The hydrogen economy continues to be top of mind for energy and chemical companies, but what’s the best end-to-end approach? What are the risks and the opportunities as industry leaders invest in sustainability initiatives.

Industry leaders are looking for innovative ways to meet their sustainability goals. Hydrogen is a valuable resource that can help companies achieve carbon reduction and reduce energy costs. Download this white paper to learn how digital technology is an essential component in delivering the hydrogen economy and enabling you to:

Learn how the Hydrogen Economy Sustainability Pathway can make key digital technologies easy to adopt for supporting innovation, execution and scaling of hydrogen projects across the value chain, from renewables to hydrogen production, to storage and delivery of hydrogen, to end use.

Hydrogen is playing a key role in meeting the dual challenge of achieving both growth and sustainability initiatives. For over 40 years, AspenTech® has been partnering with industrial leaders to help them meet this dual challenge, by leveraging domain expertise to help organizations run more safely, efficiently and sustainably. With the need to rapidly expand the deployment of hydrogen production globally, companies must address the challenges of high energy and capex cost of innovation, scaleup and storage to expedite speed to market.

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.