Application of Machine Learning in Seismic Data Interpretation for Reservoir Characterization

Thesis Abstract

Abstract
The rapid advancement of machine learning technologies has revolutionized various industries, including the field of geophysics. This thesis explores the application of machine learning algorithms in seismic data interpretation for reservoir characterization. The primary objective is to enhance the accuracy and efficiency of reservoir characterization by leveraging the power of machine learning. The introduction provides an overview of the significance of reservoir characterization in the oil and gas industry and highlights the challenges associated with traditional seismic data interpretation methods. The background of the study delves into the evolution of machine learning techniques and their increasing relevance in geophysical applications. The problem statement identifies the limitations of conventional reservoir characterization approaches, such as time-consuming manual interpretation, subjective decision-making, and limited data processing capabilities. The objectives of the study aim to address these challenges by developing and implementing machine learning models for automated and data-driven reservoir characterization. The research methodology outlines the process of data collection, preprocessing, feature selection, model training, and validation. Various machine learning algorithms, including neural networks, support vector machines, and random forests, are explored for their suitability in seismic data interpretation and reservoir characterization. The literature review investigates existing studies and technologies related to machine learning in geophysics, emphasizing their contributions to reservoir characterization. Key topics include feature extraction from seismic data, seismic attribute analysis, and reservoir property prediction using machine learning models. Chapter four presents a detailed discussion of the findings obtained through the application of machine learning algorithms to seismic data interpretation. The results demonstrate the effectiveness of machine learning in improving the accuracy and efficiency of reservoir characterization, leading to enhanced decision-making in the oil and gas industry. The conclusion summarizes the key findings and contributions of the study, highlighting the potential benefits of integrating machine learning into seismic data interpretation practices for reservoir characterization. The significance of this research lies in its potential to revolutionize the way reservoirs are characterized, leading to more informed and data-driven decisions in the exploration and production of oil and gas resources. In conclusion, this thesis provides valuable insights into the application of machine learning in seismic data interpretation for reservoir characterization. The research contributes to the advancement of geophysical technologies and offers practical solutions to enhance the efficiency and accuracy of reservoir characterization processes in the oil and gas industry.