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02236 A Neural Network for Predicting Corrosion of Grade N80 Downhole Tubulars Exposed to Stimulation Fluids

Picture for 02236 A Neural Network for Predicting Corrosion...
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Product Number: 51300-02236-SG
ISBN: 02236 2002 CP
Author: Juanita M. Cassidy and Terry H. McCoy
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Artificial neural networks (ANN) have shown promise as predictors in many situations, including corrosion risk assessment. In this investigation, a neural network has been proposed to determine corrosion losses expected from a variety of acid stimulation environments using commercial oilfield service company corrosion inhibitors. Sensitivity analysis was also undertaken to determine which input variables had most affected corrosion losses. Practical usage of a corrosion inhibitor requires development of sufficient data from weight-loss coupon or electrochemical testing to cover the normal acidizing situations, but often oilfield acid job conditions that require extrapolation or interpolation from that data set will arise. In these cases, the only way to recommend an inhibitor loading is to run a corrosion test under the conditions outside of the existing data set. However, time considerations, material availability, or other factors may limit the possibility for testing. In those situations, an accurate ANN that could predict the possibility of success would be desirable. An ANN could also help determine how much corrosion inhibitor to test before running an actual laboratory test.
Artificial neural networks (ANN) have shown promise as predictors in many situations, including corrosion risk assessment. In this investigation, a neural network has been proposed to determine corrosion losses expected from a variety of acid stimulation environments using commercial oilfield service company corrosion inhibitors. Sensitivity analysis was also undertaken to determine which input variables had most affected corrosion losses. Practical usage of a corrosion inhibitor requires development of sufficient data from weight-loss coupon or electrochemical testing to cover the normal acidizing situations, but often oilfield acid job conditions that require extrapolation or interpolation from that data set will arise. In these cases, the only way to recommend an inhibitor loading is to run a corrosion test under the conditions outside of the existing data set. However, time considerations, material availability, or other factors may limit the possibility for testing. In those situations, an accurate ANN that could predict the possibility of success would be desirable. An ANN could also help determine how much corrosion inhibitor to test before running an actual laboratory test.
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