Description

• Minimum Specifications: Participants are required to have access to modern laptops or PCs with a minimum of 8 GB of RAM and at least 30 GB of free disk space.
• Recommended Specifications: For optimal performance, it is recommended that participants use a computer with 16 GB or more of RAM and maintain 30 GB of free disk space.

Designed for you, if you are...

• A reservoir engineer, geologist, petrophysicist, production or drilling engineer with prior programming experience and basic knowledge of data science and machine learning who wants to obtain a solid understanding of neural networks, deep learning and machine learning operations (MLOps)

• Participants should have strong domain knowledge with a minimum of 5 years of experience
• Participants should have prior programming experience
• Prior experience in data science and machine learning is an advantage. If a participant does not have basic knowledge of data science and machine learning, it is recommended to attend the following course:
  Applied Machine Learning and Data Science for Upstream Professionals.

How we build your confidence

• The main Deep Learning algorithms will be thoroughly discussed and accompanied by numerous reusable code examples based on real-world Oil & Gas data sets
• MLOps concepts will be discussed to guide the development of comprehensive end-to-end ML solutions including tasks from project scoping to model serving and building a graphical user interface

The benefits from attending

• Opportunities identification for applying Deep Learning methods in your professional domain
• Sound decision-making in selecting the most suitable machine learning methods for solving specific problems
• Core Deep Learning algorithms and their implementation using TensorFlow and Keras libraries
• Practical application of main machine learning methods in real-world Oil & Gas scenarios

Topics

• Introduction to Deep Learning and TensorFlow ecosystem
• Machine learning 101
• Introduction to MLOps
• Deep Feed Forward neural networks

• Machine learning project setup
• Production forecast for unconventional reservoirs
• Reservoir fluids saturation pressure forecast
• Lithofacies identification

• Set up a machine learning project to ensure the reproducibility and model accuracy
• Use version control, docker, and code templates to streamline the model development and deployment into production
• Construct Deep Feed Forward neural networks using the TensorFlow and Keras framework
• Use various approaches to improve the accuracy of the Deep Learning models
• Explain the prediction of the Deep Learning models

• Time-series modelling
• Anomaly detection
• Bayesian neural networks and Probabilistic Forecasting
• MLOps: data pipelines, hyperparameters tuning and tracking modelling experiments

• Predictive maintenance of the Electrical Submergible Pumps (ESP)
• Production forecasting for the unconventional reservoirs
• Anomalies detection in production well behaviours

• Improve utilization of the Electrical Submersible Pump (ESP) using predictive maintenance
• Detect non-stationary production regimes in production and injection wells
• Perform a probabilistic production forecasting for unconventional oil and gas reservoirs
• Build robust data pipelines to ensure the Deep Learning model quality
• Define the best architecture of the Deep neural network, perform hyperparameters tuning and track the modelling experiments
• Serve the Deep Learning models in production

• Deep convolutional neural networks
• Object detection models
• Image segmentation models
• MLOps: Deep Learning model serving

• Sucker-rod pump performance monitoring
• Microfacies classification based on thin section images
• Object detection based on satellite images

• Monitor the sucker-rod pump performance using automatic interpretation of the dynamogram data
• Detect petrographic microfacies based on thin-section images
• Detect wells, pipelines, tanks, oil spills and other objects of interest in the satellite image data

• Text classification
• Named Entity Recognition (NER)
• Text summarization
• Large Language Models (LLM)
• MLOps: building the interface for a deep learning model and dashboarding

• Developing Oil & Gas word embeddings
• Classification of journal articles
• Creating a Named Entity Recognition (NER) model to detect Oil & Gas-related vocabulary
• Generating abstracts to summarize the journal articles

• Classify the text documents (reports, articles, etc.) based on certain topics
• Identify a certain type of information within text documents
• Create a summary of large text documents
• Extract the knowledge for a large number of unstructured text documents
• Use pre-trained Large Language Models for NLP modelling
• Build a graphical user interface (GUI) to simplify deep learning model usage

• Reinforcement learning
• Generative AI: Generative Adversarial Networks (GANs)

• Optimal well placement
• Generating synthetic well data

• Perform the optimal well placement using reinforcement learning
• Generate synthetic well data (well logs, core images, sensor data, etc.) to improve downstream model accuracy