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Close
Main menu
  • Solutions
    • Leak detection
    • Theft detection
    • Simulation
    • Operations management
    • Tightness monitoring
    • Maintenance
  • Industries
    • Oil
    • Gas
    • Chemical
    • Water
      • Clean water
      • Wastewater
    • Aviation
    • Mining & slurry
  • Products & services
    • Atmos Pipe
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Blogs

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Key considerations for leak detection on gathering networks

Gathering networks are an important aspect of the upstream energy distribution, but they are often located in high consequence areas (HCAs) and remote locations, so leak detection is a vital requirement. Sales and Support Engineer Angela Maya discusses the key considerations for leak detection on gathering networks in this blog.

A spotlight on pipeline theft in Latin America

Pipeline theft remains a global problem, with each region being impacted in different ways due to the multidimensional nature of the crime. Sales and Senior Research Engineer Harry Smith sheds light on the pipeline theft challenges facing Latin America in this article and how Atmos is directly responsible for ongoing support to the region.

High consequence areas vs moderate consequence areas: what’s the difference?

Federal regulators such as PHMSA have introduced the terms high consequence areas (HCAs) and moderate consequence areas (MCAs) to define pipelines constructed close to high-risk areas and ensure they operate under the safest conditions. This blog discusses the difference between HCAs and MCAs.

Hiding in plain sight: theft detection with transient operations

When it comes to pipeline theft, it’s the details that count. In this article by Training Manager Daniel Arroyo, learn how Atmos Theft Net can support in the understanding of double transients and differentiating pipeline theft events from leaks.

Pedal to the metal: how machine learning (ML) is optimizing gas pipeline operations

The availability of cloud computing has made it feasible to run complex pipeline simulations quickly, which has allowed machine learning to be introduced to gas transmission and distribution networks, but can machine learning overcome the challenges of a gas pipeline operation?

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