Application of Machine Learning in Seismic Data Interpretation for Reservoir Characterization

Thesis Abstract

Abstract
The utilization of machine learning techniques in the field of geophysics, particularly in seismic data interpretation for reservoir characterization, has become increasingly important in recent years. This thesis explores the application of machine learning algorithms to enhance the accuracy and efficiency of interpreting seismic data for reservoir characterization purposes. The main objective of this study is to investigate the effectiveness of machine learning models in predicting reservoir properties based on seismic data. Chapter 1 provides an introduction to the research topic, background information on machine learning and seismic data interpretation, the problem statement, objectives of the study, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to machine learning in geophysics and reservoir characterization. Chapter 3 outlines the research methodology, including data collection and preprocessing, feature selection, model development, training, and evaluation. The methodology section also details the selection of machine learning algorithms, parameter tuning, and validation techniques employed in this study. Chapter 4 presents an in-depth discussion of the findings obtained from applying machine learning algorithms to seismic data interpretation for reservoir characterization. The results are analyzed and compared to traditional methods, highlighting the advantages and limitations of using machine learning in this context. In conclusion, Chapter 5 provides a summary of the key findings, implications of the research, and recommendations for future studies. The study demonstrates the potential of machine learning techniques to improve the accuracy and efficiency of seismic data interpretation for reservoir characterization, offering valuable insights for the oil and gas industry. Overall, this thesis contributes to advancing the field of geophysics by demonstrating the effectiveness of machine learning in enhancing reservoir characterization through seismic data interpretation. The findings of this study have important implications for improving the efficiency and accuracy of reservoir characterization processes, ultimately leading to better-informed decision-making in the oil and gas industry.