Application of Artificial Intelligence in Reservoir Characterization and Production Optimization in Petroleum Engineering

Thesis Abstract

Abstract
The utilization of Artificial Intelligence (AI) in the field of petroleum engineering has gained significant attention in recent years due to its potential to revolutionize reservoir characterization and production optimization processes. This thesis investigates the application of AI techniques in improving the understanding of reservoir properties and optimizing production strategies in the petroleum industry. The study focuses on the development and implementation of AI algorithms to analyze complex reservoir data, predict reservoir behavior, and optimize production operations. The first part of the research involves a comprehensive review of existing literature on the application of AI in petroleum engineering, highlighting the current trends, challenges, and opportunities in the field. The literature review covers various AI techniques such as machine learning, deep learning, and neural networks, and their potential applications in reservoir characterization and production optimization. The research methodology section outlines the approach taken to develop and implement AI models for reservoir characterization and production optimization. This includes data collection, preprocessing, feature selection, model training, validation, and performance evaluation. The study employs real-world reservoir data to validate the effectiveness of the AI models in predicting reservoir properties and optimizing production strategies. The findings of the study reveal that AI techniques can significantly enhance reservoir characterization by providing more accurate and reliable predictions of reservoir properties such as porosity, permeability, and fluid saturation. The AI models also demonstrate the capability to optimize production operations by identifying the most efficient well placement, completion design, and production strategy for maximizing hydrocarbon recovery. The discussion section provides a detailed analysis of the results obtained from the AI models and discusses their implications for the petroleum industry. The findings highlight the potential benefits of integrating AI technologies into reservoir characterization and production optimization workflows, including improved reservoir management, enhanced production efficiency, and increased profitability. In conclusion, this thesis demonstrates the effectiveness of AI in reservoir characterization and production optimization in petroleum engineering. The study contributes to the growing body of knowledge on the application of AI in the oil and gas industry and provides valuable insights for practitioners and researchers looking to leverage AI technologies for improving reservoir management and production operations. The results of this research have significant implications for the future of reservoir engineering and underscore the transformative potential of AI in the petroleum industry.