to the existing body of knowledge by demonstrating the practical applications of AI in addressing complex challenges in petroleum engineering, paving the way for future research and innovation in the field.

Thesis Overview