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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 the volume of data within your operations continues to grow, it becomes increasingly difficult to integrate, manage and govern this data. For many companies, it remains one of the biggest challenges to achieving Pharma 4.0.

In today’s VUCA environment, organizations are turning to digitalization and industrial AI to develop autonomous and semi-autonomous processes that enable new levels of safety, sustainability and profitability. Learn how self-optimizing plants will enable companies to: empower next-generation workers to focus on the highest-value activities; improve safety and reliability with IoT predictive maintenance software; reduce downtime of plant operations by optimizing assets and equipment lifecycle; and achieve sustainability goals with reduced energy use and emission control.

This FAQ Document shares answers to questions from the webinar, “Progress Your Pharma 4.0 Journey by Solving Industrial DataOps Challenges.”

The Asset Performance Management (APM) space is evolving rapidly, and a key challenge for many plant teams is scaling and integrating APM solutions across their enterprise. IT and OT convergence also continue to drive the need for innovation.

This ARC report discusses how AspenTech's acquisition of Inmation strengthens its ability to help customers realize maximum business value from industrial data.

Unraveling the huge amount of information contained in seismic reflection images is the main goal of seismic interpretation. In recent years, seismic interpretation capabilities have seen remarkable improvements, many of which are due to the development of a wide range of volumetric seismic attributes.

This work presents a novel technology for azimuth-dependent facies analysis (Facies Analysis versus Azimuth – FACIVAZ) to improve the prediction of hydrocarbon-saturated permeable fractures in terrigenous carbonate reservoirs. The analysis is performed in the depth domain along high-resolution, full-azimuth, angle domain common image gathers created by the Aspen EarthStudy 360™ imaging system.

Aspen Big Loop™ is an open ecosystem for setting up automated, reproducible and auditable subsurface workflows. The workflows help propagate uncertainties and capture their dependencies while calibrating reservoir models to observed data. The result is reliable probabilistic predictions.