AI for Efficiency in Gas & Oil Utilities
Course Overview:
This course is designed to provide a comprehensive understanding of generative AI techniques and their potential applications in the Oil & Gas industry. Participants will learn about various generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, and how they can be used to generate realistic synthetic data, enhance data augmentation, and solve domain-specific problems. The course covers the theoretical foundations of generative models, as well as practical implementation and evaluation strategies.
Learning Objectives:
Understand the fundamental concepts and theory behind generative AI models
Implement and train GANs, VAEs, and Transformer-based generative models using deep learning frameworks
Generate realistic synthetic data for various Oil & Gas use cases, such as seismic data, well logs, and production profiles
Apply generative models for data augmentation to improve the performance of supervised learning tasks
Utilize generative AI techniques for domain-specific applications, such as reservoir characterization and well placement optimization
Course Highlights:
Introduction to Generative AI
Overview of generative models and their applications in the Oil & Gas industry
Probability distributions and sampling techniques
Generative vs. discriminative models
Hands-on exercises: Implementing basic generative models (e.g., Gaussian Mixture Models, Hidden Markov Models)
Generative Adversarial Networks (GANs)
GAN architecture and training process
Variants of GANs (e.g., DCGAN, WGAN, CycleGAN)
Evaluation metrics for GANs (e.g., Inception Score, Fréchet Inception Distance)
Hands-on exercises: Training a GAN to generate synthetic seismic data or well logs
Variational Autoencoders (VAEs)
Variational inference and latent variable models
VAE architecture and training process
Conditional VAEs and their applications
Hands-on exercises: Implementing a VAE for generating synthetic production profiles
Generative Transformer Models and Applications
Transformer-based generative models (e.g., GPT, BERT)
Fine-tuning generative Transformers for Oil & Gas text data
Domain-specific applications of generative AI in the Oil & Gas industry (e.g., reservoir characterization, well placement optimization)
Hands-on exercises: Fine-tuning a generative Transformer model for generating Oil & Gas reports
Prerequisites:
Strong understanding of deep learning concepts and architectures (e.g., CNN, RNN, Transformers)
Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)
Familiarity with probability theory and statistical concepts