Operational data is only powerful when teams can access it, trust it and act on it. Aspen InfoPlus.21® (IP.21) V15 introduces enhancements that elevate how organizations capture, visualize and use real-time data to improve collaboration and accelerate decision-making.

Attend OPTIMIZE 26 (May 11-14) to hear case studies and best practices from industry leaders such as TotalEnergies, YPFB, Equinor, Ecopetrol and Microsoft.

Metals and mining companies face growing challenges: improving recovery rates, reducing energy consumption and meeting sustainability targets—all while evaluating new process technologies and capital projects.

Process simulation tools—such as steady-state and dynamic modeling platforms—provide value far beyond traditional design and analysis. When simulation models are connected to a broader digital engineering environment, validated process data can be seamlessly reused across disciplines, supporting integrated and efficient project execution.

Oil refineries are complex operations with tight margins, constantly adapting to changing crude types and product demands. Rigorous process simulation software like Aspen HYSYS® provides vital insights to manage these challenges effectively.

En un momento en el que las industrias se ven sometidas a una presión cada vez mayor para impulsar la eficiencia, reducir las emisiones y adaptarse a unas demandas que cambian rápidamente, AspenTech V15 se ha diseñado específicamente para afrontar estos retos críticos.

New release enables companies to achieve rapid ROI and ensure continuous operational improvement.

AspenTech Subsurface Intelligence™ (ASI) is a cloud-native Industrial AI environment designed to optimize upstream oil and gas exploration and production. Built on the OSDU® Data Platform, ASI integrates seamlessly across multivendor environments to automate subsurface workflows, enable real-time collaboration and accelerate geological and reservoir modeling.

Gas-to-Liquids operations demand accurate, auditable production accounting but legacy systems and manual reconciliation often lead to errors, delays and limited visibility.

Uncertainty in subsurface characterization continues to challenge teams working in reservoir evaluation, field development planning and production forecasting. Traditional sequential workflows can introduce bias, limit scenario diversity and fail to capture the full range of subsurface variability—ultimately impacting decision quality.