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The role of crude oil on carbon dioxide (CO 2 ) corrosion has gained special attention in the last few years because of its signifi cance when predicting corrosion rates. However, the complexity and variability of crude oils makes it hard to model its effects, which can infl uence not only wettability properties but also the corrosiveness of the associated brine. This study evaluates the usefulness of artifi cial neural networks (ANN) to predict the corrosion inhibition offered by crude oils as a function of several of their properties that have been related in previous studies to the protectiveness of crude oils, i.e., nitrogen and sulfur contents, resins and asphaltenes, total acid number, nickel and vanadium content, etc. Results showed that neural networks are a powerful tool and that the validity of the results is closely linked to the amount of data available and the experience and knowledge that accompany the analysis.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractA number of different CO 2 corrosion rate prediction models have been developed for application in oil and gas production systems and are readily available in the literature or as industry standards. These models vary from the empirical models to the more recently developed mechanistic models.In this paper a number of CO 2 corrosion rate prediction models are compared. In addition to the obvious criteria of comparing the predicted corrosion rates, the models were also compared for other characteristics such as ease of implementation and insight into the main drivers of the predicted corrosion rate.Using as input the water-chemistry of condensed water, the output from each of the CO 2 corrosion prediction models was compared over a range of temperatures and pressures. A common source dataset was used as input for each of the models.For the range of input values, the suite of selected models generated results which were comparable for lower temperatures and pressures. At higher temperatures and pressures where the effect of the precipitation of iron carbonate would be expected to be influential then the outputs from the models differed. This was in part because in some cases iron carbonate precipitation was not included, but in other cases this difference was due to the alternative implementations of the effect of iron carbonate precipitation.As would be expected the mechanistic models are the more complex to implement with some being such that specialist computer code is required to numerically solve the systems of equations. However, for all the complexity of implementation, the mechanistic models have the particular strength of providing greater insight into the critical variables driving the overall corrosion mechanism and hence suggest strategies for minimizing the CO 2 corrosion risk during design and operation and are therefore to be recommended.

A number of different CO2 corrosion rate prediction models have been developed for application in oil and gas production systems and are readily available in the literature or as industry standards. These models vary from the empirical models to the more recently developed mechanistic models. In this paper a number of CO2 corrosion rate prediction models are compared. In addition to the obvious criteria of comparing the predicted corrosion rates, the models were also compared for other characteristics such as ease of implementation and insight into the main drivers of the predicted corrosion rate. Using as input the water-chemistry of condensed water, the output from each of the CO2 corrosion prediction models was compared over a range of temperatures and pressures. A common source dataset was used as input for each of the models. For the range of input values, the suite of selected models generated results which were comparable for lower temperatures and pressures. At higher temperatures and pressures where the effect of the precipitation of iron carbonate would be expected to be influential then the outputs from the models differed. This was in part because in some cases iron carbonate precipitation was not included, but in other cases this difference was due to the alternative implementations of the effect of iron carbonate precipitation. As would be expected the mechanistic models are the more complex to implement with some being such that specialist computer code is required to numerically solve the systems of equations. However, for all the complexity of implementation, the mechanistic models have the particular strength of providing greater insight into the critical variables driving the overall corrosion mechanism and hence suggest strategies for minimizing the CO2 corrosion risk during design and operation and are therefore to be recommended. Introduction The corrosion of oil and gas equipment due to the presence of CO2 in the production fluids has been the subject of extensive study. A number of CO2 predictive models have been developed in the literature with deferring levels of complexity and different theoretical bases leading to potentially inconsistent assessment of the CO2 corrosion risks. Moreover, the basis and applicability limits of these prediction models are frequently misunderstood. This paper addresses these issues by comparing some commonly used prediction models under a common dataset and providing insight about the strengths and weaknesses of each.

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