Application of Artificial Intelligence in Reservoir Characterization for Enhanced Oil Recovery

Thesis Abstract

Abstract
The oil and gas industry has been striving to enhance oil recovery from reservoirs through the utilization of advanced technologies. In recent years, Artificial Intelligence (AI) has emerged as a powerful tool in reservoir characterization, offering the potential to optimize production and improve recovery rates. This thesis explores the application of AI in reservoir characterization for enhanced oil recovery. The primary objective is to investigate the effectiveness of AI techniques in analyzing reservoir properties, predicting fluid behavior, and identifying optimal strategies for enhanced oil recovery. The research methodology employed a combination of literature review, data collection, AI algorithm development, and simulation studies. The literature review provided a comprehensive understanding of existing AI applications in reservoir characterization and highlighted the potential benefits and challenges. Data collection involved gathering reservoir data sets, including well logs, seismic data, and production history, to train and validate AI models. AI algorithm development focused on designing and implementing machine learning and deep learning models to analyze reservoir properties and predict fluid behavior. Simulation studies were conducted to evaluate the performance of AI models in optimizing production strategies for enhanced oil recovery. The findings of this study demonstrate the effectiveness of AI techniques in reservoir characterization for enhanced oil recovery. Machine learning models, such as support vector machines and random forests, showed promising results in predicting reservoir properties and fluid behavior. Deep learning models, including convolutional neural networks and recurrent neural networks, exhibited superior performance in analyzing complex reservoir data sets. The simulation studies revealed that AI-based optimization strategies can significantly improve recovery rates and enhance production efficiency. The significance of this research lies in its contribution to the advancement of reservoir characterization techniques using AI. By leveraging the power of AI, oil and gas companies can gain valuable insights into reservoir behavior, optimize production strategies, and maximize oil recovery. The implications of this research extend to the broader energy industry, where AI technologies can revolutionize reservoir management practices and drive sustainable energy production. In conclusion, the application of Artificial Intelligence in reservoir characterization for enhanced oil recovery represents a transformative approach to optimizing oil and gas production. This thesis provides valuable insights into the potential of AI techniques to revolutionize reservoir management practices and improve recovery rates. The findings of this study contribute to the growing body of knowledge on AI applications in the oil and gas industry and pave the way for future research and innovation in reservoir characterization and enhanced oil recovery.