Thesis Abstract

Abstract
The oil and gas industry plays a vital role in meeting the global energy demand, making enhanced oil recovery (EOR) techniques essential for maximizing hydrocarbon extraction from reservoirs. One promising approach is the application of artificial intelligence (AI) in reservoir characterization to optimize EOR processes. This thesis explores the potential of AI technologies, such as machine learning and neural networks, in improving reservoir characterization for enhanced oil recovery. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review in Chapter Two examines existing research on AI applications in reservoir characterization and EOR, highlighting key findings and gaps in the current knowledge. Chapter Three details the research methodology, encompassing data collection methods, AI algorithms used, model training procedures, validation techniques, and simulation scenarios. The chapter also discusses the selection criteria for input data, model parameters, and performance evaluation metrics to ensure the accuracy and reliability of the results. In Chapter Four, the findings of the AI-based reservoir characterization approach are presented and discussed in detail. The results showcase the effectiveness of AI algorithms in predicting reservoir properties, identifying potential EOR opportunities, and optimizing production strategies. The discussion also addresses the implications of these findings for the oil and gas industry, emphasizing the economic and environmental benefits of AI-driven reservoir management. Finally, Chapter Five offers a comprehensive conclusion and summary of the thesis, highlighting the key contributions, implications, and future research directions. The conclusion underscores the significance of incorporating AI technologies in reservoir characterization for enhanced oil recovery, emphasizing the potential for increased efficiency, productivity, and sustainability in the oil and gas sector. In conclusion, this thesis demonstrates the transformative potential of artificial intelligence in reservoir characterization for enhanced oil recovery. By leveraging AI technologies to analyze complex reservoir data and optimize EOR strategies, the oil and gas industry can achieve greater efficiency, profitability, and environmental sustainability in hydrocarbon extraction processes.

Thesis Overview

The project titled "Application of Artificial Intelligence in Reservoir Characterization for Enhanced Oil Recovery" aims to investigate the potential benefits and challenges associated with integrating artificial intelligence (AI) technologies in reservoir characterization techniques to enhance oil recovery processes. This research overview provides insights into the significance of the project, the research objectives, the methodology that will be employed, and the expected outcomes. Reservoir characterization plays a crucial role in the oil and gas industry as it involves the analysis and interpretation of subsurface data to understand the properties and behavior of reservoir rocks and fluids. Traditional reservoir characterization methods rely on manual interpretation of seismic, well log, and production data, which can be time-consuming and subject to human error. In recent years, the advancement of AI technologies has provided new opportunities to automate and improve the accuracy of reservoir characterization processes. The primary objective of this research is to evaluate the effectiveness of AI algorithms in reservoir characterization for optimizing oil recovery strategies. By leveraging machine learning, deep learning, and other AI techniques, the project seeks to develop predictive models that can analyze complex reservoir data more efficiently and accurately than traditional methods. The research will focus on identifying key reservoir parameters, such as porosity, permeability, and fluid saturation, that influence the success of enhanced oil recovery techniques. The methodology for this research will involve collecting and analyzing real-world reservoir data from existing oil fields. Various AI algorithms will be trained and tested using this data to predict reservoir properties and optimize production strategies. The research will also involve comparative analysis between AI-driven reservoir characterization models and traditional methods to assess the performance improvements and limitations of AI technologies in this context. The expected outcomes of this research include the development of AI-based reservoir characterization models that can enhance the accuracy and efficiency of oil recovery processes. By automating data interpretation and improving predictive capabilities, AI technologies have the potential to revolutionize reservoir engineering practices and drive significant improvements in oil production efficiency and sustainability. The findings of this research will contribute to the growing body of knowledge on the application of AI in the oil and gas industry and provide valuable insights for future research and industry applications. In conclusion, the project "Application of Artificial Intelligence in Reservoir Characterization for Enhanced Oil Recovery" aims to explore the transformative potential of AI technologies in optimizing reservoir characterization processes for improved oil recovery outcomes. Through rigorous research and analysis, this project seeks to advance the understanding of how AI can be effectively integrated into reservoir engineering practices and pave the way for more efficient and sustainable oil production methods.