This course is designed to provide a comprehensive introduction to Graph Neural Networks (GNNs) and their applications in the Oil & Gas industry. Participants will learn the fundamental concepts, architectures, and training techniques of GNNs, enabling them to develop and deploy graph-based models for various tasks relevant to the Oil & Gas domain, such as reservoir characterization, production optimization, and supply chain management.

• Understand the concepts and motivation behind Graph Neural Networks

• Represent and manipulate graph-structured data using Python libraries

• Implement and train various GNN architectures, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs)

• Apply GNNs to solve node classification, edge prediction, and graph classification tasks

• Design and develop GNN-based solutions for Oil & Gas use cases, such as reservoir modeling and production optimization

1. Introduction to Graph Neural Networks

   • Overview of graphs and their applications in the Oil & Gas industry

   • Limitations of traditional machine learning approaches for graph-structured data

   • Introduction to Graph Neural Networks and their advantages

   • Hands-on exercises: Representing and visualizing graphs using Python libraries (e.g., NetworkX, PyTorch Geometric)

2. Graph Convolutional Networks (GCNs)

   • Spectral and spatial graph convolutions

   • GCN architecture and propagation rules

   • Training GCNs using backpropagation and gradient descent

   • Hands-on exercises: Implementing and training GCNs for node classification tasks

3. Graph Attention Networks (GATs) and Message Passing

   • Attention mechanisms in graph neural networks

   • GAT architecture and attention-based message passing

   • Comparison of GATs with GCNs and other GNN variants

   • Hands-on exercises: Implementing and training GATs for edge prediction tasks

4. Advanced GNN Architectures and Techniques

   • Graph Recurrent Neural Networks (GRNNs) for capturing temporal dependencies

   • Graph Autoencoders (GAEs) for unsupervised learning and graph generation

   • Sampling techniques for large-scale graphs (e.g., GraphSAGE, Cluster-GCN)

   • Hands-on exercises: Implementing advanced GNN architectures and techniques

5. GNNs for Oil & Gas Applications

   • Case studies of GNNs in reservoir characterization and modeling

   • Production optimization using GNNs and reinforcement learning

   • Supply chain management and logistics optimization with GNNs

   • Hands-on exercises: Developing a GNN-based solution for a specific Oil & Gas use case

6. Deployment and Future Directions

   • Deploying GNN models in production environments

   • Scaling GNN training and inference for large-scale graphs

   • Hybrid approaches combining GNNs with other machine learning techniques

   • Future research directions and open challenges in GNNs for the Oil & Gas industry

   • Hands-on exercises: Deploying a GNN model using a cloud platform (e.g., AWS, GCP)

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with basic machine learning concepts and techniques

• Knowledge of graph theory and network analysis is beneficial but not required