Abstract: Methods and systems for a flowline corrosion manager is disclosed. The method includes obtaining gas flowline data regarding a first pipe component in a gas production network, determining internal assessment data for the first pipe component, and determining external assessment data for the first pipe component. Gas flowline data includes operating sensor data, cathodic protection status updates, and corrosion inhibitor compliance data over the gas production network. Internal corrosion assessment data include an internal report identifying internal corrosion presence. External corrosion assessment data includes an external report identifying external corrosion presence. The method further includes determining whether the internal corrosion assessment data and the external corrosion assessment data satisfy an integrity criterion for the first pipe component and transmitting a command that implements a remediation operation for the first pipe component.

Abstract: 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.

Abstract: 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.