Sweco, Europe’s leading architecture and engineering consultancy, needed a more consistent and scalable approach to cost estimation across its Industry and Energy Division in Finland.

During the early stages of a project, cost estimate accuracy is critical for success. Accurate estimates prevent budget overruns and enable better decision-making during feasibility studies—optimizing CAPEX from the earliest stages.

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.

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.

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.

Industrial leaders understand that AI success depends on more than algorithms, it requires a strong, connected data foundation. While purpose-built Industrial AI can deliver value on its own, organizations that effectively manage and contextualize data are better positioned to scale impact and sustain competitive advantage.