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Incorporating Monte Carlo Analysis into a State-of-the-Art Corrosion Prediction System

Picture for Incorporating Monte Carlo Analysis into a State-of-the-Art Corrosion Prediction System
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Over the years there have been several different corrosion modelling software packages developed to provide predicted (estimated) corrosion rates for use in the oil & gas industries. Many are based on the original work of DeWaard & Milliams which provided a best-fit statistical model to corrosion rates measured in flow loop laboratory tests conducted at the IFE (Institutt For Energiteknikk) in Norway ; covering (initially) just partial pressure of CO2, temperature, liquid flow velocity and pH (typically as bicarbonate and dissolved CO2).

Product Number: 51323-18910-SG
Author: Andrew Simm, Daniel Thomas, Gareth John, Steve Hodges
Publication Date: 2023
$0.00
$20.00
$20.00

The development of corrosion prediction software systems over the years has concentrated on scalar analysis, that is, using fixed input values to develop a single output parameter (corrosion rate). This is consistent with the origins of the majority of prediction systems which are based on the results of a series of laboratory tests using a standard changing variable approach to develop an algorithm providing corrosion rate from the varying inputs.
However, it has also been recognised that in real life situations, input parameters are not fixed and also that any algorithm will have a degree of uncertainty (prediction error). In order to account for variable conditions, a series of sensitivity calculations are often carried out, covering expected changes in (for example), flow rates, operating temperatures & pressures, process chemistry variations, etc. Then taking the varying predicted corrosion rates to provide an expected operating range.

The development of corrosion prediction software systems over the years has concentrated on scalar analysis, that is, using fixed input values to develop a single output parameter (corrosion rate). This is consistent with the origins of the majority of prediction systems which are based on the results of a series of laboratory tests using a standard changing variable approach to develop an algorithm providing corrosion rate from the varying inputs.
However, it has also been recognised that in real life situations, input parameters are not fixed and also that any algorithm will have a degree of uncertainty (prediction error). In order to account for variable conditions, a series of sensitivity calculations are often carried out, covering expected changes in (for example), flow rates, operating temperatures & pressures, process chemistry variations, etc. Then taking the varying predicted corrosion rates to provide an expected operating range.

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