Thesis Abstract

Abstract
The utilization of Artificial Intelligence (AI) in the oil and gas industry has gained significant attention in recent years due to its potential to revolutionize traditional practices and enhance operational efficiency. This thesis explores the application of AI in reservoir characterization and production optimization, aiming to leverage advanced technologies to improve decision-making processes and maximize hydrocarbon recovery. The study delves into the integration of AI algorithms and machine learning techniques in reservoir engineering practices, focusing on their ability to analyze complex datasets, predict reservoir behavior, and optimize production strategies. The research begins with a comprehensive literature review that examines existing studies, methodologies, and technologies related to AI in reservoir engineering. Through an in-depth analysis of ten key areas within the literature, the review highlights the current state of research and identifies gaps that warrant further investigation. Subsequently, the research methodology section outlines the approach taken to conduct the study, including data collection methods, AI algorithm selection, and evaluation criteria. The core of the thesis lies in the discussion of findings, where the application of AI in reservoir characterization and production optimization is explored in detail. By applying AI models to real-world reservoir data, the study demonstrates the capabilities of AI in predicting reservoir properties, optimizing well placement, and enhancing production efficiency. The findings provide valuable insights into the potential benefits and challenges associated with implementing AI technologies in reservoir engineering practices. In conclusion, the thesis summarizes the key findings, implications, and recommendations derived from the study. It underscores the significance of incorporating AI into reservoir engineering workflows to enhance decision-making processes, improve reservoir management practices, and ultimately increase hydrocarbon recovery. The research contributes to the growing body of knowledge on the application of AI in the oil and gas industry and sets the foundation for future research endeavors in this field. Overall, this thesis serves as a comprehensive exploration of the application of Artificial Intelligence in reservoir characterization and production optimization, highlighting its potential to transform traditional reservoir engineering practices and drive innovation in the oil and gas sector.

Thesis Overview

The project titled "Application of Artificial Intelligence in Reservoir Characterization and Production Optimization" aims to explore the utilization of artificial intelligence (AI) in enhancing the efficiency and accuracy of reservoir characterization and production optimization processes within the field of petroleum engineering. Reservoir characterization involves the analysis of subsurface reservoir properties to understand its behavior, while production optimization focuses on maximizing hydrocarbon recovery from the reservoir. AI technologies, such as machine learning algorithms and neural networks, have shown great potential in transforming traditional reservoir engineering practices by providing advanced data analytics, predictive modeling, and decision-making capabilities. By integrating AI tools into reservoir characterization and production optimization workflows, this research seeks to improve reservoir management strategies, increase production rates, and reduce operational costs for oil and gas companies. The study will begin with a comprehensive literature review to examine existing AI applications in the petroleum industry, highlighting their benefits and limitations. Subsequently, the research methodology will outline the data collection techniques, AI models, and software tools to be employed in the study. The data collected will include reservoir properties, production data, and historical performance metrics from relevant case studies. The findings of the project will be presented in chapter four, where the results of applying AI techniques to reservoir characterization and production optimization will be analyzed and discussed. This analysis will include comparisons between traditional methods and AI-driven approaches in terms of accuracy, efficiency, and cost-effectiveness. The conclusion and summary in chapter five will provide a concise overview of the key findings, implications, and recommendations derived from the study. It will also discuss the potential impact of AI technologies on the future of reservoir engineering practices and highlight areas for further research and development. Overall, this project aims to contribute to the ongoing evolution of petroleum engineering practices by demonstrating the benefits of integrating AI into reservoir characterization and production optimization processes. Through this research, the potential for improved reservoir management, increased hydrocarbon recovery, and enhanced operational efficiency in the oil and gas industry will be explored and discussed.