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

Li, Xianbing; Liu, Rui; Ma, Dong; Bai, Yang; Shi, Yukai; Anhouck, Badm

doi:https://doi.org/10.1155/2023/7296454

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Abstract Introduction Methods and Theories Results and Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

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Applications and Green Synthesis of Metal Oxide Composites

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Research Article | Open Access

Volume 2023 | Article ID 7296454 | https://doi.org/10.1155/2023/7296454

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

Xianbing Li,¹Rui Liu,¹Dong Ma,¹Yang Bai,¹Yukai Shi,²and Badm Anhouck³

Academic Editor: Rabia Rehman

Received10 Oct 2022

Revised26 Oct 2022

Accepted24 Mar 2023

Published25 Apr 2023

Abstract

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.

1. Introduction

It is important to investigate green synthesis and metal oxide composites. As a typical application of green synthesis and metal oxide composite materials, mechanical damage such as scratches, scratches, pits, and mussel eyes of the pipeline itself are internal factors that affect its service life, while soil corrosion, bacterial corrosion, and stray current corrosion in primary batteries are external factors that affect its service life. In order to inhibit the corrosion of the outer wall of the pipeline by the surrounding environment, the long-distance pipeline generally adopts the dual protection measures of anticorrosion layer and cathodic protection [1–3]. However, due to the limited construction conditions on site, in the construction process of the pipeline, the quality of the pipeline anticorrosion coating cannot be guaranteed [4–7]. At the same time, with the increase of the service life, the anticorrosion coating is aging, cracking, and even peeling, and the soil corrosive medium is immersed, thus providing conditions for the occurrence and development of corrosion, and the possibility of corrosion of the pipeline is greatly increased, increasing the unsafe operation of the pipeline. In addition, due to the poor management of pipeline operation, the pipe body bears too much load, so that the pipeline is “tired into a disease” for a long time, which increases the danger of pipeline operation [8–11]. Therefore, with the increase in the service life of buried long-distance pipelines, the possibility of accidents such as perforation, leakage, and cracking has also increased, causing huge economic losses to the production and operation management departments and bringing very serious impacts on social life and the ecological environment. Therefore, it is necessary to predict the remaining corrosion life of the pipeline, that is, to determine the remaining corrosion life of the pipeline in advance, so as to formulate a reasonable inspection and maintenance cycle in advance, to prevent accidents and avoid unnecessary waste caused by premature replacement of the pipeline, and to reduce the probability of pipeline corrosion perforation leakage accidents and the resulting economic and resource losses.

The remaining life of the pipeline can be divided into three aspects, namely, corrosion life, subcritical crack propagation life, and damage life. Among them, in addition to the subcritical crack propagation life (especially fatigue crack propagation life) is easier to predict, the study of corrosion life and damage life is far from mature, and foreign countries have not formed standards in this regard [12–15]. The research idea is to obtain the statistical results of corrosion rate through the statistical analysis of the on-site corrosion number of a large number of pipelines, or to obtain the calculation of corrosion rate through a large number of corrosion data inside and outside the pipe, and to generalize to general pipelines. There is no literature on the remaining life prediction study considering the efficiency of antiseptic layers, cathodic protection, and corrosion inhibitor protection [16–19]. The former Soviet Union has the longest pipeline system and the largest pipe diameter in the world, and their pipeline design life requirement is about 35 years. According to statistics, the main cause of the accident is corrosion, accounting for 33%–68% of the total number of accidents, so the study of corrosion life is very important. However, the main load of the pipeline is the internal pressure. In the pumping station, the internal pressure of the pipeline fluctuates only in a small range and will not alternate, so fatigue is not the main problem [20–23].

According to the analysis of more than 1500 abstracts of domestic oil and gas industry corrosion research literature retrieved on China Petroleum Abstracts, it shows that between 1985 and 1992, there were less than 20 research literatures on corrosion life prediction, and most of them studied the corrosion fatigue life and the protective life of corrosion inhibitors and coatings, etc. Only two articles have been reported on the basic corrosion rate of corrosion residual life prediction research [24–27]. Although these data do not fully and accurately reflect the status of domestic corrosion residual life research, it can be seen that domestic research in this area is indeed lacking. In recent years, with the advent of the international research climax of operational adaptability (FFS) including residual strength, residual life, and defect detection, the study of residual life of corrosion has begun to be involved, but in general, it is only in the initial stage of exploration. At present, the corrosion life prediction methods are all based on historical inspection data and reasonable mathematical models. This prediction has good reliability in a short time. With the extension of prediction time, the reliability of prediction is not high.

The external corrosion detection technologies of pipelines at home and abroad are mainly divided into two categories: ① the detection of the protection effect of the external coating of pipelines, including the current attenuation method (PCM), current gradient detection method (ACVG), and DC potential gradient method (DCVG) in multi frequency tubes; ② the detection of the cathodic protection effect is mainly CIPS.

Most of the oil and gas pipelines in service in Changqing oil field were constructed in the 1970s, and their service life is close to or more than the initial design life. There are many factors affecting the corrosion of oil and gas pipelines, and the factors also affect each other, which objectively increases the difficulty of predicting the remaining life of oil and gas pipelines due to corrosion. The corrosion rate plays a decisive role in predicting the remaining corrosion life of oil and gas pipelines. However, if the traditional probability statistics calculation method is used, the prediction accuracy error will be large due to the insufficient number of data samples. In this paper, by introducing the particle swarm algorithm and generalized regression neural network concept and algorithm flow, this paper improves the particle swarm algorithm, constructs the PSO optimized GRNN buried pipeline corrosion rate prediction model, combines the buried pipeline corrosion data, takes the rough set as the feature engineering before the model input, and then trains and learns the prediction model, completes the corrosion rate prediction of buried pipeline, and compares and analyzes the model performance. In addition, the corrosion depth of a corrosion defect area of the collection and transportation pipeline is analyzed and processed by probabilistic statistical method, and the maximum corrosion depth of the pipeline is obtained by the extreme value distribution method, and the remaining service life of the pipeline is calculated by combining the empirical formula of local corrosion progress.

2. Methods and Theories

2.1. Establishment of Corrosion Rate Prediction Model Based on Green Synthesis

Inspired by RBF network, Specht proposed a new algorithm–general regression neural network–(GRNN) in 1991. GRNN is a prefeedback neural network based on nonlinear regression theory, which is an important branch of RBF. GRNN has many advantages, such as fast training speed, good global convergence, and few adjusting parameters. In order to simplify the pipeline sample data and reduce the corrosion characteristic dimension, the attribute reduction in RS theory is first used to select the corrosion factor, and then, the reduced sample data are used as the input of GRNN, and the improved PSO is used to optimize the selection of network parameters to construct the corrosion rate prediction model of buried pipelines. Considering the problem of parameters to be optimized in GRNN, particle swarm optimization (PSO) is introduced to automatically optimize the smoothing factor of GRNN. As a classical intelligent optimization algorithm, PSO was proposed by James Kennedy and Russell Eberhart in 1995. The basic PSO is inspired by birds’ foraging behavior. If there is a D dimension search space, there are n particles to form a population.

2.1.1. Modeling Steps

The basic steps of the buried pipeline corrosion prediction model based on RS-PSO-GRNN are as follows: Step 1: Construct an index system, collect, and sort out the original data set of buried pipelines according to the actual engineering background, sort out and summarize the corrosion factors of pipelines, and determine the original sample set Step 2: Discrete the data and establish a decision table, including the conditional attribute set and the decision attribute set, the conditional attribute set is the selected feature index, and the decision attribute set is the obtained pipeline corrosion rate Step 3: Under the condition of maintaining the unidentifiable relationship between conditional attributes and decision attributes, use attribute reduction to delete redundant (conflict) attributes in the decision table to obtain the core indicator attribute set Step 4: In view of the inconsistency of the dimensions of each indicator, normalize the core indicator data set, and take turns to divide the training sample set and the test sample set Step 5: Take the training sample set as the input to the model, select the appropriate fitness function, use the modified PSO to find the best smoothing factor b, and determine the optimal RS-PSO-GRNN model Step 6: Input the test samples into the learned RS-PSO-GRNN model, obtain the network output prediction results, compare the analysis results, and verify the model performance

2.1.2. Data Standardization

Data standardization refers to the scaling of the attribute value of the sample to a specific interval, and the reasons for data standardization are as follows: first, the difference of orders of magnitude will lead to a huge advantage in the attributes of the magnitude; second, the difference of orders of magnitude will seriously reduce the iterative convergence speed of the algorithm; third, algorithms involving sample distances are particularly sensitive to the magnitude of the data. There are two common ways to standardize data: min-max normalization and z-score normalization. This paper uses root mean square error (RMSE) as the fitness function of PSO. RMSE can be used as a standard to measure the true error between the predicted value and the true value and is sensitive to predictions with large errors, and if the predicted value is far away from the true value, the value of the RMSE will increase sharply.

Prediction of pipeline corrosion is based on the actual corrosion of past and present pipelines to speculate on future pipeline corrosion trends, and part of the sample set during the sample period is used to build a predictive model to reproduce the situation of the sample period by simulating historical data, which is called historical simulation. The use of prediction models for extrapolated predictions is generally divided into two categories: preprediction and postevent prediction. Postevent projections are only based on predictions made for certain periods that have occurred during the sample period. Advance prediction refers to the prediction of future situations that have not yet occurred. In general, the performance of model predictions should be measured by the results of prior predictions, but the future situation is unknown and cannot be evaluated. In fact, the corrosion mechanism of pipeline is relatively complex, but the development and expansion of pipeline corrosion defect size are a monotonous increasing process, and in a relatively short time, the corrosion development rate is relatively stable, and the corrosion development changes slowly. Therefore, it is assumed that the development trend of pipeline corrosion will increase exponentially over a period of time.

Therefore, in the actual forecasting work, the two methods of fit test and extrapolation test are generally used for evaluation. (1) Fitting test refers to the ability to estimate the prior prediction error of the fit of historical data through the model and mainly to reproduce the ability of the model to fit historical data. The fit test generally uses the following two indicators: mean squared error and relative mean error. The extrapolation test is a test that compares the postprediction result with the actual value and generally uses the following error indicators to reflect it, generally including extrapolation test, Hill unequal coefficient, and mean absolute percentage error (MAPE). MAPE can be used to measure the predictive power of different models. Table 1 is an evaluation explanation of the prediction accuracy of MAPE.

2.2. Buried Corrosion Pipeline Remaining Life Prediction Method: Extreme Value Statistical Method Based on Green Synthesis

The remaining life of buried pipelines has strong randomness and uncertainty, and for the elderly buried pipelines with low working pressure, corrosion perforation is the most important cause of pipeline failure. Therefore, the maximum corrosion pit depth of the buried pipeline determines the service time of the pipeline, and as the corrosion pit depth deepens, when the depth exceeds the minimum wall thickness allowed for the safe operation of the pipeline, the pipeline will be in danger of corrosion perforation. The maximum allowable corrosion depth of the pipeline is determined according to the manufacturing process and corrosion parameters of the pipeline and can be determined according to ASME B31G standard. When the operating pressure value of corroded pipeline reaches the failure pressure value, the corrosion defect depth is the maximum allowable corrosion depth. Erosion has a probabilistic characteristic, and the maximum value of these local pore erosion depths follows the Gumble extreme value distribution. Extreme value type I, also known as the Gubi distribution, includes two types: “minimum extreme value type I asymptotic distribution” and “maximum extreme value I type asymptotic distribution,” which are used to study the phenomenon of the maximum or minimum value of a variable. It is widely used in the analysis and evaluation of many engineering practical problems.

Maximum corrosion depth prediction of the Gubi distribution:where F (x) is the probability that the depth is less than x; X is random variable for maximum corrosion depth, mm; λ is pitting hole depth with the highest probability density, mm; α is average value of the corrosion hole depth, mm.

Because the pipeline is buried in the ground, when collecting the depth data of the pipeline corrosion pit, it is impossible to excavate the pipeline as a whole and collect the information of the corrosion pit, so the amount of engineering is huge and it is not necessary. Usually, engineering excavation of areas with severe corrosion of pipelines is carried out for sampling and data collection. The deepest corrosion depth thus obtained is highly random, and the deepest corrosion depth of multiple locations cannot be simply averaged, and the deepest corrosion depth cannot be used as a standard for life prediction. The most reasonable method is to statistically process the detected data, obtain statistical parameters λ and α, and then calculate the probability that the deepest corrosion depth does not exceed a certain value according to formula (1).

The N maximum corrosion pit depth data measured under the same conditions are arranged in sequence from smallest to largest, marked with sequence number i and the corresponding corrosion pit depth value x_i, and then, the cumulative distribution function is calculated using the averaging method:

We take both ends of formula (1) 2 times the logarithm at the same time to get

We obtain the statistical parameters λ and α, substitute them into the equation, and then get the probability of F (X < x) that the maximum corrosion depth does not exceed a certain value. If x is used as the abscissa and −ln(ln(l/F)) as the ordinate, each (x_i, −ln(ln(l/F)) is drawn in a rectangular coordinate system. If these points show a linear relationship, these decaying data follow the Gubi extreme value distribution. Then, we perform the least squares linear fitting to obtain the slope α and intercept of the fitted straight line −αλ.

The relationship between the regression period T (x) and the cumulative probability F (x) is

The regression period is the expected value of a measurement value x as the observation sequence number. That is, N samples are taken from a certain Gubi distribution, and the maximum value means the pit depth of the entire local section from the maximum pit depth of a small number of small segments of the sample.

According to formula (5), the approximate maximum corrosion pit depth for the entire section of long pipe can be obtained. We can substitute the F (x) value obtained by formulas (4) into (3) to obtain a y value. Finally, the y-value is substituted into the fitted straight line equation to find the maximum local corrosion pit depth.

The modeling idea for predicting the corrosion remaining life of buried pipelines is as follows: first, select several pipe sections, excavate and inspect these pipe sections, determine the corrosion defect parameters of weak pipe sections, and calculate the maximum allowable corrosion depth. Then, the corrosion depth detection data are obtained through the embedded film test, and the PSO-GRNN corrosion depth prediction model at different time points is built in combination with the corrosion development trend prediction method, giving the maximum corrosion depth development law of buried pipelines. Finally, the remaining service life of buried pipeline is predicted according to this rule and the maximum allowable corrosion depth.

3. Results and Discussion

3.1. Corrosion Rate Calculation Based on Green Synthesis

Due to the complexity and variety of soil properties, the corrosion status of pipelines varies with different regions. In order to fully understand the operation condition of the pipeline, the detection work of this study mainly determines the sampling area through the early data collection and the macroscopic detection of the field survey and arranges the sampling points, so as to carry out soil detection, stray current detection, and embedded film experiment at the sampling points to calculate the corrosion rate and excavate the relationship between the corrosion factors and the corrosion rate.

In order to verify the performance of the model, we selected 20 sets of buried pipeline inspection data from Changqing gas field for simulation experiments. The main factors causing corrosion of buried pipes are soil resistivity, redox potential, PH value, stray current, and sulfate ion content. We use the 4-fold cross-verification method to divide the core index data set after the reduction of rough set properties into 3 parts: training set, verification set, and test set, of which the training set contains 12 samples, the verification set is 4 samples, and the test set is also 4 samples, which are divided into the following as shown in Table 2.

To verify the improved algorithmic performance of the PSO, we compare the standard PSO with a learning factor set to c₁ = c₂ = 1. We use the same value for the rest parameters: the number of populations, the maximum number of iterations, and the particle dimension are set to 100, 200, and 1, respectively. The convergence state and minimum RMSE of each cross-verification are shown in Table 3.

Table 3 shows that although the minimum RMSE of the two methods is relatively close, in the process of iterative optimization, the results of four cross-validations show that the convergence speed of the improved PSO is significantly faster than that of the standard PSO, and the analysis results can be obtained. The average values of the convergence states of the standard PSO and the improved PSO are 93.25 and 65.5, respectively, and the average values of the smoothing factor are 0.8425 and 1.24, respectively. The convergence rate of the improved PSO is faster, the optimization ability is stronger, and the stability is better.

Four sets of test samples are brought into a trained PSO-GRNN model for prediction, and the output is denormalized. In order to analyze the performance of the model, the error backpropagation (BP) neural network model and the rough set fusion support vector machine (RS-SVM) model are compared, and the prediction value and the actual value comparison result are shown in Figure 1, and for a more objective evaluation of the performance of the prediction model, the relative error RE is used as the evaluation criterion, as shown in Figure 2.

Although the three models can correctly predict the corrosion defect level of buried pipes, from the overall point of view, whether it is relative error, mean squared error, average absolute percentage error or residual comparison, RS-PSO-GRNN prediction accuracy is the highest, and from the MAPE prediction index evaluation, BP model and RS-SVM model have good results, while RS-PSO-GRNN achieves higher accuracy; therefore, RS-PSO-GRNN model prediction effect is better. The accuracy of the model design scheme is verified, and the evaluation results are more reliable. In summary, it can be seen that the model built in this paper has stronger robustness and better prediction performance, which provides reference significance and decision-making basis for relevant maintenance personnel to learn and grasp the corrosion development status of pipelines (Figures 3 and 4).

3.2. Prediction of the Remaining Life of Buried Corrosion Pipelines Based on Green Synthesis

Detect the soil environment in the area where the pipe section is easy to corrode, and obtain the test data of external corrosion factors. Through experimental analysis, the main corrosion factors have been determined, the external corrosion index system has been established, and the buried sheet test has been carried out for the soil areas along the pipeline section prone to corrosion. According to the embedding time, samples are taken in three batches of 0.5, 1, and 1.5 years in order to test the maximum corrosion depth of embedded films at each test point at different times.

As of 2010, Changqing oil field has built nearly 1700 km of oil transmission trunk lines and 10728 km of internal gathering and transmission pipelines. Most of its oil and gas pipelines are composed of metal pipelines and equipment and are buried underground. The oil and gas pipelines are corroded due to the contact between the pipeline and various surrounding media. The soil along the line is mostly saline soil, the soil resistivity is low, generally in 20–40 ohms/m, and the soil corrosivity is medium. In this paper, after the excavation of a serious corrosion area is detected by the external anticorrosion layer, the remaining life prediction calculation is performed. The local corrosion pit data measured after excavation are recorded as shown in Figure 5.

T (x) = 200 is obtained from formula (5), and then, F (x) is obtained as 0.995. Bringing the value of F (x) into equation (3) of the fitting equation yields a maximum local corrosion pit depth of 5.69 mm. Combined with the formula recommended by APIRP579, the critical corrosion depth of the corrosion pipeline is calculated to be 8.89 mm, and then, the local corrosion progression formula in the form of curtain index is used to calculate the local uniform corrosion of the pipeline to obtain a power law coefficient of 1.852. By taking the critical corrosion depth of 5.69 mm into the power law calculation formula, it can be found that the remaining life of the corrosion of the pipeline is t = 23.04 − 9.44 = 13.6 years (Figure 6).

Due to the constant and changing corrosion of pipelines, on the one hand, repair and maintenance plans should be developed for these pipelines. On the other hand, it is also necessary to determine the future detection time of the defective pipeline based on the prediction results, so as to continuously use new information to optimize the model performance and correct the previous prediction conclusions. Therefore, the corrosion development status of the pipeline can be grasped timely and accurately, and the deficiency that the model is only suitable for medium and short-term corrosion prediction can be avoided.

4. Conclusion

Aiming at the corrosion problem of long-distance oil and gas pipelines, this paper studies the prediction of corrosion rate of buried oil and gas pipelines in Changqing gas field. By improving the inertia weight and learning factor of the traditional particle swarm optimization algorithm, the parameters of the generalized regression neural network are optimized, and the corrosion rate prediction model of buried pipelines is finally constructed. The specific results are as follows:(1)This paper proposes an improved PSO optimization GRNN buried pipeline corrosion rate prediction method. This study establishes the network mapping model between the key influencing factors of buried pipeline corrosion and corrosion rate, combines the buried piece test data and pipeline detection data, and evaluates the prediction performance of the model. The results show that the model can accurately predict and evaluate the corrosion status of buried pipelines with good results, compared with other prediction models. It has relative superiority and also lays a scientific and reliable foundation for the study of the remaining life of pipeline corrosion in the fifth chapter.(2)The extreme value distribution prediction method mainly analyzes the maximum corrosion depth in the local corrosion area through the extreme value distribution and uses the local progress empirical formula to determine the remaining life of the pipeline, which is suitable for the uniform corrosion of the pipeline for life prediction and can be applied in the Changqing gas field.

The method selected in this paper has opened up a new way for corrosion residual life prediction of buried pipelines, and has certain reference value, but some aspects still need to be further improved and perfected. For example, the positive impact of the anticorrosion coating and cathodic protection on the pipeline anticorrosion shall be considered during the embedded film test. In addition, although oil and gas will be treated by desulfurization, dehydration, and corrosion inhibitor before transmission, weak internal corrosion will still occur, which is also the future research direction.

Data Availability

The data supporting the current study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors would like to show sincere thanks to those techniques who have contributed to this research.

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Copyright © 2023 Xianbing Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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