Pipeline Corrosion Research Articles

Machine learning-assisted intelligent interpretation of distributed fiber optic sensor data for automated monitoring of pipeline corrosion

Distributed fiber optic sensor (DFOS) offers unique capabilities of monitoring corrosion for long pipelines. However, manually interpreting DFOS data is labor-intensive and time-consuming. To address this challenge, this paper presents a machine learning approach for real-time automatic interpretation of DFOS data used to monitor both uniform and non-uniform corrosion in pipeline. A machine learning model is developed to automatically detect corrosion based on DFOS data, and a corrosion quantification method is developed based on the output of the machine learning model. The proposed approaches are evaluated using laboratory experiments in terms of accuracy and robustness to pipeline diameter, spatial resolution of DFOS, type of fiber optic cable, and sensor installation methods. The results show that the F1 score for corrosion detection and the R2 value for corrosion quantification are 0.986 and 0.953, respectively. This research will facilitate pipeline corrosion monitoring by enabling automatic distributed sensor data interpretation.

Automatic detection system with efficient and accurate sample preparation for cobalt ion concentration in zinc hydrometallurgy

Cobalt ion is an impurity that has a negative effect in zinc hydrometallurgy, resulting in lower zinc yield and quality. Currently, the zinc hydrometallurgy industry typically uses manual sampling followed by manual sample preparation, and single-wavelength spectral analysis to detect cobalt ions in zinc solution, which is inefficient and lacks accuracy. Therefore, a new automatic detection system for cobalt ions concentration is developed. The system effectively overcomes challenges of pipeline blockage and corrosion by innovatively implementing negative pressure extraction, volume quantification, and split pipeline structure. Compared with manual detection, the system has higher detection repeatability and efficiency, specifically reducing reagent consumption by 70 %, and improving the detection speed by 8 times. In addition, to solve the spectral drift and noise problems, a spectral preprocessing algorithm by forced contraction of long-wavelength region spectra and Gaussian filtering is proposed. This algorithm not only improves the local linearity and regression accuracy of the spectrum, but also improves the detection repeatability of the system. Finally, five full-wavelength modeling methods are applied to the system. The best root mean square error for low concentration (0.1 to 1 mg/L) and high concentration (1 to 20 mg/L) cobalt zinc solution is only 0.0027 mg/L and 0.071 mg/L, respectively.

Two-Phase Flow Pattern Identification in Vertical Pipes Using Transformer Neural Networks

The oil and gas industry consistently embraces innovative technologies due to the significant expenses associated with hydrocarbon transportation, pipeline corrosion issues, and the necessity to gain a deeper understanding of two-phase flow characteristics. These solutions involve the implementation of predictive models utilizing neural networks. In this research paper, a comprehensive database comprising 4864 data points, encompassing information pertaining to oil–water two-phase flow properties within vertical pipelines, was meticulously curated. Subsequently, an encoder-only type transformer neural network (TNN) was employed to identify two-phase flow patterns. Various configurations for the TNN model were proposed, involving parameter adjustments such as the number of attention heads, activation function, dropout rate, and learning rate, with the aim of selecting the configuration yielding optimal outcomes. Following the training of the network, predictions were generated using a reserved dataset, thus facilitating the creation of flow maps depicting the patterns anticipated by the model. The developed TNN model successfully predicted 9 out of the 10 flow patterns present in the database, achieving a peak accuracy of 53.07%. Furthermore, the various predicted flow patterns exhibited an average precision of 63.21% and an average accuracy of 86.51%.

Comparative study of a low phosphorous co‐polymer inhibitor with commercial scale inhibitors used in cooling water systems

AbstractTo reduce the economic loss caused by scale formation and corrosion of equipment and pipelines, a low phosphorus polymer scale inhibitor is synthesized and characterized, the ability of the polymer to mitigate the calcium scale and the influences of operating conditions on CaCO3 scale are investigated by static tests. X‐ray diffraction (XRD) and scanning electron microscope (SEM) studies are performed to understand the morphological changes of the scales in the presence of the polymer. The interaction between polymer molecules and calcite crystal surface is studied by molecular dynamics simulation. The synthesized polymer is compared with the commercial product such as hydrolysis polymaleic acid (HPMA), polyacrylic acid (PAA), and 1‐hydroxy ethylidene‐1,1‐diphosphonic acid (HEDP). The experimental results show that the polymer shows better inhibition of calcium scale than the commercial product. The scale inhibition efficiency of the polymer is 92% at 10 mg/L for CaCO3 scale, 83% at 14 mg/L for Ca3(PO4)2 scale, and 98% at 6 mg/L for CaSO4 scale. SEM and XRD analysis show that the polymer induces the conversion of CaCO3 from calcite to arachite and crystal. The interaction of the polymer with both 104 and 1–10 calcite crystal surfaces are more stable than that of commercial product. The experimental results are in good agreement with those of molecular dynamics simulation.

Investigating the Influence of Environmental Factors on Corrosion in Pipelines Using Geospatial Modeling

This study integrated geographic information system (GIS) and remote sensing technology to identify areas around pipelines that are more susceptible to corrosion having the Kurdistan pipeline as a case study. Geospatial data are used to target factors such as rainfall, temperature, rivers, and minerals which increase the corrosion rate. Spatial data such as, the direction of slope, rainfall, proximity to rivers, and minerals, were collected and analyzed; maps were created for every individual factor to visualize their distribution. By overlaying these maps, regions that are at higher risk of corrosion were identified, which can be prioritized for further investigation or preventive measures. This paper’s findings are significant for oil and gas industries, including pipeline operators and designers as corrosion can lead to devastating consequences. The novelty of this study is to identify areas along the pipeline at higher risk of corrosion through the application of geospatial information systems and remote sensing. This methodology holds immense potential for industries looking to proactively prevent corrosion through the implementation of preventative maintenance, monitoring programs, and the application of protective coatings and inhibitors. The results of this research demonstrate that environmental data, GIS, and remote sensing can predict corrosion in oil pipelines, offering valuable insights for better managing corrosion risk.

Appraisement of groundwater quality status within Enugu, Nigeria, using physicochemical evaluation and water quality indices

Enugu state is notorious for potable water scarcity which forces residents to depend on polluted water sources. In this study, an appraisement of the quality status of groundwater from some areas within Enugu was conducted. Physicochemical characteristics of groundwater sampled from 15 boreholes that supply domestic water to Aninri, Awgu, Ezeagu, Udi and Oji River Areas of Enugu, Nigeria, were determined. A total of 45 water samples were obtained across both rainy and dry seasons. Standard methods were used to determine the physicochemical parameters and trace metals. The physicochemical results showed values within the WHO safe limits, except sulphate (1670 ± 21.5 mg L−1) in Mpu water samples, and high chloride levels in Ndeaboh and Mpu (18088 ± 29.4 and 1095 ± 23.9 mg L−1 for dry and rainy seasons, respectively). The pH values were predominantly slightly acidic, ranging from 5 to 8.2. Groundwater within Ukaete, Ndeaboh, Oduma and Amankpunato had very high levels of electrical conductivity (2897.80 ± 173.38, 31000 ± 45.9, 1672 ± 9.3 and 1222 ± 8.1) in the dry season, respectively; and 1665.06 ± 326.6, 5410 ± 2.4, 4860 ± 9.4, 1872 ± 19.3 in the rainy season, respectively. The higher chloride values observed, possibly due to a geogenic origin, could also be responsible for the higher values of the electrical conductivity. There were traces of copper and lead in the samples during the dry season; however, lead was not detected in most borehole water samples during the wet season. Fe was the only metal that recorded values above the WHO standard. Traces of copper in the samples may have resulted from corrosion in the copper pipelines, reservoirs or other metallic fittings used in the construction of the boreholes. The water quality index (WQI) of the borehole water examined was above 100 in all except Ogugu Awgu samples, indicating that borehole water was generally not potable and therefore unsuitable for domestic application or drinking. The observed results revealed that the groundwaters from the study area were contaminated with chloride and lead, causing the water parameters to be above standard limits. Lead at any concentration is deemed toxic according to USEPA. There is a need to treat borehole water supplies in the area before its application for domestic use.

Characterization of soil properties influence to the corrosion of the underground pipeline

Underground pipes are commonly used as transportation system that carries water, gasoline and oil which is a safe and faster way. However, the underground pipes undergo corrosion although made with primary protection such as galvanisation. The purpose of this research is to determine the factors that affect soil characteristics and lead to underground pipe corrosion. To determine soil contamination is present in the soil sample, the primary soil characteristics that are investigated in this study are particle size, moisture content, pH level, and chemical ion content. The characteristics of the soil will be determined as the factor influencing the corrosion of the underground pipes based on the information gathered and the results of previous research. The environment that a material is exposed to determines how it will behave when it corrodes, and the material exposed to an environment determines how corrosive it is. Increase in soil water content has a direct impact on the interactions between pipes, resulting in both localized and generalized corrosion, which deteriorates pipeline materials and damages infrastructure. The finer soil particles, owing to the increase in swelling, shrinkage, and plasticity are considered as corrosive medium for underground pipelines and steel structures. Concentrations of clay minerals also the main source of quality control. Clay minerals with illite and montmorillonite content absorb water at a higher rate than clay minerals with kaolinite content, which makes them very efficient reducing metals. When soil is more acidic, structural materials like steel are far more likely to corrode. When soil acidity reaches a particular point, it will accelerate the chemical process of corrosion concurrently. To improve the soil qualities, 4% of dolomite lime is applied as a stabiliser. These dolomite lime lowers soil moisture content and shows that the pH of the soil rises from 4.69 to 6.1.

Equilibrium and kinetic studies in adsorption of H2S using coconut shell activated carbon xerogel: Effect of mass adsorbent and temperature

The presence of acid gases like hydrogen sulfide (H2S) in natural gas, refinery gas, and coal gas necessitates their removal from the gas stream to prevent corrosion of pipelines and production equipment and to ensure safety due to the toxic nature of H2S. A study was conducted to synthesize an adsorbent that is made from coconut shell which is modified to form coconut shell-activated carbon xerogel (CSACX). The main objective is to determine the kinetic models’ equation and adsorption isotherms that are most suitable for the adsorption of hydrogen sulfide by using CSACX. Two parameters were studied which were the adsorbent mass and the gas temperature. The adsorption process could be mathematically represented using the Thomas Model, Yoon-Nelson Model, and Adam-Bohart. It is expected that the adsorption process is mathematically represented using the Thomas and Yoon-Nelson Model for both the effect of mass of adsorbent and effect of gas temperature. The kinetic models used were Pseudo-First Order and Pseudo-Second Order. Suitability of equation was determined by correlation coefficient (R2). The higher the R2, the more suitable the equation is to process. It is expected that the adsorption processes using the CSACX fit both pseudo-first and pseudo-second order equation. This means that both physisorption and chemisorption occur during the adsorption process.

Cathodic protection systems: approaches and open challenges

Cathodic Protection System (CPS) is used on a variety of structures to prevent corrosion in ship hulls, pipelines, and underground storage tanks. Transmission pipelines extend over wide distances that may extend for hundreds of kilometers, the problem of corrosion exists in pipelines transporting liquids such as oil, petroleum or water, as well as in pipelines transporting gases. Corrosion leads to leakage into the surrounding environment, causing pollution. Artificial intelligence (AI) is used in risk management to determine the possibility of corrosion growth rates, considering all the determinants and factors that contribute to corrosion, including alternating currents, soil properties, environmental conditions, and geographical features. According to research, there are two ways to enhance the performance of cathodic protection systems: one is to design cathodic protection in unconventional ways based on different algorithms and methods that show superiority over the traditional method in determining the number and location of anodes, and the other is to continuously monitor the environmental conditions surrounding the structure to be protected, which includes soil and weather. A study will be conducted to determine the most effective factors in protecting the pipelines and other factors to determine the required energy in an efficient method.

Forecast of the rate of underground corrosion of steel pipelines. Review

Forecast of the rate of underground corrosion of steel pipelines. Review

Comparison of aluminum formate and traditional aluminum coagulants in structure, hydrolysates, coagulation behavior, and its corrosion resistance advantage

Aluminum formate (AF) has been considered as a superior coagulant that can decompose under the UV light without causing equipment corrosion during water treatment. The main components of the aluminum formate coagulant are aluminum formate (C3H3AlO6), aluminum formate hydrate (C3H3AlO6·3H2O), and aluminum hydrogen formate (C3H3AlO6·2H2O2). Analysis of the hydrolyzed products revealed that the content of added formic acid during the synthesis of aluminum formate is a crucial factor influencing its coagulation effectiveness. Increasing the HCOOH/Al ratio from 1.5 to 4.0 resulted in smaller mass-to-charge ratio (m/z) and higher positive charge for AF hydrolysates, with fewer undetected aluminum species present. Hydrolysates of AF 1.5 exhibited similar characteristics to those of aluminum polychloride, while AF 3.0 demonstrated optimal glucan removal efficiency (>55 %) through physical absorption rather than chemical bond bridging as the primary coagulation mechanism. The jar test results for bovine serum albumin (BSA) coagulation removal indicated a decrease in removal efficiency as the HCOOH/Al molar ratio increased from 1.5 to 4.0, accompanied by a decrease in pH conditions from 6.35 to 7.27. The highest BSA removal efficiency (58 %) was achieved using AF 1.5 at pH 6.35, suggesting that BSA molecules were captured by aluminum formate through chemical bonds such as NO-Al-, Al2(SO4)3, unlike the physical absorption effect observed in glucan coagulation process. Furthermore, it should be noted that compared to traditional aluminum salt coagulants, aluminum formate exhibits excellent pipeline corrosion resistance properties.

Unique corrosion reinforcement mechanism of pipeline oil sludge with sulfate-reducing bacteria on X60 steel and the targeted long-term inhibition of dazomet delivery

The mutual promotion behavior of pipeline oil sludge and wild SRB on pipeline steel corrosion was investigated. The resistance probe technique was used for long-term monitoring of MIC underneath SRB oil sludge. Corrosion of pipeline steel under oil sludge was the result of multifactorial coupling. SRB biofilm enhanced the conductivity of the oil sludge that serves as an electron transfer "bridge" between the SRB and carbon steel, accelerating the extracellular electron transfer process of MIC, and hence the dissolution of the carbon steel. Delivery of dazomet (DMTT) was a long-term strategy for mitigating pipeline corrosion under SRB oil sludge.

Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model

Submarine pipelines play an important role in the oil and gas transportation. However, pipeline corrosion can cause the structural degradation of pipelines, which may lead to failure accident and environmental pollution. Pipeline degradation prediction based on monitoring data is of great significance for preventing corrosion failure. In this paper, an ensemble learning (EL) based approach integrated with multi-objective optimization is developed for pipeline corrosion degradation prediction. Firstly, the engineering theory and domain knowledge are integrated into feature engineering to improve the interpretability. Several new feature variables are constructed based on the corrosion mechanism and empirical models. Secondly, an improved multi-objective seagull optimization algorithm (IMOSOA) is proposed to optimize the hyper-parameters of EL model. Subsequently, a novel data-driven model, so-called theory-guided IMOSOA-EL is proposed for the corrosion rate prediction. And six benchmark functions are used to verify the optimization performance of IMOSOA. Then, different feature subsets are developed based on correlation analysis. A comprehensive evaluation indicator is proposed for the optimal feature subset selection. The results demonstrate that the proposed model presents superiority in prediction accuracy compared with other models. This study is significant for reliability assessment and maintenance decision-making of submarine pipelines.

Temperature Dependence of Adsorption and Effectiveness for a Pyrimidinium-Type Corrosion Inhibitor on Mild Steel

In the oil and gas industry, produced water with a high dissolved salt content is a common byproduct of hydrocarbon extraction from conventional and unconventional wells. Other than salts, corrosive gases such as CO2 are abundant in the production stream, which dissolve and acidify the solution, posing a risk of internal pipeline corrosion. To mitigate this issue, injection of corrosion inhibitors has emerged as a cost-effective approach. In various aggressive conditions, heterocyclic molecules that contain nitrogen atoms have proven to be highly effective corrosion inhibitors for many alloys. In this study, tetrahydropyrimidinium (THP-C14) inhibition efficiencies were investigated at temperatures of 25°C, 55°C, and 80°C using electrochemical methods, including linear polarization resistance and potentiodynamic sweeps. Corrosion inhibition data were then correlated with THP-C14 concentration, using the five adsorption isotherms: Langmuir, Temkin, Frumkin, Flory-Huggins, and Dhar-Flory-Huggins models. These isotherms utilize different assumptions to establish the correlation between coverage and inhibitor concentration. The suitability of these five isotherm models for describing the corrosion inhibition behavior of THP-C14 was examined. In addition, the thermodynamic parameters (Kad, ΔadGo) of adsorption for THP-C14 at 25°C, 55°C, and 80°C were calculated and compared using the aforementioned adsorption isotherm models. Finally, a mechanism was proposed for the adsorption behavior of the THP-C14 corrosion inhibitor model compound. Chloride ions were important for inhibitor adsorption.

Structural analysis of petroleum acids in highly acidic crude oil and deacidification using organic amines

Naphthenic acids, polar compounds with carboxylic group, are commonly found in crude oils as product of biodegradation. The presence of NAs in oil can cause corrosion of processing equipment and pipelines and can lead to degradation of crude oil quality. Qilu high acid crude oil was designated for research, from which the acidic fraction was separated by caustic extraction. Elemental analysis, Fourier transform infrared spectroscopy, nuclear magnetic resonance and high-resolution Orbitrap mass spectrometry were used for characterization. The structure of the acidic fraction was verified to be naphthenic acids primarily, and the average molecular formula was obtained. Based on the derivation of the modified B-L method, the average molecular structure was monocycloalkyl benzoic acid with single carboxyl substitution The effects of various depickling solvents and process conditions on deacidification results as well as product were investigated and illustrated with potentiometric titration, UV spectroscopy, zeta potential test and Karl Fischer method. Among the three groups of deacidifiers, inorganic bases represented by sodium hydroxide have the best deacidification efficiency, reaching 83.5 %. But the emulsification caused by sodium naphthenate is severe and the emulsion is stable, making it difficult to achieve oil–water separation. Polyethylene polyamines cause the least emulsification. Moreover, as the number of amino groups in the monomer rises, the number of sites that can be neutralized with petroleum acids increases, and the deacidification rate rises. As result, the most suitable deacidifier for processing is ethanolamine, which can reduce the total acid number of crude oil to 0.47 after three stages of deacidification at 70 °C and a concentration of 10 %.

Application of pipeline leakage detection based on distributed optical fiber acoustic sensor system and convolutional neural network

Underwater pipelines are exposed to harsh environments, including high salinity, multi-modal vortex corrosion, and severe wave interference. Their safety is essential for the development and transportation of marine energy. Therefore, real-time safety monitoring of long-distance energy pipelines is of great strategic importance for ensuring the safety of life and property and energy security. With the rapid development of energy development, the corrosion and leakage mechanisms of natural gas pipelines, as well as their identification and early warning, have become the focus of attention. Optical fiber sensing technology has been applied to various energy safety monitoring fields. However, the mechanism of sound source fluctuations in pipeline leakage and the mutual coupling mechanism between distributed optical fiber sensing technology and leakage sound waves are not yet clear. This paper establishes a model based on sound wave propagation and leakage noise response, derives a quadratic fitting relationship between pipeline pressure fluctuations and leakage orifices and a relationship between leakage noise source standard deviation and orifices, and proposes a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) permutation entropy underwater natural gas pipeline leakage signal recognition method based on distributed optical fiber acoustic sensing technology. The results of theoretical analysis are verified by experiments. It shows that the signal processing method of CEEMDAN permutation entropy is superior to traditional noise reduction methods, which can better preserve the features of the original signal; the radial basis function (RBF) neural network model can accurately identify four different leakage features with an accuracy of 88.15%, and its recognition stability and generalization ability are superior to convolutional neural network, backpropagation, and random forest. Therefore, the research results of this paper provide a new method for safety monitoring in the application of energy pipeline transportation engineering, and expand the potential application scenarios of distributed acoustic sensing sensor systems and RBF neural networks.

Microbial Diversity of the Produced Waters from the Oilfields in the Republic of Tatarstan (Russian Federation): Participation in Biocorrosion

A variety of microorganisms grow in oil reservoirs, where they participate in the biodegradation of oil and lead to changes in oil quality. Today, our knowledge about microbial processes in oil reservoirs is largely limited, and among the urgent tasks for the oil industry, monitoring and controlling microbial activity (including the activity of microbes responsible for biocorrosion) are important. In this work, we aimed to characterize the bacterial community structure of the produced waters from oilfields in the south of the Republic of Tatarstan (Russian Federation) using cultural and molecular methods of analysis. Bacteria from produced water samples from various oil wells were enriched, and 16S rRNA gene amplicon sequencing was used to assess the phylogenetic diversity of the bacterial communities. Thus, analysis of the bacterial communities revealed the presence of various members within the genera Desulfotomaculum, Clostridium, Acetobacterium, Desulfovibrio, Dethiosulfovibrio, Desulfomicrobium, Fusibacter, Dethiosulfatibacter, Thermovirga, Sphaerochaeta, and Desulfoplanes, but in different produced water samples. The role of the bacterial associations in biocorrosion was separately assessed in experiments on monitoring and stimulating biocorrosion. The bacterial community receiving additional nutrition was shown to have a higher corrosion potential, and scanning electron microscopy analysis confirmed changes in the surface of the metal coupons during immersion testing. The practical value of this research lies in identifying the potential contribution of microbial communities to oil pipeline corrosion.

The influence factors of dielectric barrier discharge plasma to production of syngas derived from H2S-CO2 acid gas

H2S and CO2, two harmful acid waste gases, often co-exist in important chemical production such as coal-chemical industry, natural gas chemical industry and petrochemical industry, causing corrosion of industrial equipment and pipelines, and must be treated innocuously. Co-conversion of H2S-CO2 mixed acid gas to syngas has been carried out using dielectric barrier discharge (DBD) plasma-catalysis, which renders the highly corrosive and toxic H2S and greenhouse gas CO2 harmless, and produces syngas. The effects of various parameters of the DBD plasma on the reaction of one-step conversion of H2S-CO2 to syngas were studied. Moreover, a comparative study of the different parameters of DBD plasma was carried out. The intrinsic correlation between the reaction performance of syngas production via H2S-CO2 conversion and these parameters, including specific energy input (SEI), discharge shape, discharge frequency, discharge gap and discharge length, was investigated and revealed. On this basis, a multi-tube parallel DBD plasma reaction system was designed and constructed.

Ensemble Algorithm for Simulated Corrosion Data-tentative

Abstract The application of robotics continues to advance and lends itself to various disciplines. Previous work has shown how Markov decision processes can be integrated in an algorithm to handle pipeline inspection. This has been extended to building models and addressing the errors in the model from the data collected. This paper made use of data collected by simulated robots in a pipeline environment, to build an ensemble. Five models were explored and analysed viz: Linear model, Generalised linear model, K-nearest neighbour, Neural network, and Random Forests models. The models were used to explore the responses acquired from the pipeline corrosion data. The performance of each of the ensembles are evaluated based on the Root Mean Square Error (RMSE), Adjusted R2 and Mean Absolute Error. Model evaluation show that although the averaging ensemble has a better fit (34.56%) than the stacking ensembles (17.22% and 16.98% respectively) considered, it also has the higher RMSE,(81.90) than the stacking models ( 40.91 and 38.53 respectively) forcing the decision maker to make a trade-off between fit and precision.

Studying Corrosion Failure Prediction Models and Methods for Submarine Oil and Gas Transport Pipelines

To predict the corrosion failure of carbon steel oil and gas pipelines more accurately, a new corrosion failure prediction model for submarine oil and gas transport pipelines was constructed. A corrosion failure prediction management system was also developed based on the constructed model. To construct the model, corrosion experiments were carried out to analyze the influences of temperature, partial pressure of CO2, pH value, and flow rate acting on the corrosion rate. Based on the analysis results and the corrosion experiment data, a new corrosion failure prediction model containing the time and flow rate for oil and gas pipelines was constructed. The model is based on the existing corrosion prediction model and has a determination coefficient, R2, of 0.9573, which indicates good prediction accuracy. A machine learning prediction model was also used to predict, and the prediction results are compared with that of the proposed model, which further verifies the accuracy and feasibility of the proposed model. A corrosion failure prediction management system for carbon steel oil and gas pipelines was developed based on the constructed model, which makes corrosion failure prediction more convenient and faster and provides a reference for the accurate prediction and efficient control of oil and gas pipeline corrosion failure.