Application of Artificial Intelligence in Predicting Reservoir Properties for Enhanced Oil Recovery in Petroleum Engineering

Project Abstract

The petroleum industry plays a critical role in meeting global energy demands, making the efficient extraction of oil resources essential. Enhanced oil recovery (EOR) techniques aim to maximize oil production from reservoirs, necessitating accurate prediction of reservoir properties. This research focuses on the application of artificial intelligence (AI) in predicting reservoir properties for enhanced oil recovery in petroleum engineering. The primary objective is to develop a predictive model that harnesses AI algorithms to improve the accuracy and efficiency of reservoir property predictions, ultimately enhancing oil recovery processes. The study commences with an introduction that outlines the significance of applying AI in the petroleum industry, particularly in predicting reservoir properties for EOR. The background of the study delves into existing literature on AI applications in reservoir engineering and highlights the gaps that this research seeks to address. The problem statement identifies the challenges faced in accurately predicting reservoir properties and emphasizes the need for advanced technologies like AI to overcome these obstacles. The research objectives are delineated to guide the study, focusing on developing an AI-based model for predicting key reservoir properties crucial for EOR success. The limitations and scope of the study are also discussed to provide clarity on the extent and constraints of the research. The significance of the study is underscored, emphasizing the potential impact of AI-driven reservoir property predictions on optimizing oil recovery processes in the petroleum industry. The structure of the research elucidates the organization of the study, detailing the chapters and their respective contents. In the subsequent literature review, ten key articles and studies are analyzed to provide a comprehensive overview of AI applications in predicting reservoir properties for enhanced oil recovery. The review highlights the advancements, challenges, and opportunities in this field, setting the foundation for the research methodology. Chapter three delves into the research methodology, outlining the approach, data collection methods, AI algorithms employed, model development process, and validation techniques. By incorporating a rigorous methodology, the study aims to ensure the reliability and accuracy of the AI model in predicting reservoir properties effectively. Chapter four presents the detailed discussion of findings, analyzing the results generated by the AI model and their implications for enhancing oil recovery processes. The seven key findings are thoroughly examined, providing insights into the effectiveness of AI in predicting reservoir properties and its potential for optimizing EOR strategies. Finally, chapter five constitutes the conclusion and summary of the project research, encapsulating the key findings, contributions, limitations, and future research directions. By harnessing the power of artificial intelligence in predicting reservoir properties for enhanced oil recovery, this study aims to pave the way for more efficient and sustainable oil extraction practices in the petroleum engineering domain.

Project Overview