Thesis Abstract

Abstract
The oil and gas industry has witnessed a significant transformation in recent years with the integration of advanced technologies to optimize operations and increase efficiency. One such technology that has gained prominence is Artificial Intelligence (AI), which offers innovative solutions to enhance reservoir characterization for improved oil recovery. This thesis explores the utilization of AI in reservoir characterization to optimize oil recovery processes, focusing on its application in enhancing productivity in the oil and gas sector. The introduction section provides an overview of the research, highlighting the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The background of the study discusses the importance of reservoir characterization in oil recovery and the potential benefits of integrating AI technologies in this process. The problem statement identifies the gaps in existing reservoir characterization methods and the need for more advanced solutions to improve oil recovery efficiency. The literature review chapter critically evaluates existing research on AI applications in reservoir characterization and oil recovery. It explores ten key areas including AI algorithms, machine learning techniques, data analytics, and modeling approaches used in reservoir characterization. The review also discusses case studies and industry applications to provide a comprehensive understanding of the current state of AI in oil and gas operations. In the research methodology chapter, the study outlines the methods and techniques employed to investigate the utilization of AI in reservoir characterization. The chapter covers data collection, analysis, modeling, and simulation processes, as well as the tools and software used to implement AI algorithms for reservoir characterization. The methodology section also discusses the research framework and criteria for evaluating the effectiveness of AI in enhancing oil recovery processes. The discussion of findings chapter presents a detailed analysis of the results obtained from applying AI technologies in reservoir characterization. It highlights the key findings, trends, and insights gathered from the study, focusing on the impact of AI on reservoir modeling, data interpretation, and decision-making processes in oil recovery operations. The chapter also discusses the challenges, limitations, and implications of utilizing AI in reservoir characterization for enhanced oil recovery. Finally, the conclusion and summary chapter provide a comprehensive overview of the research findings and their implications for the oil and gas industry. The study concludes with recommendations for further research and practical applications of AI in reservoir characterization to optimize oil recovery processes. The summary highlights the key contributions of the research and its potential to drive innovation and efficiency in oil and gas operations through the utilization of AI technologies. Overall, this thesis contributes to the growing body of knowledge on the application of Artificial Intelligence in reservoir characterization for enhanced oil recovery. By leveraging AI algorithms, machine learning techniques, and data analytics, the study demonstrates the potential of advanced technologies to revolutionize oil and gas operations and drive sustainable growth in the industry.

Thesis Overview

The project titled "Utilization of Artificial Intelligence in Reservoir Characterization for Enhanced Oil Recovery" focuses on the integration of artificial intelligence (AI) technologies in the field of petroleum engineering to improve reservoir characterization methods and enhance oil recovery processes. Reservoir characterization plays a critical role in the efficient extraction of hydrocarbons from subsurface reservoirs, and the application of AI offers promising opportunities to optimize this process. The research aims to investigate how AI techniques, such as machine learning algorithms, neural networks, and data analytics, can be leveraged to analyze large volumes of reservoir data, including seismic surveys, well logs, and production data. By utilizing AI, the project seeks to develop advanced models for reservoir characterization that can provide more accurate predictions of reservoir properties, such as porosity, permeability, and fluid saturation. Furthermore, the project aims to explore the use of AI in optimizing production strategies and maximizing oil recovery from reservoirs. By implementing AI-based reservoir management systems, operators can make data-driven decisions in real-time, leading to improved reservoir performance and increased hydrocarbon production. The research overview will delve into the current challenges faced in reservoir characterization and oil recovery processes, highlighting the limitations of traditional methods and the potential benefits of integrating AI technologies. By combining the expertise in petroleum engineering with AI capabilities, this project aims to contribute to the advancement of the oil and gas industry by providing innovative solutions to optimize reservoir development and production operations. Overall, the project "Utilization of Artificial Intelligence in Reservoir Characterization for Enhanced Oil Recovery" seeks to bridge the gap between traditional reservoir engineering practices and emerging AI technologies to unlock new opportunities for improving oil recovery efficiency and maximizing hydrocarbon reserves in subsurface reservoirs.