Thesis Abstract

Abstract
The rapid advancements in artificial intelligence (AI) have revolutionized various industries, including petroleum engineering. This thesis explores the application of AI in reservoir characterization and production optimization in the petroleum sector. The primary objective is to investigate how AI technologies can enhance the efficiency and accuracy of reservoir characterization and production optimization processes, ultimately improving the overall productivity and profitability of petroleum operations. The study delves into the background of AI technologies, highlighting their potential benefits and challenges in the context of petroleum engineering. The research begins with a comprehensive literature review that examines existing studies on AI applications in reservoir characterization and production optimization. This review identifies key trends, challenges, and opportunities in the field, providing a solid foundation for the subsequent research. The methodology chapter outlines the research approach, data collection methods, and analytical techniques employed in the study. It also discusses the criteria used to evaluate the effectiveness of AI technologies in reservoir characterization and production optimization. The findings chapter presents the results of the research, showcasing how AI technologies can significantly enhance the accuracy and efficiency of reservoir characterization and production optimization processes. Through detailed case studies and data analysis, the study demonstrates the practical benefits of integrating AI solutions into petroleum engineering practices. The discussion chapter critically evaluates the findings, comparing them with existing literature and highlighting the implications for the industry. In conclusion, this thesis underscores the importance of leveraging AI technologies to improve reservoir characterization and production optimization in petroleum engineering. The study makes a significant contribution to the field by showcasing the potential of AI to transform traditional practices and drive innovation in the petroleum sector. The research findings offer valuable insights for industry practitioners, policymakers, and researchers seeking to harness the power of AI for enhanced performance and sustainability in petroleum operations. Overall, this study serves as a comprehensive exploration of the application of artificial intelligence in reservoir characterization and production optimization in petroleum engineering, offering practical recommendations and insights for future research and industry applications.

Thesis Overview

The project titled "Application of Artificial Intelligence in Reservoir Characterization and Production Optimization in Petroleum Engineering" aims to explore the integration of artificial intelligence (AI) technologies in enhancing reservoir characterization and production optimization processes within the petroleum engineering domain. This research overview delves into the significance of leveraging AI tools and techniques to address the challenges faced by the petroleum industry in maximizing the efficiency and productivity of oil and gas reservoirs. Reservoir characterization is a crucial aspect of petroleum engineering that involves the detailed analysis and understanding of subsurface reservoir properties such as rock and fluid characteristics, permeability, porosity, and reservoir geometry. Traditional reservoir characterization methods often rely on manual interpretation of seismic data, well logs, and other geological information, which can be time-consuming and prone to human errors. By incorporating AI algorithms and machine learning models, this project seeks to streamline the reservoir characterization process by automating data analysis, pattern recognition, and predictive modeling. Furthermore, production optimization in petroleum engineering pertains to the strategic management of reservoir operations to maximize hydrocarbon recovery while minimizing costs and environmental impact. AI-based optimization techniques offer the potential to optimize production strategies, well placement, and reservoir management decisions through real-time data analytics, predictive modeling, and decision support systems. By harnessing AI capabilities, this project aims to improve reservoir performance, enhance production efficiency, and ultimately increase the profitability of oil and gas operations. The research will involve a comprehensive review of existing literature on AI applications in reservoir characterization and production optimization, highlighting the latest advancements, challenges, and opportunities in this rapidly evolving field. Subsequently, the project will employ a research methodology that integrates data collection, analysis, modeling, and simulation techniques to demonstrate the effectiveness of AI-driven solutions in enhancing reservoir management practices. Overall, this project seeks to contribute to the ongoing digital transformation of the petroleum industry by showcasing the potential benefits of AI technologies in revolutionizing reservoir characterization and production optimization processes. Through empirical research and data-driven insights, the findings of this study aim to provide valuable recommendations and guidelines for industry professionals, researchers, and decision-makers seeking to leverage AI for sustainable and efficient petroleum engineering practices.