Project Abstract

Abstract
Enhanced Oil Recovery (EOR) techniques play a crucial role in maximizing oil production from mature oil fields. In recent years, the integration of Machine Learning (ML) algorithms has gained significant attention in the petroleum industry for optimizing EOR processes. This research project focuses on the application of ML algorithms to enhance the efficiency and effectiveness of EOR techniques in mature oil fields. The primary objective is to develop a comprehensive framework that leverages ML algorithms to optimize the selection and implementation of EOR methods based on reservoir characteristics and production data. The research begins with an in-depth exploration of the background of EOR techniques and the challenges faced in mature oil fields. The problem statement highlights the limitations of traditional approaches and the need for advanced optimization methods to improve oil recovery rates. The study aims to address these challenges by defining clear research objectives that focus on the integration of ML algorithms into EOR decision-making processes. The scope of the research encompasses the application of various ML algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning, to analyze reservoir data, production history, and fluid properties. The significance of this study lies in its potential to revolutionize EOR practices by providing a data-driven approach to optimize oil recovery strategies, reduce costs, and increase production efficiency in mature oil fields. The research methodology involves a systematic review of existing literature on EOR techniques, ML applications in the oil and gas industry, and optimization strategies. Additionally, the study includes data collection from case studies of mature oil fields, simulation modeling, algorithm development, and performance evaluation. The research methodology is designed to provide a comprehensive analysis of the effectiveness of ML algorithms in optimizing EOR techniques. The discussion of findings in Chapter Four presents a detailed analysis of the results obtained from the application of ML algorithms in optimizing EOR processes. This section includes a comparison of different ML approaches, their impact on production performance, and the identification of key factors influencing the success of EOR operations in mature oil fields. The findings highlight the potential of ML algorithms to enhance decision-making processes, improve reservoir management, and increase oil recovery rates. In conclusion, this research project emphasizes the importance of integrating ML algorithms into EOR practices to optimize oil recovery techniques in mature oil fields. The study provides valuable insights into the benefits of using data-driven approaches to enhance production efficiency, reduce operational costs, and maximize oil reserves. The findings of this research contribute to the advancement of EOR technologies and pave the way for future innovations in the petroleum industry.

Project Overview