Thesis Abstract

Abstract
The exploration and characterization of reservoirs play a crucial role in the oil and gas industry. Seismic data interpretation is a key method used for understanding subsurface structures and identifying potential hydrocarbon reservoirs. However, the interpretation of seismic data is a complex and time-consuming process that requires expertise and advanced analytical tools. In recent years, machine learning algorithms have shown great promise in improving the efficiency and accuracy of seismic data interpretation for reservoir characterization. This thesis investigates the application of machine learning algorithms in seismic data interpretation for reservoir characterization. The research aims to develop and evaluate machine learning models that can effectively analyze seismic data to identify and characterize potential hydrocarbon reservoirs. The study focuses on exploring different types of machine learning algorithms, such as supervised learning, unsupervised learning, and deep learning, to enhance the interpretation of seismic data. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review on the application of machine learning algorithms in geophysics and reservoir characterization. The review covers key concepts, methodologies, and findings from previous research studies in this field. Chapter 3 outlines the research methodology used in this study, including data collection, preprocessing, feature extraction, model selection, training, and evaluation. The chapter also discusses the validation and testing of the machine learning models developed for seismic data interpretation. Chapter 4 presents a detailed discussion of the findings obtained from applying machine learning algorithms to seismic data interpretation for reservoir characterization. The chapter analyzes the performance of different machine learning models and discusses the implications of the results. Finally, Chapter 5 provides a conclusion and summary of the research thesis. The chapter summarizes the key findings, discusses the contributions to the field of geophysics, and suggests potential areas for future research. Overall, this thesis contributes to the advancement of seismic data interpretation techniques through the application of machine learning algorithms, offering new insights and opportunities for improving reservoir characterization in the oil and gas industry.

Thesis Overview