Thesis Abstract

Abstract
The demand for energy resources, particularly oil, continues to rise globally, necessitating the optimization of existing oil recovery processes. In the petroleum industry, reservoir characterization plays a crucial role in understanding the subsurface reservoirs for efficient oil extraction. This research project focuses on the application of Artificial Intelligence (AI) in reservoir characterization to enhance oil recovery processes. The integration of AI technologies, such as machine learning and neural networks, offers promising solutions to address the complexities and uncertainties associated with reservoir characterization. The study begins with an exploration of the theoretical background and existing literature on reservoir characterization and AI applications in the petroleum industry. The literature review highlights the potential benefits and challenges of integrating AI in reservoir characterization for enhanced oil recovery. The research methodology section outlines the approach taken to collect and analyze data, including the selection of appropriate AI algorithms and techniques for reservoir characterization. The findings from the study provide valuable insights into the effectiveness of AI in improving reservoir characterization accuracy and optimizing oil recovery processes. Through the analysis of real-world reservoir data, the study demonstrates the potential of AI technologies to enhance decision-making in reservoir management and increase oil production rates. The discussion of findings delves into the implications of AI integration in reservoir characterization, including its impact on operational efficiency and cost-effectiveness. In conclusion, this research project emphasizes the significance of AI in reservoir characterization for enhanced oil recovery. By leveraging AI technologies, petroleum engineers and geoscientists can gain a deeper understanding of reservoir properties and behavior, leading to more effective strategies for oil extraction. The study contributes to the growing body of knowledge on AI applications in the petroleum industry and provides practical recommendations for implementing AI-driven reservoir characterization techniques. Overall, this research project underscores the transformative potential of AI in optimizing oil recovery processes and ensuring sustainable energy production in the future.

Thesis Overview

The project titled "Application of Artificial Intelligence in Reservoir Characterization for Enhanced Oil Recovery" aims to explore the integration of artificial intelligence (AI) techniques in reservoir characterization processes to enhance oil recovery in the petroleum industry. Reservoir characterization is a critical aspect of oil and gas exploration and production, as it involves the analysis and description of subsurface reservoir properties to optimize recovery strategies. Traditional methods of reservoir characterization often rely on manual interpretation of seismic data, well logs, and other geological information, which can be time-consuming and subject to human error. By leveraging AI technologies such as machine learning, deep learning, and data analytics, this project seeks to automate and improve the accuracy of reservoir characterization processes. AI algorithms can analyze vast amounts of data quickly and identify complex patterns that may not be apparent to human analysts. This can lead to more precise reservoir models, better prediction of fluid flow behaviors, and ultimately, more efficient oil recovery strategies. The research overview will delve into the current challenges faced in reservoir characterization, highlighting the limitations of traditional methods and the potential benefits of incorporating AI technologies. The project will involve a comprehensive literature review to examine existing studies on AI applications in reservoir characterization and oil recovery. By synthesizing this knowledge, the research aims to identify gaps in the literature and propose a novel approach to integrating AI into reservoir characterization workflows. The methodology section of the project will outline the steps involved in implementing AI techniques for reservoir characterization, including data collection, preprocessing, model training, and validation. The project will also discuss the selection of appropriate AI algorithms based on the specific requirements of reservoir characterization tasks. The findings and discussion section will present the results of applying AI in reservoir characterization, including the accuracy of the models developed, the efficiency gains achieved, and any challenges encountered during the implementation process. The discussion will analyze the implications of the findings for the petroleum industry and explore potential areas for further research and development. In conclusion, this project aims to demonstrate the potential of AI technologies in revolutionizing reservoir characterization practices for enhanced oil recovery. By automating and optimizing this critical aspect of oil and gas production, the project seeks to contribute to the efficiency, sustainability, and profitability of the petroleum industry.