Saudi Arabian Oil Company

Fathi BuGubaia

Rashid D Al Hajri

Khalid Al Ghamdi

Mohammad R Khunaizi

Khalid Al Subaie

Zayed Qahtani

Mohammad S Al-Qahtani

Sarafudheen M Tharayil

Muhammad Sohaib Khan

Balakoteswara R Koppuravuri

In an example method, a system obtains first data indicating a plurality of properties of a plurality of gas flow lines. The properties include, for each of the gas flow lines (i) data representing a flow rate of a gas through that gas flow line, (ii) data representing a pressure of the gas in that gas flow line, and (iii) data representing an additive included in the gas in that gas flow line, such as a substance for inhibiting corrosion. For each of the gas flow lines, the system uses a computerized neural network to determine a risk of corrosion associated with that gas flow line based on the properties of that gas flow line, determines whether the metric for that gas flow line is greater than a threshold level, and if so, generates a notification for presentation to a user.

Predicting internal corrosion in gas flow lines using machine learning

The disclosure describes hydrocarbon flowline corrosion inhibitor overpressure protection. Such a protection system includes a fluid flow pathway fluidically coupled to a corrosion inhibitor injection pump that injections corrosion inhibitor into a hydrocarbon carrying flowline. When the injection pump pressure exceeds a threshold flow pressure, the corrosion inhibitor is flowed through a first branch of the fluid flow pathway to relieve the excess pressure. The first branch is fluidically isolated from a second branch. When a rupture disc in the first branch fails, then the corrosion inhibitor is diverted to flow through the second branch and the first branch is isolated from the corrosion inhibitor flow.

Hydrocarbon flowline corrosion inhibitor overpressure protection

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Mubarraz, Saudi Arabia

Fathi BuGubaia