With over 50 years of expertise and a portfolio exceeding 10,000 pipeline studies across more than 50 countries, Hydraulic Analysis Group stands as a world leader in pipeline hydraulic modeling, digital twins, surge analysis, and on-site diagnostic solutions. Trusted by major players in the water, oil, gas, energy, and petrochemical sectors, the company delivers unbiased, independent advice that empowers clients to make informed decisions about their most valuable pipeline assets.

Hydraulic Analysis Group’s core offering revolves around advanced hydraulic digital twins and real-time leak detection, powered by their proprietary VariSim™ software. This technology, developed over decades by Simulation Software Limited within the group, sets benchmarks in modeling accuracy by accommodating complex control systems, variable pipeline wave speeds, and temperature fluctuations. Their solutions extend from detailed design and surge analysis to simulation of operational challenges, offering robust support in design validation, efficiency improvement, and compliance with safety and environmental standards.

The company’s innovation is perhaps best exemplified by its SMART Leakage Platform, a live hydraulic model that integrates GIS, pressure, flow, acoustic, and transient data to identify and address leaks rapidly—delivering proven results in regions like the UK, Middle East, and Southeast Asia. Hydraulic Analysis Group’s real-time digital tools continuously help water and energy utilities maximize network efficiency, reduce operational costs, conserve resources, and minimize carbon emissions.

As industries worldwide raise the bar for operational performance, sustainability, and automation, Hydraulic Analysis Group remains a trusted partner at the forefront of digital transformation. Their commitment to high-fidelity modeling, independent consultancy, and collaboration with tech innovators like Rogue7 Inc. ensures clients access the most advanced solutions for pipeline safety, optimization, and future-ready network management.