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.
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Aspen SeisEarth is a one-stop shop for interpreters. This integrated solution suite provides fast, multi-survey structural and stratigraphic interpretation and visualization from regional to reservoir, enabling multiple users to collaborate in a single shared environment.
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AspenTech Solutions Combined with Machine Learning Help Reduce Geological Risk During Well Planning and Drilling
Brochure:
Subsurface Science & Engineering Solutions for Carbon Storage
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Sustainability Pathway: Carbon Capture, Utilization & Storage