鹽穴儲氣庫注采管柱內(nèi)腐蝕速率預測模型研究

駱正山，歐陽長風，王小完，張新生

鹽穴儲氣庫注采管柱內(nèi)腐蝕速率預測模型研究

駱正山，歐陽長風，王小完，張新生

（西安建筑科技大學 管理學院，西安 710055）

鹽穴儲氣庫；注采管柱；腐蝕速率預測；主成分分析法（KPCA）；改進灰狼優(yōu)化（IGWO）；極限學習機（ELM）

近年來，我國天然氣供需量穩(wěn)步增長，高質(zhì)量發(fā)展戰(zhàn)略能源儲備設施地下儲氣庫具有重大意義[1]。鹽穴儲氣庫具有孔隙率低、滲透率小、塑性形變能力強等優(yōu)勢[2-3]。注采管柱是鹽穴儲氣庫的重要組成，長期處于地下復雜環(huán)境使其易受多種腐蝕因素的影響并造成運維災害[4-5]。因此，探究鹽穴儲氣庫注采管柱的腐蝕機理與規(guī)律，建立高精度的腐蝕預測模型意義重大。

目前，國內(nèi)外學者已對管線腐蝕現(xiàn)象展開了大量研究。張新生等[6]研究了海洋立管的腐蝕發(fā)展規(guī)律，建立了初始條件滑動的非等間距管道腐蝕預測灰色模型SUGM（1,1,）。Chen等[7]利用主成分分析法提取海底管道內(nèi)腐蝕的關(guān)鍵腐蝕因素，消除冗余信息并確定管道失效原因。王曉敏等[8]基于時變可靠性方法提出了一種多失效模式腐蝕影響下的地下管道失效概率預測方法。謝飛等[9]從化學反應、電化學反應和傳質(zhì)過程3個方面探究了天然氣管道CO2腐蝕機理，提出了基于腐蝕機理的腐蝕速率預測模型。但以上傳統(tǒng)研究方法針對管線腐蝕預測問題仍存在不足：SUGM（1,1,）初始條件的確定方式復雜，大幅更新腐蝕數(shù)據(jù)對預測結(jié)果的準確性存在較大影響；主成分分析法僅適用于處理線性映射問題，對非線性數(shù)據(jù)的特征提取效果較差；基于時變可靠性的失效概率方法難以準確定義失效事件的關(guān)聯(lián)性，預測前提存在主觀因素；機理模型研究僅考慮在理想溶液環(huán)境內(nèi)的單一電化學腐蝕作用，未能考慮到非理想環(huán)境中管道的復雜流動等問題。隨著智能信息處理技術(shù)的高速發(fā)展，大量智能優(yōu)化算法得以應用于管道的腐蝕預測研究。凌曉等[10]通過優(yōu)化反向傳播神經(jīng)網(wǎng)絡（BPNN）參數(shù)，對輸油管道的內(nèi)腐蝕速率進行了預測分析。駱正山等[11]建立基于動態(tài)貝葉斯網(wǎng)絡（DBN）的疲勞壽命模型，預測了海底腐蝕管道的失效概率。Peng等[12]通過優(yōu)化支持向量回歸（SVR）模型參數(shù)，對多相流管道的腐蝕速率進行了預測分析。曲志豪等[13]利用網(wǎng)格搜索算法優(yōu)化了隨機森林回歸模型，建立了GA–RFC模型并對油氣管道腐蝕速率進行了預測。但上述智能算法仍存在不足：BPNN存在結(jié)構(gòu)復雜、訓練速度慢、易陷入局部極小值等缺點；DBN的先驗概率假設具有較強的主觀性，對于屬性非完全獨立的大規(guī)模樣本適用性不佳；SVR中參數(shù)的確定存在強隨機性，使得模型預測結(jié)果的波動性較大；RFC模型中含有噪聲樣本時容易發(fā)生過擬合現(xiàn)象。

綜上，本文在核主成分分析中引入小波核函數(shù)，對鹽穴儲氣庫注采管柱內(nèi)的腐蝕因素進行特征提取，利用改進灰狼優(yōu)化算法優(yōu)化極限學習機的輸入權(quán)值矩陣和隱含層閾值，建立小波KPCA–IGWO–ELM的鹽穴儲氣庫注采管柱內(nèi)腐蝕速率預測模型。在MATLAB中對比分析多種預測模型的仿真結(jié)果，驗證所建模型的適用性與準確性，為鹽穴儲氣庫注采系統(tǒng)安全運行提供可靠支撐。

1 KPCA

2 IGWO–ELM模型

2.1 ELM

求解線性方程組式（7）得到最小二乘解：

2.2 IGWO

改進灰狼優(yōu)化[23]（IGWO）是Mirjalili等人于2020年提出的一種新型群體智能優(yōu)化算法，算法在灰狼優(yōu)化[24]（GWO）的基礎上引入了基于維度學習狩獵（DLH）的改進搜索策略，有效解決了GWO種群多樣性差、后期收斂速度慢、易陷入局部最優(yōu)等缺點。

重復以上過程，當?shù)螖?shù)等于最大迭代次數(shù)時循環(huán)結(jié)束，輸出最優(yōu)適應度值作為獵物最終位置，IGWO獲全局最優(yōu)解。

2.3 IGWO–ELM預測模型構(gòu)建

2.4 預測模型評價指標

本文選用均方根誤差（RMSE）、平均絕對百分比誤差（MAPE）和決定系數(shù)（2）3個指標[25]對IGWO–ELM模型預測結(jié)果進行評價，計算公式見式（22）—（24）。

圖1 IGWO–ELM模型腐蝕預測流程

3 實例應用

3.1 指標構(gòu)建與數(shù)據(jù)采集

以某鹽穴儲氣庫注采管柱的實測試驗為例，選取10種常見腐蝕因素構(gòu)建鹽穴儲氣庫注采管柱的內(nèi)腐蝕指標體系，如圖2所示。結(jié)合項目運行資料，設定預測模型工況條件適用范圍如表1所示，取250組實測數(shù)據(jù)用作預測模型樣本，部分數(shù)據(jù)見表2。

圖2 鹽穴儲氣庫注采管柱的內(nèi)腐蝕指標體系

3.2 特征提取

將采集的250組數(shù)據(jù)做歸一化處理，后用小波KPCA對腐蝕指標進行特征提取，得出綜合腐蝕因素特征值與貢獻率，如表3所示。

KPCA規(guī)定，被選主成分的累計貢獻率應不低于95%。分析表3易知，前3項主成分的累計貢獻率高達98.61%，故將前3項作為影響鹽穴儲氣庫注采管柱內(nèi)腐蝕的特征指標。

表1 預測模型工況適用范圍

Tab.1 Application range of prediction model

3.3 腐蝕速率預測結(jié)果分析

將訓練好的IGWO–ELM對其余50組腐蝕數(shù)據(jù)進行測試。為體現(xiàn)IGWO–ELM預測模型的準確性，選用ELM、PSO–ELM、SSA–ELM對相同數(shù)據(jù)進行預測分析，模型的預測結(jié)果對比見圖4，預測相對誤差對比見圖5。表4為分別用高斯核函數(shù)和小波核函數(shù)進行特征提取后，4個預測模型的相對誤差分析結(jié)果。

表2 鹽穴儲氣庫注采管柱的內(nèi)腐蝕數(shù)據(jù)

Tab.2 Internal corrosion data of injection and production string in salt cavern gas storage

表3 特征變量提取

Tab.3 Extraction of characteristic variables

圖3 IGWO–ELM適應度收斂曲線

圖4 預測結(jié)果對比圖

圖5 預測相對誤差對比圖

表4 不同核函數(shù)的預測結(jié)果相對誤差分析

Tab.4 Relative error analysis of prediction results of different kernel functions

由圖4可知，相較于ELM、PSO–ELM和SSA– ELM，IGWO–ELM的預測結(jié)果更接近于實際值，擬合程度更高。分析圖5和表4可知，經(jīng)小波KPCA的模型預測性能均優(yōu)于經(jīng)高斯KPCA的模型，且經(jīng)小波KPCA的ELM、PSO–ELM、SSA–ELM、IGWO– ELM的平均相對誤差分別為9.404 8%、5.061 5%、1.573 7%、0.707 3%，相較于經(jīng)高斯KPCA的平均相對誤差分別降低了3.409 2%、2.933 5%、1.018 4%、0.577 1%，說明小波KPCA–IGWO–ELM模型的性能提升較大。為驗證模型的預測效果，采用2.4節(jié)中的3項指標分別對4種預測模型進行評價分析，結(jié)果見表5。

由表5可知，IGWO–ELM的2高達0.992 5，其RMSE和MAPE分別比ELM降低了0.116 6、16.175 1%，比PSO–ELM降低了0.073 2、8.410 6%，比SSA–ELM降低了0.072 3、4.656 7%。在一定工況條件適用范圍內(nèi)，說明IGWO–ELM模型的性能優(yōu)良，鹽穴儲氣庫注采管柱內(nèi)腐蝕速率的預測結(jié)果更精準。

表5 模型性能評價指標對比

Tab.5 Comparison of model performance evaluation indicators

4 結(jié)論

1）利用小波KPCA提取出包含98.61%腐蝕信息的3項主成分，基于此的預測結(jié)果相對誤差最小。經(jīng)小波KPCA的ELM、PSO–ELM、SSA–ELM、IGWO– ELM的平均相對誤差分別為9.404 8%、5.061 5%、1.573 7%、0.707 3%，相較于經(jīng)高斯KPCA的平均相對誤差分別降低了3.409 2%、2.933 5%、1.018 4%、0.577 1%。

[1] 魏國齊, 鄭雅麗, 邱小松, 等. 中國地下儲氣庫地質(zhì)理論與應用[J]. 石油學報, 2019, 40(12): 1519-1530.

WEI Guo-qi, ZHENG Ya-li, QIU Xiao-song, et al. Geolo-gi-cal Theory and Application of Underground Gas Storagein China[J]. Acta Petrolei Sinica, 2019, 40(12): 1519-1530.

[2] 完顏祺琪, 丁國生, 趙巖, 等. 鹽穴型地下儲氣庫建庫評價關(guān)鍵技術(shù)及其應用[J]. 天然氣工業(yè), 2018, 38(5): 111-117.

WANYAN Qi-qi, DING Guo-sheng, ZHAO Yan, et al. Key Technologies for Salt-Cavern Underground Gas Storage Construction and Evaluation and Their Application[J]. Natural Gas Industry, 2018, 38(5): 111-117.

[3] WANG Tong-tao, YANG Chun-he, CHEN Jia-song, et al. Geomechanical Investigation of Roof Failure of China's First Gas Storage Salt Cavern[J]. Engineering Geology, 2018, 243: 59-69.

[4] LIU Wei, CHEN Jie, JIANG De-yi, et al. Tightness and Suitability Evaluation of Abandoned Salt Caverns Served as Hydrocarbon Energies Storage under Adverse Geologi-calConditions (AGC)[J]. Applied Energy, 2016, 178: 703-720.

[5] CHEN Xiang-sheng, LI Yin-ping, LIU Wei, et al. Study on Sealing Failure of Wellbore in Bedded Salt Cavern Gas Storage[J]. Rock Mechanics and Rock Engineering, 2019, 52(1): 215-228.

[6] 張新生, 葉曉艷. 不同初始條件的UGM(1,1)管道腐蝕預測建模研究[J]. 中國安全科學學報, 2019, 29(3): 63-69.

ZHANG Xin-sheng, YE Xiao-yan. Study on UGM(1,1) Modeling for Prediction of Pipes Corrosion under Different Initial Conditions[J]. China Safety Science Journal, 2019, 29(3): 63-69.

[7] CHEN Xiao-xu, WANG Lin-yuan, HUANG Zhi-yu. Prin-cipal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines[J]. Mathematical Problems in Engineering, 2020, 2020: 3681032.

[8] 王曉敏, 駱正山, 高懿瓊, 等. 基于多種失效模式及其隨機相關(guān)性的地下管道腐蝕可靠性分析[J]. 表面技術(shù), 2022, 51(4): 202-210.

WANG Xiao-min, LUO Zheng-shan, GAO Yi-qiong, et al. Reliability Analysis of Corrosion Affected Underground Steel Pipes Considering Multiple Failure Modes and Their Stochastic Correlations[J]. Surface Technology, 2022, 51(4): 202-210.

[9] 謝飛, 李佳航, 王新強, 等. 天然氣管道CO2腐蝕機理及預測模型研究進展[J]. 天然氣工業(yè), 2021, 41(10): 109-118.

XIE Fei, LI Jia-hang, WANG Xin-qiang, et al. Research Progress on CO2Corrosion Mechanism and Prediction Model of Natural Gas Pipelines[J]. Natural Gas Industry, 2021, 41(10): 109-118.

[10] 凌曉, 徐魯帥, 余建平, 等. 基于改進的BP神經(jīng)網(wǎng)絡的輸油管道內(nèi)腐蝕速率預測[J]. 傳感器與微系統(tǒng), 2021, 40(2): 124-127.

LING Xiao, XU Lu-shuai, YU Jian-ping, et al. Prediction of Corrosion Rate in Oil Pipeline Based on Improved BP Neural Network[J]. Transducer and Microsystem Techno-logies, 2021, 40(2): 124-127.

[11] 駱正山, 趙樂新, 王小完. 基于動態(tài)貝葉斯網(wǎng)絡的海底管道點蝕疲勞損傷失效模型研究[J]. 表面技術(shù), 2020, 49(1): 269-275.

LUO Zheng-shan, ZHAO Le-xin, WANG Xiao-wan. Failure Model for Pitting Fatigue Damaged Pipeline of Subsea Based on Dynamic Bayesian Network[J]. Surface technology, 2020,49(1): 269-275.

[12] PENG Shan-bi, ZHANG Zhe, LIU En-bin, et al. A New Hybrid Algorithm Model for Prediction of Internal Corrosion Rate of Multiphase Pipeline[J]. Journal of Natural Gas Science and Engineering, 2021, 85: 103716.

[13] 曲志豪, 唐德志, 胡麗華, 等. 基于優(yōu)化隨機森林的H2S腐蝕產(chǎn)物類型及腐蝕速率預測[J]. 表面技術(shù), 2020, 49(3): 42-49.

QU Zhi-hao, TANG De-zhi, HU Li-hua, et al. Prediction of H2S Corrosion Products and Corrosion Rate Based on Optimized Random Forest[J]. Surface Technology, 2020, 49(3): 42-49.

[14] SCH?LKOPF B, SMOLA A, MüLLER K R. Nonlinear Component Analysis as a Kernel Eigenvalue Problem[J]. Neural Computation, 1998, 10(5): 1299-1319.

[15] FEZAI R, MANSOURI M, TAOUALI O, et al. Online Reduced Kernel Principal Component Analysis for Process Monitoring[J]. Journal of Process Control, 2018, 61: 1-11.

[16] HU Qin, QIN Ai-song, ZHANG Qing-hua, et al. Fault Diagnosis Based on Weighted Extreme Learning Machine with Wavelet Packet Decomposition and KPCA[J]. IEEE Sensors Journal, 2018, 18(20): 8472-8483.

[17] BRO R, SMILDE A K. Principal Component Analysis[J]. Anal Methods, 2014, 6(9): 2812-2831.

[18] JOLLIFFE I T, CADIMA J. Principal Component Analy-sis: A Review and Recent Developments[J]. Philosophical Transactions Series A, Mathematical, Physical, and Engi-neering Sciences, 2016, 374(2065): 20150202.

[19] 遲恩楠, 李春祥. 基于優(yōu)化組合核和Morlet小波核的LSSVM脈動風速預測方法[J]. 振動與沖擊, 2016, 35(18): 52-57.

CHI En-nan, LI Chun-xiang. Forecast of Fluctuating WindVelocity Using LSSVM with Optimized Combination Kernel and Morlet Wavelet Kernel[J]. Journal of Vibration and Shock, 2016, 35(18): 52-57.

[20] HUANG Guang-bin, ZHU Qin-yu, SIEW C K. Extreme Learning Machine: Theory and Applications[J]. Neuroco-mputing, 2006, 70(1-3): 489-501.

[21] HUANG Guang-bin, ZHOU Hong-ming, DING Xiao- jian, et al. Extreme Learning Machine for Regression and Multiclass Classification[J]. IEEE Transactions on Systems, Man, and Cybernetics Part B, Cybernetics: A Publication of the IEEE Systems, Man, and Cybernetics Society, 2012, 42(2): 513-529.

[22] HUANG Gao, HUANG Guang-bin, SONG Shi-ji, et al. Trends in Extreme Learning Machines: A Review[J]. Neural Networks, 2015, 61: 32-48.

[23] NADIMI-SHAHRAKI M H, TAGHIAN S, MIRJALILI S. An Improved Grey Wolf Optimizer for Solving Engi-neering Problems[J]. Expert Systems With Applications, 2021, 166: 113917.

[24] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey Wolf Optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.

[25] 駱正山, 秦越, 張新生, 等. 基于LASSO-WOA-LSSVM的海洋管線外腐蝕速率預測[J]. 表面技術(shù), 2021, 50(5): 245-252.

LUO Zheng-shan, QIN Yue, ZHANG Xin-sheng, et al. Prediction of External Corrosion Rate of Marine Pipelines Based on LASSO-WOA-LSSVM[J]. Surface Technology, 2021, 50(5): 245-252.

Research on Prediction Model of Internal Corrosion Rate in Injection and Production String of Salt Cavern Gas Storage

,,,

(School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China)

The injection and production string of the salt cavern gas storage has been in a complex underground environment for a long time, making it susceptible to a variety of corrosion factors. This work aims to improve the prediction accuracy of the corrosion rate in the injection and production string of the salt cavern gas storage, thereby ensuring the health and operational safety of these facilities. To accomplish the above objectives, the solution proposed is to establish an internal corrosion rate prediction model based on wavelet kernel principal component analysis (KPCA) and an extreme learning machine (ELM) after improved gray wolf optimization (IGWO).First of all, in the actual operation data of the injection and production string of the salt cavern gas storage, 10 indicators with larger corrosion factors are selected, such as: partial pressure of carbon dioxide, hydrogen sulfide partial pressure, inner wall surface temperature, etc. Subsequently, the internal corrosion index system of the injection and production string of the salt cavern gas storage was established.Secondly, the wavelet KPCA is used to extract the key features that affect the internal corrosion rate of the injection and production string, and then IGWO is used to iteratively optimize the input weight matrix and hidden layer threshold of the ELM model, and stop the loop until the termination condition is met. Furthermore, a prediction model of corrosion rate in the injection and production string of IGWO-ELM salt cavern gas storage is established. Finally, numerical simulation and simulation calculation are carried out in MATLAB software, and the prediction errors of the IGWO-ELM model are compared with the three models of ELM, PSO-ELM and SSA-ELM respectively. The research results show that the wavelet KPCA effectively extracts the three principal components that contain 98.61% of the original information in the corrosion data of the injection-production pipe string of the salt cavern gas storage.Applying the reconstructed corrosion data to the ELM, PSO-ELM, SSA-ELM, and IGWO-ELM models, their average relative errors are 9.404 8%, 5.061 5%, 1.573 7%, and 0.707 3%. The prediction results of the IGWO-ELM model are in good agreement with the actual values.The root mean square error of the constructed IGWO-ELM model is 0.008 8, the average absolute percentage error is 0.260 9%, and the coefficient of determination (2) is as high as 0.992 5. Its prediction result is better than the other three comparison models. The kernel principal component analysis with the introduction of wavelet kernel function has an excellent ability to extract corrosion characteristics of the injection and production string of the salt cavern gas storage. Within the applicable range of certain working conditions, the established IGWO-ELM model can effectively predict the internal corrosion rate of the injection and production string of the salt cavern gas storage.It not only provides a reference basis for the integrity evaluation and risk warning of the injection and production system of the salt cavern gas storage, but also provides new ideas and methods for the corrosion study of the injection and production string of the salt cavern gas storage.

salt cavern gas storage; injection and production string; corrosion rate prediction; principal component analysis (KPCA); improved gray wolf optimization (IGWO); extreme learning machine (ELM)

TG174

A

1001-3660(2022)06-0283-08

10.16490/j.cnki.issn.1001-3660.2022.06.026

2021–04–28；

2021–12–07

2021-04-28；

2021-12-07

國家自然科學基金（41877527）；陜西省社科基金（2018S34）

National Natural Science Foundation of China (41877527); Shaanxi Provincial Social Science Fund (2018S34)

駱正山（1969—），男，博士，教授，主要研究方向為油氣管道風險評估。

LUO Zheng-shan (1969-), Male, Doctor, Professor, Research focus: oil and gas pipeline risk assessment.

駱正山, 歐陽長風, 王小完, 等.鹽穴儲氣庫注采管柱內(nèi)腐蝕速率預測模型研究[J]. 表面技術(shù), 2022, 51(6): 283-290.

LUO Zheng-shan, OUYANG Chang-feng, WANG Xiao-wan, et al. Research on Prediction Model of Internal Corrosion Rate in Injection and Production String of Salt Cavern Gas Storage[J]. Surface Technology, 2022, 51(6): 283-290.

責任編輯：萬長清