Various aspects of the mechanism of C02 corrosion are reviewed, together with a discussion about the validity of a number of simplifications which can be used with models for predicting the corrosion rate. A "worst case" rate can often be predicted. To this end a number of parameters has been identified, the
influence of which has to be accounted for. The effects of protective corrosion product layers and of dissolved corrosion product on pH needs to be included in the prediction. More quantitative information about the effect of flowpattern and flowrate is needed. For wet gas pipelines, the prediction of the effect of injection of glycol as a measure against corrosion is of special interest. Predictive models consisting of a system of rules and equations can be conveniently developed in computer spreadsheets.
The pitting corrosion and crevice corrosion of oilfield production alloys (e.g., 13Cr/UNS S41000, 17-4PH/UNS S17400, 25Cr/UNS S32750, A286/UNS S66286, 718/UNS N07718) and proprietary austenitic stainless steels for directional drilling (PREN between ~20 to ~45) has been investigated. Specifically, series of electrochemical tests have been conducted to rank the alloys, establish simple correlations between electrochemical parameters, PRENmod, and 3-to-60-day immersion tests in 3.5% NaCl at ambient temperature. For all but one alloy, pitting was absent in stark contrast to crevices. Upon tracking populations and dimensional characteristics of crevices over time, trendlines comparing the susceptibility of the alloys towards crevice corrosion were established. Practical conclusions were reached, including the following: (1) 13Cr consistently developed crevices within days, (2) 17-4PH as well as all traditional directional drilling stainless steels developed crevices within one to five weeks, and, (3) neither 718, 25Cr, nor newer directional drilling alloys with both high nickel and high PRENmod showed any sign of crevices upon being tested up to 60 days. Through a variety of comparisons, this investigation also reveals useful technical directions for the development of new, economical, and fit-for-service Oil & Gas alloys for both production and drilling.
The control of multiphase flow corrosion in oil and gas industry is one of the biggest challenging tasks. Since the 1990s, several organizations have established and operated large-scale flow loops to simulate and reproduce the field service environment of oil and gas pipelines. Based on comparison and investigation of the above loops, a new and advanced system, including several four inches internal diameter loops for studying corrosion under multiphase flows, was successfully built by us. By using this system, multiphase flows with various combinations of gas, water, oil and sand can be realized at the highest temperature of 140 oC and the highest pressure of 10 Mpa. Moreover, some loops in this system can adjust pipeline at different angels from 0 to 90°, which allow horizontal/vertical/sloping conditions to be simulated in laboratory. Many advanced measuring and monitoring technologies, such as Particle Imaging Velocimetry (PIV), high speed video camera and LPR/ER probe, are employed for simultaneously recording flow events and corrosion rates. An inhouse plane three-electrode probe is employed for conducting in situ electrochemical measurements. Such technologies would allow deep researching of corrosion behaviors and mechanisms in multiphase flow environments. Moreover, a new software based on Fluent and the existing multiphase corrosion models was developed to realize the numerical simulation of multiphase flow in loop.
Hydrogen sulfide gas produced by sulfate reducing microorganisms (SRM) creates significant challenges in the petroleum industry including corrosion concerns, product devaluation, and significant health risks. Biocides and inhibitors are often employed to control these detrimental activities. Recently, co-injection of a synergistic blend of biocides and the SRM inhibitor, nitrite, was proposed as an effective means to control biogenic sulfide production, however, the method only addressed inhibition of SRM activity and not kill. Inhibition can have the undesirable consequence of allowing SRM to resume full activity once the inhibitor is depleted, thus requiring the continuous input of expensive chemicals to maintain control. On the other hand, biocides are designed to reduce SRM concentrations thus reducing the need to add additional chemical until the SRM population re-establishes. Lab results, using an SRM field enrichment, demonstrated that the sequential injection of nitrite inhibitor followed by glutaraldehyde led to an 8-log reduction in SRM while only a 2-log reduction when co-injecting these chemicals at equivalent concentrations. It is proposed that pretreatment with the inhibitor, nitrite, or other respiratory inhibitor, results in a reduction in cellular ATP of the SRM creating a sublethal stress response allowing for their enhanced kill upon subsequent biocide addition.
Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets for which the oil and gas industry is not immune. Over the last few decades, both downstream and upstream industry segments have recognized the magnitude of CUI and challenges faced by the industry in its ability to handle CUI risk-based assessment, predictive detection and inspection of CUI. It is a concern that is hidden, invisible to inspectors and prompted mainly by moisture ingress between the insulation and the metallic pipe surface. The industry faces significant issues in the inspection of insulated assets, not only of pipes, but also tanks and vessels in terms of detection accuracy and precision. Currently, there is no reliable NDT detection tool that can predict the CUI spots in a safe and fast manner. In this study, a cyber physical-based approach is being presented to identify susceptible locations of CUI through a collection of infrared data overtime. The experimental results and data analysis demonstrates the feasibility of utilizing machine-learning techniques coupled with thermography to predict areas of concern. This is through a simplified clustering and classification model utilizing the Convolutional Neural Networks (CNN). This is a unique and innovative inspection technique in tackling complex challenges within the oil and gas industry, utilizing trending technologies such as big data analytics and artificial intelligence.
Solid particle erosion is one of the key issues affecting operational reliability and the cost of tools and equipment in the oil and gas industry. In a particular erosive environment, the extent to which erosion occurs depends on many factors, such as flow conditions, fluid properties, wall material, and particle properties. As a result, it is difficult to investigate the effects of all of these factors using experimental methods. One comprehensive alternative, however, is to use computational fluid dynamics (CFD), which can provide the analyst with a great deal of information about the phenomenon, such as where erosion occurs as well as its severity. Of course, when using any CFD-based erosion prediction method, care must be taken when selecting appropriate meshing practices, solution parameters, and sub-models.
Best practices and guidelines for solid particle erosion modeling using CFD are described. In addition to discussing many parameters that should be considered when using CFD to predict solid particle erosion, the effects of many of these parameters and sub-models within the CFD codes are also discussed with several examples comparing CFD results to available experimental data. This paper can serve as a first step toward developing a comprehensive guideline for the industrial modeling of erosion phenomena and to help engineers improve the accuracy of erosion wear predictions.