The gas producing operations involve large number of flowlines scattered over a huge geographical area. The corrosion issues in gas producing operations are complex and very challenging especially when operational upsets are encountered. In these sceneries, the effective corrosion management relies on predictive analytics and is pro-active data-driven. However, in reality the operational data collection and analysis of a large flowline network in a short period of time is not practicable and usually requires plenty of time and high costs which in turn results reactive measures rather than pro-active, causing huge expense in term of production loss, maintenance and inspections.

In order to address these challenges, an artificial intelligence (AI) based application has been developed to predict internal corrosion risk pro-actively near to real-time. The application automates the entire data acquisition process. It analyzes and identifies internal corrosion threats on operating hydrocarbon gas flowlines capitalizing on the pre-determined process stream data in conjunction with operational data.

This system clearly identifies any protection and preventive violations on large produced gas flowlines network and provide real time recommendations. This has resulted in preventing loss of containment due to internal corrosion, improve system reliability, integrity, availability and profitability. Application includes a user- friendly front end application accessed by end users for tracking the corrosion in the flowlines.

This application has been filed as a Patent in US Patent office (#17/409,400) on 08/23/2021 and has been launched and implemented during 2021.