Thesis Abstract

Abstract
The petroleum industry is constantly seeking innovative solutions to enhance reservoir characterization and production optimization processes for improved efficiency and economic viability. This thesis explores the application of Artificial Intelligence (AI) techniques in addressing these challenges within the realm of Petroleum Engineering. The study focuses on leveraging AI algorithms and machine learning models to analyze complex reservoir data, optimize production strategies, and improve decision-making processes. The research commences with an in-depth investigation into the current practices and challenges encountered in reservoir characterization and production optimization in the petroleum industry. By examining the background of the study, the complexities of reservoir management, and the limitations of traditional methods are highlighted. The problem statement underscores the necessity for advanced technologies, such as AI, to overcome these challenges and enhance operational performance. The primary objective of this study is to evaluate the effectiveness of AI applications in reservoir characterization and production optimization. By employing state-of-the-art AI algorithms, the research aims to develop predictive models that can accurately forecast reservoir behavior, optimize production strategies, and minimize operational risks. The scope of the study encompasses the implementation of AI techniques across various stages of reservoir management, from data acquisition and interpretation to real-time decision support systems. Through a comprehensive literature review, the thesis explores existing research and advancements in AI applications within the petroleum industry. Various studies on machine learning, neural networks, and data analytics in reservoir engineering are reviewed to identify best practices and potential areas for improvement. The review also delves into case studies and practical applications of AI in reservoir characterization and production optimization. The research methodology section outlines the approach taken to implement AI techniques in reservoir characterization and production optimization. Data collection methods, model development processes, and validation techniques are discussed in detail. The study utilizes a combination of historical reservoir data, well logs, seismic surveys, and production data to train and validate AI models for predictive analytics. The discussion of findings section presents the results of applying AI algorithms to reservoir characterization and production optimization tasks. Through a series of case studies and simulations, the study demonstrates the effectiveness of AI in improving reservoir management practices, optimizing production efficiency, and reducing operational costs. The findings highlight the potential of AI to revolutionize decision-making processes in the petroleum industry. In conclusion, this thesis provides a comprehensive overview of the application of Artificial Intelligence in reservoir characterization and production optimization in Petroleum Engineering. By leveraging AI technologies, the industry can enhance reservoir management practices, optimize production strategies, and achieve greater operational efficiency. The study underscores the significance of AI in transforming traditional approaches to reservoir engineering and outlines future research directions for continued innovation and advancement in the field.

Thesis Overview