Thesis Abstract

Abstract
Seismic data analysis plays a crucial role in the exploration and extraction of hydrocarbon resources. Traditionally, this process has been labor-intensive and time-consuming, requiring expert interpretation to identify potential reservoirs. With advancements in technology, the integration of machine learning algorithms has revolutionized the field of geophysics, offering efficient and accurate methods for interpreting seismic data. This thesis explores the application of machine learning algorithms in seismic data analysis for hydrocarbon exploration. Chapter one provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter sets the stage for understanding the importance of incorporating machine learning in seismic data analysis. Chapter two presents a comprehensive literature review covering ten key areas related to seismic data analysis, machine learning algorithms, and their application in hydrocarbon exploration. This chapter synthesizes existing knowledge and identifies gaps that this research aims to address. Chapter three outlines the research methodology employed in this study. It includes detailed descriptions of data collection, preprocessing techniques, feature selection, model development, evaluation metrics, and validation processes. The chapter also discusses the selection of machine learning algorithms suitable for seismic data analysis. Chapter four delves into the discussion of findings derived from applying machine learning algorithms to seismic data analysis. It presents the results of the study, including the performance metrics of the developed models, comparison with traditional methods, and insights gained from the analysis of hydrocarbon reservoirs. Finally, chapter five encapsulates the conclusion and summary of the project thesis. It highlights the key findings, contributions to the field of geophysics, implications for hydrocarbon exploration, and recommendations for future research directions. The conclusion underscores the significance of integrating machine learning algorithms in seismic data analysis to enhance the efficiency and accuracy of hydrocarbon exploration processes. In conclusion, this thesis sheds light on the transformative impact of machine learning algorithms in seismic data analysis for hydrocarbon exploration. By leveraging advanced computational techniques, geophysicists can unlock valuable insights from seismic data, leading to improved decision-making in identifying and characterizing subsurface reservoirs. The findings of this research contribute to the ongoing evolution of geophysical exploration practices, paving the way for enhanced resource discovery and extraction in the energy sector.

Thesis Overview