Rock Physics Driven Machine Learning for Reservoir Characterization with GeoAI

Course Overview

GeoAI is a HampsonRussell product that encompasses a novel seismic reservoir characterization technology 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 apply to real seismic gathers, estimating multiple rock and elastic reservoir properties in a simplified machine learning approach.

This course covers all aspects of GeoAI, beginning with an introduction to the practical use of deep neural networks (DNNs) for predicting elastic and rock properties, and an overview of relevant machine learning theory. In this supervised learning workflow, the relationships mapping the pre-stack seismic to the properties of interest are learned from the data itself. Key to deriving robust operators from large training datasets. GeoAI enables the generation and incorporation of synthetic data into machine learning workflows and its use in CNNs to obtain more reliable volume estimates.

Content:

• 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 wells and synthetic seismic gathers covering the ranges of expected variations in reservoir properties and reservoir thickness.
• Examples showing how the inclusion of synthetic data improves the property estimates.
• Lithofacies Classification, Rock Physics Modeling, Statistics, Variogram Modeling, and 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.

Software covered

HampsonRussell GeoAI

Who should attend?

Geophysicists, geologists, engineers, and technical staff who want to understand the theory and learn how to apply these increasingly critical techniques

Pre-requisite

Experience with HampsonRussell Software is not a prerequisite for this workshop; however, students will become familiar with its functionality through participation.

Duration

2 days (4 hours per day)

Format

Instructor-led, workflow-based, virtual training

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