Thesis Abstract

Abstract
This thesis explores the application of artificial intelligence (AI) in reservoir characterization to enhance oil recovery processes in unconventional reservoirs. The oil and gas industry has seen significant advancements in technology over the years, with AI emerging as a powerful tool for optimizing reservoir management strategies. Unconventional reservoirs present unique challenges due to their complex geological characteristics, requiring innovative approaches for efficient oil recovery. This study focuses on leveraging AI techniques to improve reservoir characterization, leading to enhanced production rates and recovery efficiencies. The introduction provides an overview of the motivation behind using AI in reservoir characterization and outlines the objectives of the study. The background of the study discusses the evolution of AI technologies in the oil and gas industry and highlights the significance of applying AI in unconventional reservoirs. The problem statement identifies the challenges faced in conventional reservoir characterization methods and the need for AI-driven solutions. The objectives of the study aim to investigate the effectiveness of AI in characterizing unconventional reservoirs and optimizing oil recovery processes. Limitations of the study are acknowledged, including data availability, computational resources, and the complex nature of unconventional reservoirs. The scope of the study defines the boundaries within which the research will be conducted, focusing on specific AI techniques and reservoir types. The significance of the study emphasizes the potential impact of AI-driven reservoir characterization on improving oil recovery rates, reducing operational costs, and maximizing resource utilization. The structure of the thesis outlines the organization of chapters and the flow of content. Chapter Two presents a comprehensive literature review covering ten key aspects related to AI applications in reservoir characterization and oil recovery. Relevant studies, methodologies, and technologies are analyzed to provide a solid foundation for the research. Chapter Three details the research methodology, including data collection, AI algorithms selection, model development, and validation procedures. The chapter also discusses the selection criteria for the case studies and the evaluation metrics used to assess the performance of AI models. Chapter Four presents an in-depth discussion of the findings derived from applying AI techniques to reservoir characterization in unconventional reservoirs. The results of the case studies are analyzed, highlighting the impact of AI on improving reservoir understanding, identifying optimal drilling locations, and enhancing production forecasting accuracy. The implications of the findings on oil recovery strategies and operational decision-making are discussed. Finally, Chapter Five presents the conclusions drawn from the study and summarizes the key findings, contributions, and limitations. Recommendations for future research directions are provided, focusing on further advancements in AI applications for reservoir characterization and oil recovery optimization in unconventional reservoirs. Overall, this thesis contributes to the growing body of knowledge on the integration of AI in the oil and gas industry to address the challenges of reservoir management and enhance hydrocarbon production efficiency in unconventional reservoirs.

Thesis Overview

The project titled "Application of Artificial Intelligence in Reservoir Characterization for Improved Oil Recovery in Unconventional Reservoirs" aims to explore the potential of utilizing artificial intelligence (AI) techniques to enhance reservoir characterization processes and improve oil recovery in unconventional reservoirs. Unconventional reservoirs, such as shale formations, present unique challenges due to their complex geology and low recovery rates compared to conventional reservoirs. By integrating AI technologies into the reservoir characterization workflow, this research seeks to address these challenges and optimize the production from unconventional reservoirs. The research will begin with a comprehensive review of existing literature related to reservoir characterization, artificial intelligence applications in the oil and gas industry, and the specific challenges associated with unconventional reservoirs. This literature review will provide a solid foundation for understanding the current state of the art and identifying gaps that can be addressed through the proposed research. The methodology chapter will outline the approach taken to implement AI techniques in reservoir characterization, including data collection, preprocessing, feature selection, and model development. Various AI algorithms, such as machine learning, neural networks, and deep learning, will be explored to analyze and interpret complex reservoir data for improved decision-making in oil recovery processes. The research findings chapter will present the results of applying AI in reservoir characterization, including the performance of different AI models in predicting reservoir properties, identifying optimal well locations, and optimizing production strategies. The discussion will focus on the effectiveness of AI techniques in improving oil recovery rates and reducing uncertainties in reservoir management decisions. Finally, the conclusion chapter will summarize the key findings of the research and provide insights into the practical implications of integrating AI into reservoir characterization for improved oil recovery in unconventional reservoirs. The research will contribute to the growing body of knowledge on the application of AI in the oil and gas industry and offer valuable recommendations for industry practitioners and researchers seeking to enhance reservoir management practices in unconventional reservoirs.