https://espace.library.uq.edu.au/view/UQ:379703/UQ379703_OA.pdf

Predicting concrete corrosion of sewers using artificial neural network.

Molecular modelling of organic compounds using computational software has emerged as a powerful approach for theoretical determination of the corrosion inhibition potential of organic compounds. Some of the common techniques involved in the theoretical studies of corrosion inhibition potential and mechanisms include density functional theory (DFT), molecular dynamics (MD) and Monte Carlo (MC) simulations, and artificial neural network (ANN) and quantitative structure–activity relationship (QSAR) modeling. Using computational modelling, the chemical reactivity and corrosion inhibition activities of organic compounds can be explained. The modelling can be regarded as a time-saving and eco-friendly approach for screening organic compounds for corrosion inhibition potential before their wet laboratory synthesis would be carried out. Another advantage of computational modelling is that molecular sites responsible for interactions with metallic surfaces (active sites or adsorption sites) and the orientation of organic compounds can be easily predicted. Using different theoretical descriptors/parameters, the inhibition effectiveness and nature of the metal–inhibitor interactions can also be predicted. The present review article is a collection of major advancements in the field of computational modelling for the design and testing of the corrosion inhibition effectiveness of organic corrosion inhibitors.

Molecular modelling of compounds used for corrosion inhibition studies: a review.

Effective management of assets in the oil and gas industry is vital in ensuring equipment availability, increased output, reduced maintenance cost, and minimal nonproductive time (NPT). Due to the high cost of assets used in oil and gas production, there is a need to enhance performance through good assets management techniques. This involves the minimization of NPT which accounts for about 20–30% of operation time needed from exploration to production. Corrosion contributes to about 25% of failures experienced in oil and gas production industry, while more than 50% of this failure is associated with sweet and sour corrosions in pipelines. This major risk in oil and gas production requires the understanding of the failure mechanism and procedures for assessment and control. For reduced pipeline failure and enhanced life cycle, corrosion experts should understand the mechanisms of corrosion, the risk assessment criteria, and mitigation strategies. This paper explores existing research in pipeline corrosion, in order to show the mechanisms, the risk assessment methodologies, and the framework for mitigation. The paper shows that corrosion in pipelines is combated at all stages of oil and gas production by incorporating field data information from previous fields into the new field’s development process.

/pdf/advances-in-asset-management-techniques-an-overview-of-5e5rxsv5g0.pdf

Advances in Asset Management Techniques: An Overview of Corrosion Mechanisms and Mitigation Strategies for Oil and Gas Pipelines

Local supersaturation and the growth of protective scales during CO2 corrosion of steel: Effect of pH and solution flow

Evolution of corrosion prediction models for oil and gas pipelines: from empirical-driven to data-driven

The role of crude oil on carbon dioxide (CO2) corrosion has gained special attention in the last few years because of its significance when predicting corrosion rates. However, the complex...

/pdf/use-of-artificial-neural-networks-for-predicting-crude-oil-4qq2y9d9c2.pdf

Use of Artificial Neural Networks for Predicting Crude Oil Effect on Carbon Dioxide Corrosion of Carbon Steels

A computer system and method for predicting the aqueous phase CO2 corrosion rate of a pipe useful in the production and transportation of oil and gas. Input parameter values corresponding to water chemistry and physical fluid and pipe properties are received. Based on these input parameter values, the system and method derive current-voltage relationships for multiple cathodic reduction reactions according to an electrochemical model of the corrosion reaction, and a current-voltage relationship for the anodic oxidation reaction of iron dissolution. A current density is obtained, at the intersection of an extrapolation of the anodic current-voltage relationship and an extrapolation of the summed cathodic current-voltage relationships. The predicted corrosion rate is then calculated from the obtained current density. The effects of secondary parameters such as scale and flow regime, and the efficacy of a corrosion inhibitor, can also be evaluated.

Method and system for predicting corrosion rates using mechanistic models

Assessment and comparison of CO2 corrosion predicition models

Thermodynamic Prediction of FeCO 3 /FeS Corrosion Product Films

Design and Operations Guidelines to Avoid Erosion Problems in Oil and Gas Production Systems - One Operator's Approach

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