Project Abstract

Abstract
The petroleum industry has been exploring innovative technologies to optimize oil recovery from unconventional reservoirs. One promising approach is the utilization of Artificial Intelligence (AI) to predict reservoir properties for enhanced oil recovery. This research investigates the application of AI in predicting reservoir properties to improve oil recovery in unconventional reservoirs. The study aims to address the challenges faced in conventional reservoir management by leveraging AI algorithms for accurate and efficient predictions. The research begins with a comprehensive review of literature on AI applications in the petroleum industry, focusing on reservoir characterization and prediction techniques. Various AI algorithms, such as machine learning, neural networks, and genetic algorithms, are explored in the context of reservoir property prediction. The literature review also examines case studies and success stories of AI implementation in reservoir engineering. The methodology section outlines the research approach, including data collection, preprocessing, feature selection, and model development. The study utilizes a dataset of reservoir properties from unconventional reservoirs to train and validate AI models. Various AI algorithms are implemented and evaluated based on their predictive performance and computational efficiency. The results and discussion section presents the findings of the study, including the accuracy and reliability of AI models in predicting reservoir properties. The analysis highlights the strengths and limitations of different AI algorithms and their implications for enhanced oil recovery strategies. The discussion also addresses the challenges and opportunities of integrating AI technologies into reservoir engineering practices. In conclusion, the research emphasizes the significance of AI in predicting reservoir properties for enhanced oil recovery in unconventional reservoirs. The study demonstrates the potential of AI algorithms to optimize reservoir management and increase oil production rates in challenging geological settings. The findings contribute to the growing body of knowledge on AI applications in the petroleum industry and provide valuable insights for future research and practical implementations. Overall, this research sheds light on the promising role of Artificial Intelligence in revolutionizing reservoir engineering practices and unlocking the full potential of unconventional reservoirs for enhanced oil recovery. The study underscores the importance of embracing AI technologies to improve decision-making processes and achieve sustainable energy production in the evolving landscape of the petroleum industry.

Project Overview