March 2016, Vol. 243, No. 3

Features

CO2 Corrosion Prediction: Consider the Need for Validation

Carbon dioxide (CO₂₎ corrosion modeling software is used by asset owners, contractors and consultants in the oil and gas industry to predict the CO₂ corrosion rate at both the design and operation phases of pipelines and well tubing. Prediction results from a dozen CO₂ corrosion models are known to vary wildly. This article discusses the need for CO₂ corrosion model validation and introduces the CO₂ corrosion model validation matrix and index score system for an objective and effective validation of a CO₂ corrosion model’s performance.

Corrosion Model Validation

 CO₂ corrosion is a recognized integrity threat worldwide. CO₂ corrosion modeling has been used at both the design and operation phases of oil and gas pipelines for the prediction of internal corrosion growth rates. Since the classic carbon dioxide corrosion model published by C. DeWaard and D.E. Milliams in 1970s¹, over a dozen CO₂ corrosion models have been developed over the past 40 years. An excellent overview of the different CO₂ corrosion models is given in reference².

A considerable gap exists between CO₂ corrosion model predictions and the reality^2-4. Some CO₂ model developers claim that their model can “accurately” predict this and “accurately” predict that, but when it comes to the corrosion rate prediction, it simply fails and it fails badly.

Figures 1 and 2 show comparisons of the measured corrosion growth rate and the corrosion growth rates predicted from 13 CO₂ corrosion prediction models under two specific field conditions³. Some CO₂ corrosion models consistently underestimate the CO₂ corrosion rate under most operating conditions by a factor of over 10 in some cases (Models “F” and “J”).

Some CO₂ corrosion models consistently overestimate the CO₂ corrosion rate (Models “C” and “K”). Other models simply fail to give reasonable predictions when the operating conditions change (Models “B,” “D” and “G” in Figure 1).

When a model failed to predict the corrosion rate, it failed. Explaining the failure to predict by saying the model is sensitive to pH, sensitive to oil wetting, sensitive to shear stress so on and so forth is completely irrelevant to the end users. It is nothing but the final corrosion rate predicted by a CO₂ corrosion model that matters to the end users.

A model’s ability to “accurately” predict pH, the effects of oxygen, NaCl, bicarbonate, H₂S, HAc, scaling, oil wetting, fluid velocity, and any other factors has absolutely no use if the model consistently fails to make a reasonable prediction of the actual corrosion rate.

Figure 1 shows that four out of the 13 CO₂ corrosion-prediction models produced reasonable corrosion growth rates, while the majority of the CO₂ prediction models simply failed to produce meaningful results. Under another specific field condition (Figure 2), all models failed to produce reasonable corrosion rates.

Contractors or consultants who have been using a single CO₂ corrosion modeling software for all clients and under all operating conditions may not realize the considerable, sometimes shocking uncertainties in the predicted corrosion growth rates (by a factor of over 10). Facility owners and users of CO₂ corrosion model software should protect their interest by validating the CO₂ corrosion model software independently.

Without proper validation, facility owners and users of CO₂ corrosion-modeling programs have no way of knowing the accuracy of the predicted corrosion growth rates. The blind trust in a single CO₂ corrosion model without proper validation and the subsequent use of the modeled results in the design will either expose the assets to increased integrity risk (in case of Models “F” and “J”) or lead to overdesign with the unnecessary use of corrosion-resistant alloys (CRAs) or additional inhibitor dosage (in case of models “C” and “K”).

Commercial CO₂ model software developers typically do not provide the users with any validation details. Validation of modeled results against lab or field data is often difficult as quality lab or field data under the prevailing operating conditions used in the prediction software are not readily available. This is particularly true at the design stage where the input parameters are often simulated or projected.

Validation of modeled results against corrosion-monitoring data in the field may not be applicable as that data is based on “spot” measurement at a specific location, and under some uncertain local operating conditions. Also, the modeled results represent the “worst-case” scenario in the whole system (not spot measurement) under the specific operating conditions.

The only practical way to ensure that the modeled results are reasonably reliable is to validate the CO₂ modeling software itself by utilizing the user’s or any third-party’s well-defined quality lab and field data before starting the modeling project. It is critical to the user or any third-party quality data, not the model developer’s data for the validation process.

Validating Corrosion Models

 An objective and effective validation process must cover a wide range of parameter values in a systematic way. Non-performing CO₂ corrosion models (such as Models “F”, “J”, “C”, “K”) over a wide range of operating conditions will be positively identified and the errors of prediction are objectively quantified.

The following CO₂ corrosion model validation matrix (CO₂MVM) and CO₂ corrosion model validation index score (CO₂MoVIS) systems are proposed for the objective, comprehensive and systematic validation of any CO₂ corrosion-modeling software.

The CO₂ corrosion model validation matrix (CO₂MVM) consists of eight categories of input parameters in three different input value ranges (low, medium and high), with a total of 48 data sets in the matrix. The absolute value of error percent, PE, in each data set is used to compute the average score, defined as the CO₂ corrosion model validation index score (MoVIS), in three input parameter value ranges (low, medium, high).

The MoVIS-L, MoVIS-M and MoVIS-H scores are direct indications of a CO₂ corrosion model’s prediction accuracy in the low, medium and high input parameter value ranges, respectively. The overall MoVIS score is the average of the MoVIS-L, MoVIS-M and MoVIS-H scores, representing the absolute error percentage averaged over the eight input parameter categories in three ranges of input parameter values. The overall MoVIS score is a direct, objective and comprehensive measure of a CO₂ corrosion model’s prediction accuracy.

After validating the CO₂ corrosion model software, facility owners and users of the CO₂ corrosion model software will know the accuracy or the uncertainty in the predicted results which will lead to better engineering and financial decisions when it comes to corrosion allowance, material selection, chemical treatment, CO₂ removal, glycol injection, pH stabilization and other methods for CO₂ corrosion mitigation.

Table 2 shows the recommended parameter value range to be used in the CO₂ Corrosion Model Validation Matrix. It is important to note all data sets used in the matrix must be high quality. CO₂ corrosion modeling follows the “garbage in, garbage out” rule. If a high-quality data set is not available in some boxes in the matrix, leave the boxes blank and exclude them in the computation of the MoVIS score. Low-quality data should never be used in the validation matrix.

High-quality data should meet the following criteria:

 

• The lab or field data must be from a reliable and reputable source and must be verifiable with a clear and detailed description of the source, history and the background information relating to the data.

 

• The lab or field data must be complete and have detailed information on the operating/test conditions and the test/measurement procedures/techniques used to obtain the data. Incomplete data should not be used in the validation matrix.

 

• The lab or field data from – and/or used by – the CO₂ corrosion model developer should not be used in the validation matrix.

 

Conclusion

It is always easier and better to validate the CO₂ corrosion modeling software before beginning a modeling project than trying to validate the modeled results afterward. A CO₂ corrosion prediction model may be a useful tool at the design and operating phases of the oil and gas pipeline, but it must be used with caution.

References 

1. C. De Waard and D. E. Milliams, Carbonic Acid Corrosion of Steel, Corrosion, Vol. 31, No. 5, 1975, 1975, p175-181
2. R. Nyborg, CO2 Corrosion Models for Oil and Gas Production Systems, NACE CORROSION / 2010, Paper No. 10371
3. G. Gabetta, Corrosion and Fitness for Service, 11th International Conference on Fracture 2005 (ICF11), Turin, Italy, 20-25 March 2005, paper No. 4173
4. S. M. HOSSEINI, Avoiding Common Pitfalls in CO2 Corrosion Rate Assessment for Upstream Hydrocarbon Industries, the 16th Nordic Corrosion Congress, 20-22nd May 2015, Stavanger, Norway, Paper No.24

 

Author: J.H. Qiu is the principal consultant at WebCorr Corrosion Consulting Services, Singapore. He is a NACE-certified corrosion specialist and served the Singapore section as a committee member, vice chairman and chairman from the 1990s to early 2000s. Qui obtained bachelor’s and doctorate degrees in engineering both in the field of corrosion. He can be contacted at j.h.qiu@corrosionclinic.com. 

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