Corrosion in Wet Gas Piping: Root Cause, Mitigation, and Neural Network Prediction Modeling
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TECHNICAL ARTICLE—PEER-REVIEWED
Corrosion in Wet Gas Piping: Root Cause, Mitigation, and Neural Network Prediction Modeling D. Ifezue . F. H. Tobins
Submitted: 22 January 2016 / Published online: 18 February 2016 ASM International 2016
Abstract This paper discusses the root causes and operational mitigations of corrosion anomalies reported for an FPSO wet gas system, and crucially, proposes a neural network (NN) prediction model. The NN model involves ‘back-propagation’ processing of each nodal root cause and mitigation to obtain a value which when combined with a processing weight and then summed, provides an output value. This value is then used to further adjust the weights. Each weight correlates with the magnitude of influence on the overall corrosion rate. The ability to train the model (i.e., weight-adjustment during processing) makes it responsive and adaptable, such that when fresh data inputs are made in a ‘forward-propagation’ mode, into the large modeling database that has been developed (which includes a large number of susceptibility factors), significant increases in the accuracy of predicting corrosion rate and integrity behavior of the wet gas system can be achieved. The identified root causes and mitigations will be useful in further understanding the internal degradation mechanisms operating in wet gas systems in general. Keywords Wet gas Root cause Mitigation Neural network prediction model CO2 corrosion
D. Ifezue (&) DAIZIF Technologies Ltd, Altrincham, UK F. H. Tobins Department of Mechanical Engineering, University of Abuja, P.M.B 117, Abuja, Nigeria
Introduction Internal corrosion of a pipe is random with several causative variables. Only a slight change in one of the causative factors can essentially adjust likelihood and severity. Majority of reported models used for predicting internal corrosion in wet gas systems [1, 7] are based on the principle of mass transport and electrochemistry. These models however are still unable to precisely predict in a repeatable manner, the likelihood of corrosion, rate of corrosion, pattern of corrosion, and probable location where corrosion will occur for piping/pipelines seeing the same operating environment/chemistry. Typical anomalies in wet gas systems are within the low-to-medium severity range (i.e., \50% wall loss). Although a pinhole leak rather than a rupture will be the likely mode of failure, the safety, health, and environment (SHE) consequence is usually in the medium-to-high severities, with the risk of an explosion depending on proximity to an ignition source (e.g., flare or hot compressor motor). The severity of the overall risk is dependent on the chance of people being in the vicinity during a failure event. Oil/gas topsides/FPSO facilities are increasingly reliant on processed inspection data from a typical integrity database in order to understand corrosion patterns and assess integrity. For a system which requires to be operated continuously with: minimum downtime, low safety risks, and low maintenance costs, this can be
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