HampsonRussell WellGen Workshop

November 6th, 2023 from from 9:00 AM - 5:00 PM

Learning Objectives

This course presents 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 these supervised learning workflows, the relationship mapping the seismic to the properties of interest are learned from the data itself. Key to deriving robust operators is big data. This course shows how to generate and incorporate synthetic data (WellGen) in the machine learning workflow (Emerge) to get more reliable estimates.

Audience

Content

• Introduction to deep neural networks (DNNs) and the need for big datasets.
• Creating big data by generating synthetic well and seismic data.
• Examples showing how the inclusion of synthetic data improves the property estimates.
• Lithofacies Classification, Rock Physics Modeling, Statistics, Variogram modelling, Well Simulations.
• Generating synthetic seismic data using AVO modelling
• Seismic data preparation including correlation, generation of angle gathers and scaling.
• Supervised learning, multi-linear regression and deep neural networks.
• Generation and selection of seismic attributes for use with supervised learning.
• Quality controlling the results including cross-plotting.
• Explains both the theory and practice of deep neural networks using WellGen and Emerge.
• 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 and Emerge using a real Gulf of Mexico gas sand example.

Price:

US $1000.00 USD