As the hydrogen economy continues to gain momentum, organizations are looking for faster, more efficient ways to design and deploy hydrogen projects. Growing pressure to meet sustainability targets is only increasing the demand to make these initiatives both viable and profitable.

As companies look to advance their sustainability projects to meet net-zero goals, hydrogen has quickly emerged as a viable clean energy solution. But what is the best approach to quickly, reliably and cost effectively deploy green hydrogen production?

In the presence of complex geology and lateral velocity variations, unresolved velocity anomalies in the overburden degrade deeper imaging.

Discover how seismic volume-based unsupervised facies classification associated with advanced visualization and detection helps delineate the prospect’s potential, increase drilling success and reduce cost and risk.

Surface waves can generate coherent noise, known as ground roll, in seismic surveys. Ground roll can significantly degrade data quality.

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.

Learn how AspenTech Inmation works to unlock value from your industrial data.

Recently, the AspenTech DataWorks team commissioned Vanson Bourne to perform an independent study of 200 North American and European decision makers to learn more about their industrial data usage

Learn how BASF, a large global chemical producer, uses AspenTech Inmation™ to unlock value from industrial data.

Today’s leading chemical companies are making better, faster business decisions using data-driven information. How? They are unlocking the value of their data using AspenTech® solutions.