Thesis Abstract

Abstract
The application of machine learning algorithms in seismic data processing for subsurface imaging in oil and gas exploration has gained significant attention in recent years due to its potential to enhance the efficiency and accuracy of subsurface imaging processes. This thesis investigates the use of machine learning techniques to analyze and interpret seismic data for improved subsurface imaging in the oil and gas industry. The research focuses on developing and implementing machine learning algorithms to process and interpret seismic data, with the goal of enhancing the resolution and accuracy of subsurface imaging. The thesis begins with an introduction that provides an overview of the research topic and outlines the objectives of the study. The background of the study discusses the importance of subsurface imaging in oil and gas exploration and highlights the current challenges and limitations of traditional seismic data processing techniques. The problem statement identifies the gaps in existing approaches and sets the foundation for the research questions addressed in this study. The objectives of the study are to investigate the feasibility and effectiveness of using machine learning algorithms for seismic data processing, to develop novel machine learning models for subsurface imaging, and to evaluate the performance of these models in comparison to traditional methods. The limitations of the study are also discussed, including potential challenges and constraints that may impact the research outcomes. The scope of the study defines the boundaries and focus areas of the research, while the significance of the study highlights the potential impact of the findings on the oil and gas industry. The structure of the thesis is organized into five main chapters. Chapter One provides an introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. Chapter Two presents a comprehensive literature review on machine learning algorithms, seismic data processing techniques, and subsurface imaging methods in the context of oil and gas exploration. The review synthesizes existing research and provides a theoretical foundation for the study. Chapter Three outlines the research methodology, including data collection, preprocessing, feature extraction, model development, training, and evaluation. The chapter also discusses the selection of machine learning algorithms, parameter tuning, validation techniques, and performance metrics used to assess the models. Chapter Four presents the results and findings of the study, including the performance evaluation of the developed machine learning models and a comparative analysis with traditional methods. The conclusion and summary in Chapter Five provide a synthesis of the research findings, implications for practice, recommendations for future research, and a reflection on the overall contributions of the study to the field of geophysics and oil and gas exploration. The thesis concludes with a discussion on the potential applications and benefits of integrating machine learning algorithms into seismic data processing for subsurface imaging in the oil and gas industry. In summary, this thesis contributes to the growing body of research on the application of machine learning algorithms in geophysics, specifically for enhancing subsurface imaging in oil and gas exploration. The findings of this study have the potential to improve the efficiency, accuracy, and cost-effectiveness of subsurface imaging processes, ultimately benefiting the oil and gas industry by enabling more informed decision-making and resource optimization.

Thesis Overview