Oil Corrosion Research Articles

Water Separation and Formation of Cells with Differential Aeration as Factors Controlling Corrosion of Steel Pipelines in a Crude Oil Storage Facility

The mechanism causing the dramatic intensification of the corrosion deterioration of carbon steel pipes in a crude oil storage facility has been investigated. This study considers a number of factors affecting corrosion in crude oil, such as the water content, the corrosivity of the aqueous phase, the kinetics of water–oil separation, the effect of dissolved oxygen, the effect of the crude oil quality, the degree of stagnancy inside of the pipes, the possible contribution of microbially induced corrosion (MIC) and the presence of deposits. The key root of the corrosion intensification was the separation of the water phase, supported by stagnancy, which eventually led to the formation of stable shallow pits surrounded by cathodic areas.

Research of the anti-static and corrosion resistance of S-ZnO/m-SWCNTs/ FC composite coating

Abstract In order to solve the hazards caused by corrosion and charge accumulation in aircraft, electronic equipment, oil and gas pipelines, the corrosion resistance and anti-static performance of coatings need to be noted. In this study, monodisperse single-walled carbon nanotubes functional slurry (m-SWCNTs) was obtained by the combination of high-energy ultrasonic dispersion and non-covalent amphiphilic dispersant (PVP). In this way, the uniform dispersion of low-content m-SWCNTs in fluorocarbon composite coatings was achieved, and the micronano structured surface of the fluorocarbon composite coating was constructed by using the modified-ZnO in order to achieve superhydrophobicity. The results showed that the corrosion resistance of the fluorocarbon composite coating was further improved and the antistatic property of the composite coating met the use requirements, which was achieved through the synergistic effect of the m-SWCNTs network and the modified-ZnO. The resistivity of S-ZnO/m-SWCNTs/FC coating is 3.15 Ωm, acting as anti-static coating to reduce safety hazards, its corrosion current is 9.66×10−8 A/cm2, which is lower than bare steel by 3 orders of magnitude. Meanwhile, the composite coating owns the self-cleaning, superhydrophobic and organic solvent affinity, acting as oil-water separation coating. This research provides a new idea for the fabrication of multifunctional coating, elucidating the significant potential of its applications.

A novel instrumentation approach for achieving in-situ temperature sensing of a Li-ion cylindrical cell under immersion cooling

Battery thermal management systems are critical for high-performance electric vehicles, where the ability to remove heat and homogenize temperature distributions in single cells and packs are key considerations. Immersion cooling, which immerses the battery in a dielectric fluid, can improve cooling efficiency compared to air cooling. However, stringent instrumentation standards make in-situ monitoring under immersion cooling a difficult challenge. This study proposes a novel cell instrumentation method based on the integrated combination of three O-rings, which for the first time achieves in-situ temperature sensing of batteries under oil-immersed cooling conditions. Reliability of the sealing system and instrumentation methodology is ensured by leakage tests and oil corrosion assessment. Data such as cell impedance and discharge capacity also support the success and reliability of the instrumentation process without affecting the battery performance. Compared with natural air cooling, immersion cooling can reduce the temperature rise during battery operation by >10 °C under high C-rate discharge conditions. However, this is accompanied by an increase in radial temperature gradient. The temperature gradient of the cell with oil immersed reaches a maximum of >7 °C, while the temperature gradient under natural cooling under the same test conditions is only 3 to 4 °C. This leads to intensified temperature inhomogeneity of the cell, which may accelerate the local ageing and degradation. Our instrumentation approach contributes to a deeper understanding of the thermal behaviour of batteries under direct immersion cooling, aiding the development of more advanced cooling strategies and associated battery thermal management models.

Development of a New Method for the Assessment of Mineral Insulating Oil Corrosivity Against Silver

Development of a New Method for the Assessment of Mineral Insulating Oil Corrosivity Against Silver

Corrosion Characteristics of Polymer-Modified Oil Well Cement-Based Composite Materials in Geological Environment Containing Carbon Dioxide.

Oil well cement is easily damaged by carbon dioxide (CO2) corrosion, and the corrosion of oil well cement is affected by many factors in complex environments. The anti-corrosion performance of oil well cement can be improved by polymer materials. In order to explore the influence of different corrosion factors on the corrosion depth of polymer-modified oil well cement, the influence of different corrosion factors on corrosion depth was studied based on the Box-Behnken experimental design. The interaction of different influencing factors and the influence of multiple corrosion depths were analyzed based on the response surface method, and a response surface model was obtained for each factor and corrosion depth. The results indicate that within the scope of the study, the corrosion depth of polymer-modified oil well cement was most affected by time. The effects of temperature and the pressure of CO2 decreased sequentially. The response surface model had good significance, with a determination coefficient of 0.9907. The corrosion depth was most affected by the interaction between corrosion time and the pressure of CO2, while the corrosion depth was less affected by the interaction between corrosion temperature and corrosion time. Improving the CO2 intrusion resistance of cement slurry in an environment with a high concentration of CO2 gas can effectively ensure the long-term structural integrity of cement.

Addressing Hydrogen Sulfide Corrosion in Oil and Gas Industries: A Sustainable Perspective

In the oil and gas industry, the corrosion attributed to hydrogen sulfide (H2S) is one of the most significant challenges. This review paper systematically investigates the diverse facets of H2S corrosion, including its sources, corrosion locations, mechanisms, and resultant corrosion products. Understanding different forms of H2S corrosion, such as stress-oriented hydrogen-induced cracking (SO-HIC), sulfide stress cracking (SSC), and hydrogen-induced cracking (HIC), provides a thorough comprehension of these phenomena. The paper discusses critical factors influencing H2S corrosion, such as temperature, flow rate, pH, and H2S concentration, highlighting their implications for sustainable practices in the oil and gas sector. The review emphasizes the significance of monitoring and mitigation strategies, covering continuous monitoring, applying corrosion inhibitors, selecting materials, and conducting thorough data analysis and reporting. Furthermore, the role of training in fostering a sustainable approach to H2S corrosion management is highlighted. This exploration advances the overarching goal of sustainable development in the oil and gas industries by providing insights into understanding, monitoring, and mitigating H2S corrosion. The findings presented here offer a foundation for developing environmentally conscious strategies and practices to guarantee the long-term viability and flexibility of refinery operations.

Mapping soil corrosivity potential to exclusion fencing using pedotransfer functions and open‐source soil data

AbstractSoil corrosivity is a term used to describe the corroding susceptibility (risk) of metal infrastructure in different soil environments. Soil corrosivity mapping is a crucial step in identifying potentially problematic, high‐maintenance fence lines and can help improve fence longevity by identifying soil environments where the use of more expensive, corrosion‐resistant materials would be more cost‐effective in the long term. Soil corrosion damage sustained on exclusion fences can be a serious management issue for conservation programs and initiatives, as it weakens the fence netting and provides opportunities for invasive animal migration and occupation (e.g. feral cats and foxes) into areas of high conservation value. The increasing accessibility of geospatial analysis software and the availability of open‐source soil data provide land managers with the opportunity to implement digital soil databases and pedotransfer functions to produce fence corrosion risk maps using commonly measured soil attributes. This paper uses open‐source government agency soil data (shapefiles) to map fence corrosion risk in the southern part of the Yorke Peninsula in South Australia, with the intention to assist with the installation of a new barrier (exclusion) fence as part of the Marna Banggara rewilding project. The risk classifications (low, moderate and high risk) made by this map were compared with rates of zinc corrosion (μm/year zinc loss) observed at field sites and correctly predicted the amount of fence damage sustained at five of the eight sites. The mapping approach outlined in this study can be implemented by environmental managers in other areas to inform strategies for enhancing fence longevity.

2,4-dihydroxybenzaldehyde based Schiff base derivatives as an effective corrosion inhibitor for steel corrosion in 1 M hydrochloric acid solution

The present research is centered on Schiff base derivatives such as 4-((hexylimino)methyl)benzene-1,3-diol (HIMB) and 4,4′-((1E,1′E)-(hexane-1,6-diylbis(azaneylylidene))bis(methaneylylidene))bis(benzene-1,3-diol) (HAMB). FTIR and 1H NMR were used to determine the structure of the produced Schiff base compounds. Furthermore, gravimetric and electrochemical methods were used to investigate the efficacy of these compounds to suppress corrosion of carbon steel oil well pipes in filtered formation water solution containing 1 M hydrochloric acid. Experiments at various concentrations allowed us to assess HIMB and HAMB's inhibitory efficacy and adsorption behavior. The investigated inhibitors become further active at preventing as concentration increases. The potentiodynamic polarization investigation found that HIMB and HAMB both inhibit cathodic and anodic processes. Affording to impedance (EIS) information, the charge transfer resistance (Rct) increases when HIMB and HAMB concentrations grow. Adsorption of investigated inhibitors at the carbon steel according to Langmuir isotherm model was measured using gravimetric measurements. The presence of a membrane of HIMB and HAMB inhibitors on top of the carbon steel surface of oil well pipes was supported by theoretical data as well as experimental tests.

Development of machine learning algorithms for predicting internal corrosion of crude oil and natural gas pipelines

Energy industry is losing billions of dollars each year due to corrosion. However, publicly accessible datasets on pipeline corrosion are limited, making it difficult to accurately predict and prevent corrosion. In this research, thousands of corrosion scenarios of crude oil pipelines and natural gas pipelines were simulated using OLI Studio Corrosion Analyzer, a software based on the rigorous first principles. Thousands of datasets were generated with simulation. Then two ML algorithms, Random Forest (RF) and CatBoost were developed and used to predict the internal corrosion rate (CR) based on pipeline operating conditions. The models for crude oil pipeline CR predictions reached high prediction accuracy using five important features: temperature (T), molar concentrations (CO2, CCl−1), velocity (v), and pH value (pH). Similarly, the models for natural gas pipeline CR predictions achieved high prediction accuracy using six important features: stream compositions (nO2,nH2S,nCO2), pressure (P), velocity (v), and temperature (T). Both the RF- and CatBoost-Corrosion Rate models demonstrated good performance in predicting the internal corrosion rates of crude oil and natural gas pipeline.

A study on the microbial biocorrosion behavior of API 5 L X65 carbon steel exposed to seawater

AbstractDespite much progress achieved, corrosion in oil and gas pipelines remains a concern due to industrial failures, and environmental and economic damages. Oil and gas pipeline failures are very common as a result of the natural degradation of carbon steel exposed to the marine environment. It has been reported that the presence of microorganisms in seawater contributes to the intensification of corrosion and subsequent biofilm formation on metal surfaces. Few scientific publications have investigated the effects of seawater containing natural microorganisms on the internal corrosion of pipelines, which is the motivation for this study. The present study aims to report the corrosion behavior of carbon steel American Petroleum Institute (API) 5 L X65 exposed to seawater collected from the Persian Gulf containing microorganisms such as sulfate‐reducing bacteria (SRB), iron‐reducing bacteria (IRB), and acid‐producing bacteria (APB). Microorganisms were detected, segregated, colonized, and finally injected into autoclaved seawater to investigate their effect on the corrosion behavior of carbon steel. Nondestructive electrochemical techniques were performed to study the corrosion behavior and field emission electron microscope images and energy dispersive x‐ray analysis (EDS) were also utilized to characterize the corrosion products. The results confirmed the amounts of oxygen and iron presented of FeO as the main corrosion product in abiotic conditions. Corrosion products and electrochemical properties of steel have been influenced by microbial activity. The variation of the open circuit potentials (OCP) and Electrochemical Impedance Spectroscopy (EIS) demonstrated that SRB and APB could not only significantly harm an alloy, but also could alter its electrochemical behavior from uniform to local damage. The outcomes proved that biofilm formation and bacterial activity can cause serious degradation.

Prediction of the corrosion depth of oil well cement corroded by carbon dioxide using GA-BP neural network

A corrosion prediction model was established based on the genetic algorithm (GA) and back propagation (BP) neural network to predict the long-term corrosion changes of oil well cement, Considering that the cement sheath is susceptible to corrosion and its corrosion degree is not easy to observe in acid gas wells and geological storage wells containing carbon dioxide (CO2). The initial weights and thresholds of the neural network were optimized by GA. The number of hidden layer nodes was selected by error verification, and the network was trained with an improved algorithm. The sample data was regression processed based on the empirical formula and was used in the network training. The simulation results shows that: The improved GA-BP network model (3–5-6–1) has a higher prediction accuracy with faster convergence and better fitting effect compared with the traditional BP neural network and the regression model (REG) in long-term prediction of corrosion depth in oil well cement. The regression coefficient (R2) of the prediction model is 0.9913, and the mean square error (MSE) of test samples is 0.0026. The modeling idea proposed in this paper can be applied to improve the accuracy of prediction models in predicting the corrosion of oil well cement.

A Bayesian approach to assess under-deposit corrosion in oil and gas pipelines

Under-deposit corrosion (UDC) and microbiologically influenced corrosion under deposits (UD-MIC) have increasingly been identified as severe forms of localized corrosion threatening the integrity of pipelines. This work utilizes a knowledge-based, semi-quantitative Bayesian approach to capture UDC and UD-MIC susceptibility and severity. This article proposed a Bayesian Network framework to study susceptibility to UDC and UDC corrosion rate. The effective corrosion rate is introduced as a measure to combine the susceptibility and corrosion rate. This measure could identify high-risk locations by assessing the probable corrosion rate while highlighting the pipeline's vulnerability to deposit settlement. Four case studies of pipeline failures due to UDC illustrate the framework's validity. A case study for a sweet gas pipeline is adapted to explore the model's robustness in assessing cases with low probabilities of UDC occurrence. The gas pipeline data, the corrosion key performance indicators spanning six years, general information on the pipeline, and the Bayesian network are made publicly available through a repository.

Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Managing the oil and gas pipelines against corrosion is one of the major challenges of the oil and gas sector because of the complexities associated with the initiation, stabilization, and growth of the corrosion defects. The present research attempts to develop a model for predicting the maximum depth of pitting corrosion in oil and gas pipelines using SVM algorithm. In order to improve the SVM performance, Hybrid PSO and GA was utilized. Monte Carlo simulation was used to determine the time lapse for the pit depth growth. In order to implement the above modeling approaches and to prove their efficiency and accuracy against a large database, a total of 340 data samples for corrosion depth and rate are retrieved from the Iranian Oilfields. The performance of the new algorithm shows that it has higher stability and accuracy. In addition, the forecasting results of the new algorithm are compared with the 11 intelligent optimization algorithms, it shows that the novel hybrid algorithm has higher accuracy, better generalization ability, and stronger robustness. The coefficient of determination (R2) value in the testing phase for SVM-HGAPSO was estimated by 0.99. Proposed hybrid model and Monte-Carlo simulations pitting corrosion based on Poisson square wave process have been used to predict the time evolution of the mean value of the pit depth distribution for different categories of maximum pitting rates (low, moderate, high and sever). The models was validated with 4 field data for each of the pitting corrosion categories and the results agreed well. The pipelines under severe pitting corrosion rate were, more conservatively predicted by HGAPSO-SVR than those under low, moderate and high pitting corrosion rates. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.

A novel dynamic parameter method (DPM) based on ANN for safety assessment of corroded pipelines

Corrosion in oil and gas pipelines is inevitable, which seriously affects the safety of pipelines. The safety assessment of corroded pipelines is a matter of urgency. To describe the damage behavior of pipelines, the Chen-Chu criterion was proposed. However, a key parameter in Chen-Chu criterion was determined empirically. In this paper, a novel dynamic parameter method (DPM) is proposed to determine the key parameter based on artificial neural network (ANN). The empirical parameter in Chen-Chu criterion is optimized by ANN and turns into dynamic one varying with input. The data for training in ANN is obtained by double circular arc (DCA) model. Compared to finite element method and previous prediction equations, the DPM are closer to the experimental data. Besides, by comparing with the experimental data, it is found that DPM is even more accurate than direct ANN method. It provides a new idea for the fusion of solution of traditional methods and data-driven methods.

Study on the Galvanic Corrosion between 13Cr Alloy Tubing and Downhole Tools of 9Cr and P110: Experimental Investigation and Numerical Simulation

The galvanic corrosion of oil and gas production string is related to wellbore integrity and production safety. In order to study the galvanic corrosion of an oil and gas well tubing assembly and maintain production safety, this paper studied the galvanic corrosion behavior between 13Cr alloy steel tubing and the downhole tools of 9Cr and P110 in formate annular fluid via experimental and numerical simulation methods. The chemical composition, HTHP corrosion tests and electrochemical measurement of the three materials were investigated to analyze the corrosion mechanism and electrochemical parameters. Then, a full-sized 3D galvanic corrosion model of 13Cr tubing and a 9Cr/P110 joint combination was established using COMSOL Multiphysics software based on the electrochemical test results. The mechanism and current variation law of the galvanic corrosion of different tubing materials are discussed and analyzed in the paper. The results revealed that the corrosion rates obtained based on the electrochemical test are as follows: P110 (0.072 mm/y) > 9Cr (0.033 mm/y) > 13Cr (0.0022 mm/y). The current densities of a combination of 13Cr tubing with a 9Cr joint and 13Cr tubing with a P110 joint vary dramatically: the current density of the 13Cr tubing–P110 joint reach 1.6 × 10−4 A/cm2, higher than the current density of the combination of 13Cr tubing and a 9Cr joint. The results of a 3D FEM analysis show that the 13Cr tube demonstrates obvious galvanic corrosion with 9Cr and P110 joints, which is consistent with the analysis results of the polarization curve. This study therefore explains the galvanic corrosion mechanism of different tubing materials and provides guidance for the safe use of tubing in the productive process.

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Green synthesis and metal oxide composites have attracted much attention from researchers of industry and academia. As a typical application of green synthesis and metal oxide composites, the continuous change of industrial technology and the continuous improvement of the social and economic level, the demand for oil and gas are also increasing. However, the spatial gap between the place of origin and the place of demand for oil and gas resources is large, so the long-distance oil and gas pipeline came into being. However, under the action of time, coupled with the corrosion effect of the soil due to deep burial, some pipelines have serious aging and corrosion phenomena. Therefore, in order to give corresponding guarantees for economic development, we need to conduct in-depth research and analysis of the corrosion of oil and gas long-distance pipelines and give effective solutions. In this paper, the corrosion rate prediction of buried oil and gas pipelines is studied in Changqing gas field. By improving the inertial weights and learning factors of the traditional particle swarm algorithm, the parameters of the generalized regression neural network are optimized and selected, and the corrosion rate prediction model of buried pipelines is finally constructed. Comparative analysis with other swarm intelligence algorithms shows that the improved particle swarm algorithm has stronger convergence ability and higher prediction accuracy than the BP model and SVM model. In addition, based on the detection data collected at the site of the gathering and transportation pipeline in Changqing gas field, this paper uses the extreme value distribution theory and the local corrosion progress formula to establish a prediction model for the residual life of corrosion of buried pipelines. The model established in this paper can effectively determine the risk pipe segment of buried pipeline and provide a decision-making basis for pipeline management departments. The work provides an important application guidance to green synthesis and metal oxide composites.

Study on long -term influence of oil corrosion on asphalt adhesion properties and the mechanism

In order to study the long-term effects of leaking oil on the adhesion properties of asphalt pavements, some tests were conducted in this paper on asphalt eroded by leaking engine oil of different time duration. The effects and mechanism were investigated by physical property tests, scanning electron microscope (SEM) tests, infrared spectroscopy (FT-IR) tests and gel permeation chromatography (GPC) tests. The physical property tests show that the adhesion capacity of asphalt binder corroded by engine oil is significantly reduced; the change rate gradually slows down with the extension of corrosion time; the viscosity at the same temperature declines, and the sensitivity of viscosity change with temperature is reduced. Microscopic tests show that the location of characteristic peaks in asphalt after oil corrosion does not change much, but the intensity changes significantly; the process of asphalt subjected to oil corrosion is a physical dissolution process; leaking oil corrosion will form cracks on asphalt binders, resulting in the increase of small molecules and the loss of large molecules. Finally, Grey Relational Analysis is used in the paper to correlate the viscosity of asphalt with the functional groups and molecular weight of asphalt and their distribution before and after oil corrosion, and it is found that oil corrosion mainly affects the asphalt corrosion performance by changing the Weight average molar mass (Mw), Small molecular size (SMS) and CC of asphalt.

Investigation of the Pipeline Coating Properties Degradation to Increase the Energy Efficiency of the Cathodic Protection Stations

In this paper, it is proposed to investigate the influence of the parameter providing the rate of pipeline coating degradation on the protective length of cathodic protection systems (CPS). Comparison of the calculated values of the pipeline coating resistance with the predicted values is carried out according to the theoretical distribution of the pipeline coating resistance in time. The difference between the practical value of the parameter providing the rate of coating degradation of the considered pipeline section from the predicted one is determined. The dependence of the parameter providing the rate of pipeline coating degradation on the protective length of the CPS has been formed, which makes it possible to reduce the number of cathodic protection stations along the pipeline path at the design stage, and to diminish their energy consumption in the electrochemical protection system from corrosion of oil and gas pipelines.

Predictive deep learning for pitting corrosion modeling in buried transmission pipelines

Despite significant efforts and investments in the renewable energy sector, fossil fuels continue to provide the majority of the world's energy supply. Transmission pipelines, which are extensively used in the oil and gas industry, are vulnerable to various failure mechanisms, such as corrosion. Among these, pitting corrosion in offshore pipelines is the most prevalent type of external corrosion. This study explores the potential of deep learning models (Generalization and Generalization-Memorization models) to predict the maximum depth of pitting corrosion in oil and gas pipelines. The models are trained considering various characteristics of the soil where the pipe is buried and different types of the protective coating of the pipes. The application of deep neural networks resulted in a mean squared error of prediction of 0.0055 in training data and 0.0037 in test data. These results demonstrate that deep learning models outperform all empirical and hybrid models applied in previous studies on the same dataset. The proposed model in this study has the potential to predict failure rates of the pipelines due to external corrosion and enhance the safety and reliability of these facilities.

MODIFIED POLYALKENYL SUCCINIMIDE ADDITIVE FOR MOTOR OILS

The article is devoted to the synthesis of a chemically modified version of one of the most important additives for the prevention of low-temperature precipitation in engine oils - polyalkenyl succinimide. This additive is prepared by alkylating maleic anhydride with an α-olefin oligomer and then reacting the resulting oligoalkenyl succinic anhydride with various amines. A new compound, an oligomer of hexene-1 with dicyclopentadiene, was used as an alkylating agent, which makes it possible to expand the raw material reserves of oligomeric compounds used in the synthesis of polymer additives, as well as to impart anticorrosive properties to the additive by introducing dicyclopentadiene rings. The composition and structure of the obtained compounds were studied by appropriate physicochemical research methods. The content of 1.5% of the obtained additive in M-11 oil completely eliminates oil corrosion and improves detergent properties. And this allows you to create a lubricant composition with a simpler composition and, as a result, gives an economic effect. The sample of the new additive complies with the requirements for the industrially produced C-5A additive and, in addition to an increased oil viscosity index; it also has anti-corrosion properties, which is due to the chemical structure of the raw material, i.e., the presence of a carbocyclic dicyclopentadiene fragment in the molecule