Thesis Abstract

Abstract
The efficient characterization of subsurface reservoirs plays a crucial role in the successful exploration and production of hydrocarbon resources. Seismic data analysis has been a fundamental tool in this process, providing valuable insights into the geological structures and properties of reservoirs. However, the interpretation of seismic data can be complex and time-consuming, often requiring specialized expertise and manual intervention. In recent years, advanced machine learning techniques have emerged as powerful tools for automating and enhancing the analysis of seismic data, offering the potential to improve the accuracy and efficiency of reservoir characterization. This research project focuses on the application of advanced machine learning techniques to analyze seismic data for reservoir characterization. The primary objective is to investigate the effectiveness of machine learning algorithms in identifying and interpreting key geological features within subsurface reservoirs. The study aims to develop and implement a machine learning-based framework that can process and analyze seismic data to extract meaningful information about reservoir properties such as porosity, lithology, and fluid content. The thesis begins with an introduction to the research topic, providing background information on the importance of reservoir characterization and the challenges associated with traditional seismic data analysis methods. The problem statement highlights the limitations of current approaches and the need for more efficient and accurate techniques for reservoir characterization. The objectives of the study are outlined, focusing on the development of a machine learning-based approach to analyze seismic data for reservoir characterization. The methodology chapter details the research approach, data collection procedures, and the specific machine learning algorithms and techniques used in the study. It discusses the preprocessing of seismic data, feature extraction methods, model training, and evaluation processes. The research methodology also includes a description of the evaluation metrics and validation techniques employed to assess the performance of the machine learning models. The findings chapter presents the results of the experimental analysis, showcasing the capabilities of the developed machine learning framework in characterizing reservoir properties from seismic data. The discussion section provides a comprehensive analysis of the findings, highlighting the strengths and limitations of the proposed approach and comparing it with traditional methods of reservoir characterization. The implications of the research findings for the oil and gas industry are also discussed. In conclusion, this thesis demonstrates the potential of advanced machine learning techniques in improving the analysis of seismic data for reservoir characterization. The study contributes to the growing body of research on the application of artificial intelligence in the field of geophysics and offers valuable insights into the benefits of adopting machine learning approaches for subsurface reservoir exploration. The research findings have important implications for the oil and gas industry, providing a pathway towards more accurate and efficient reservoir characterization processes. Keywords Seismic data analysis, Reservoir characterization, Machine learning, Geological features, Oil and gas exploration, Subsurface reservoirs.

Thesis Overview

The project titled "Analysis of seismic data for reservoir characterization using advanced machine learning techniques" focuses on the integration of geophysics and machine learning to enhance reservoir characterization in the oil and gas industry. The primary objective of this research is to leverage advanced machine learning algorithms to analyze seismic data, enabling more accurate and detailed characterization of subsurface reservoirs. Reservoir characterization plays a crucial role in the exploration and production of hydrocarbons, as it provides valuable insights into the size, shape, and properties of underground reservoirs. Traditional methods of reservoir characterization rely on seismic data interpretation by geophysicists, which can be time-consuming and subjective. By incorporating advanced machine learning techniques, this project aims to automate and optimize the analysis of seismic data, leading to more efficient and accurate reservoir characterization. The research will involve collecting and preprocessing seismic data from various sources, including seismic surveys and well logs. Machine learning algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) will be employed to extract meaningful features from the seismic data and identify patterns indicative of reservoir properties. These algorithms will be trained on labeled data to learn the complex relationships between seismic attributes and reservoir characteristics. Furthermore, the project will explore the use of unsupervised learning techniques such as clustering and dimensionality reduction to uncover hidden patterns in the seismic data and classify different reservoir zones. By combining supervised and unsupervised learning approaches, the research aims to provide a comprehensive understanding of reservoir properties and heterogeneities. The expected outcome of this research is a novel framework that integrates advanced machine learning techniques with geophysical data analysis to improve reservoir characterization accuracy and efficiency. By automating the interpretation of seismic data and extracting valuable insights from large datasets, the project aims to enhance decision-making processes in reservoir management and optimize hydrocarbon recovery strategies. Overall, this research represents a significant advancement in the field of reservoir characterization by leveraging cutting-edge machine learning technologies to analyze seismic data. The integration of geophysics and artificial intelligence has the potential to revolutionize the way reservoirs are characterized and managed, ultimately leading to more sustainable and efficient resource extraction practices in the oil and gas industry.