Using a self-growing neural network approach to CCS monitoring

English
Japanese
Russian
Chinese

Using a Self-growing Neural Network Approach to CCS Monitoring

This article shows how a machine-learning workflow based on a Self-Growing Neural Network (SGNN) was used by Aspen SeisEarth™ as an efficient and unbiased scanning tool for carbon capture and storage (CCS) monitoring, enabling faster identification of the confinement system.

AspenTech DataWorks

Asset Performance Management

Digital Grid Management

Manufacturing and Supply Chain

Performance Engineering

Subsurface Science & Engineering

Using a Self-growing Neural Network Approach to CCS Monitoring

Related Content