HampsonRussell GeoAI Workshop

AUGUST 21-24, 2023 at 9:00 - 13:00 CET (each day)

Learning Objectives

GeoAI is a HampsonRussell product, which encompasses a novel technology for seismic reservoir characterization for projects with limited well control. Use Rock Physics theory and statistical simulations to model various geological scenarios. Train convolutional neural networks (CNNs) using simulated synthetic data and transfer learning to real seismic, estimating multiple rock and elastic reservoir properties in a simplified machine learning approach.

Audience

Content

This course covers all aspects of GeoAI starting with an introduction to the practical use of deep neural networks (DNNs) to predict elastic and rock properties, as well as an overview of relevant machine learning theory. In this supervised learning workflow, the relationship mapping the pre-stack seismic to the properties of interest are learned from the data itself. Key to deriving robust operators is big data sets of training data. GeoAI allows to generate and incorporate synthetic data in the machine learning workflow and utilize synthetic data in the CNNs to get more reliable volume estimates.

Topics covered include:

• Supervised learning, multi-linear regression, and deep neural networks.
• Introduction to deep neural networks (DNNs) and the need for big datasets.
• Creating big data by generating synthetic well and synthetic seismic gathers.
• Examples showing how the inclusion of synthetic data improves the property estimates.
• Lithofacies Classification, Rock Physics Modeling, Statistics, Variogram Modeling, Well Simulations.
• Seismic data preparation including correlation, generation of angle gathers and scaling.
• CNN training and transfer learning.
• CNN application for both regression and classification studies.
• Quality control of the results.
• Benefits of GeoAI methodology and practical applications.

Course Benefits

• Explains both the theory and practice of deep neural networks.
• The workshop demonstrates a complete workflow showing how to estimate elastic (e.g., P-wave impedance, Vp/Vs and density) and rock properties (e.g., Porosity, Saturation) using both real and synthetic data. These data science-based (DNN) estimates are compared with theory-based (inversion) estimates.
• Teaches the user how to apply WellGen, Emerge and CNN using a real Gulf of Mexico gas sand example.
  
  
  

Price:

 $2,000.00 USD per attendee (*group discount may apply)