Predict-Pipe 4.3 – Pipeline Corrosion Prediction and Prevention

Predict-Pipe 4.3 – Pipeline Corrosion Prediction and Prevention

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Corrosion Prediction

Predict®-Pipe 4.3 enables easy and accurate Internal Corrosion Direct Assessment of both dry gas (DG-ICDA) and wet gas (WG-ICDA) pipelines exposed to corrosive environments due to water accumulation. The latest release integrates multi-phase flow modeling with Honeywell’s proven corrosion prediction technology to enable informed financial and engineering decisions.​

​​​​Discover more about Honeywell Predict Corrosion Suite now​​

Predict-Pipe 4.3 offers: 

  • An ionic, pH computation module that accounts for the effects of 16 different anion and cation species

  • Rigorous water phase behavior calculations coupled with the ability to account for the effects of glycol (MEG, TEG and DEG)

  • Wet-Gas ICDA module to model onshore and off-shore natural gas pipeline systems carrying wet gas that may often contain produced or condensed water (Not covered by DG- ICDA)

  • Advanced flow modeling and ability to correlate wall shear stress effects on corrosion rate 

  • Ability to accurately determine scaling effects due to formation of iron carbonate and iron sulfide scales as a function of temperature and pH

  • pH prediction module that facilitates accurate inlet and outlet condition characterizations

  • Corrosion prediction based on aqueous CO2 / H2S data.

  • Ability to accurately characterize role of oxygen concentration in corrosive systems

  • Improved rules to account for variation of water content in oil and gas systems (production and transmission).

  • NEW! CO2 and H2S Concentrations can now be entered with very low values

  • NEW! Improved pressure drop calculations

Predict-Pipe 4.3 is built upon a framework of standard industry practices (NACE SP0206, SP0110), proprietary data from hundreds of laboratory tests that constituted a Joint Industry Project (JIP) on multi-phase CO2/H2S corrosion and the industry’s most comprehensive database on steel corrosion rates. The software incorporates environmental (electrochemical) as well as fluid dynamic parametric effects on corrosion.

Predict-Pipe 4.3 offers an intuitive user interface that takes a little time to master and delivers results quickly. In addition, a multiphase flow model enables the user to visualize the flow regime and evaluate the effects of flow occurring in a particular section of a pipe.

With Predict-Pipe 4.3, users can:

  • Benefit from significant savings in inspecting and managing transmission pipelines

  • Prevent failure due to corrosion through proactive corrosion management 

  • Facilitate line assessment in situations where alternative methods (inline inspection, hydro testing) may be impractical because the pipelines are considered 'unpiggable'

  • Accurately identify critical segments with potential for corrosion damage

  • Perform detailed examination of dry gas and wet gas pipelines through appropriate identification of assessment sites

  • Predict phase behavior of water in transmission lines

  • Determine multiphase flow behavior and accurately correlate flow effects

  • Graphically view corrosion profile over entire length of pipeline

  • Perform comprehensive corrosion and cost characterization for entire pipeline systems. 

Documentation
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Predict® Corrosion Suite BrochurePrevent corrosion by intelligently predicting when it occurs326.11 KBBrochure13 Apr 2018
Predict-Pipe4-1-PINPredict®-Pipe 4.1 is the latest release of the industry-leading pipeline corrosion evaluation software suite designed to accurately quantify corrosion in natural gas gathering and transmission pipeline systems.455.34 KBProduct Information Note25 Feb 2016

​Training, maintenance and support are critical aspects to consider when choosing a technology partner. Honeywell offers world-class training through our Automation College as well as comprehensive maintenance and support services designed to leverage your physical and intellectual assets and help you sustain and increase their value and performance over time.​
 
 
 
 
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