Application of Machine Learning in Seismic Data Analysis for Reservoir Characterization

Thesis Abstract

Abstract
This thesis investigates the utilization of machine learning techniques in seismic data analysis for reservoir characterization, aiming to improve the accuracy and efficiency of identifying and characterizing subsurface reservoirs. The growing demand for energy resources necessitates the development of advanced technologies to enhance the exploration and production of hydrocarbons. Seismic data analysis plays a crucial role in understanding subsurface structures and properties, aiding in the identification of potential reservoirs for oil and gas exploration. However, the interpretation of seismic data is complex and time-consuming, requiring expertise and manual intervention. Machine learning algorithms offer a promising approach to automate and enhance this process, enabling faster and more accurate reservoir characterization. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the stage for the research by highlighting the importance of applying machine learning in seismic data analysis for reservoir characterization. Chapter 2 presents a comprehensive literature review, discussing ten key studies and research works related to the application of machine learning in seismic data analysis and reservoir characterization. The review covers various machine learning algorithms, methodologies, and case studies that have been employed in similar research areas, providing valuable insights and a foundation for the current study. Chapter 3 details the research methodology employed in this study, including data collection, preprocessing, feature extraction, model selection, training, and evaluation. The chapter outlines the steps taken to implement machine learning algorithms in seismic data analysis, highlighting the significance of each stage in achieving accurate reservoir characterization results. Chapter 4 presents an in-depth discussion of the findings obtained from the application of machine learning in seismic data analysis for reservoir characterization. The chapter analyzes the performance of different machine learning models, evaluates the accuracy of reservoir characterization results, and discusses the implications of the findings in the context of oil and gas exploration. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future studies in the field. The chapter highlights the contributions of this research to the field of geophysics and emphasizes the potential impact of applying machine learning techniques in seismic data analysis for reservoir characterization. In conclusion, this thesis demonstrates the effectiveness of machine learning in enhancing the accuracy and efficiency of seismic data analysis for reservoir characterization. The research findings contribute to the advancement of geophysical exploration techniques and offer valuable insights for industry professionals and researchers working in the field of oil and gas exploration.