|
|
|
|
|
|
Detection and Analysis of Water Content of Crude Oil by Near Infrared Spectroscopy |
LIU Hong-ming1,2, LIU Yu-juan1*, ZHONG Zhi-cheng1, SONG Ying1*, LI Zhe1, XU Yang1 |
1. Key Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation & Electrical Engineering, National Geophysical Exploration Equipment Engineering Research Center, Jilin University, Changchun 130012, China
2. Tonghua Normal University, Tonghua 134000,China |
|
|
Abstract As an important strategic resource, petroleum has an important significance in real-time analysis and detection of its components in the petrochemical industry. With the continuous development of petroleum resources, in the production process of long-term oil wells and before the exploitation of new oil wells, it is necessary to analyze and test the components of underground crude oil to determine the necessity of mining. Real-time detection of crude oil components plays a key role in the process of crude oil exploitation, production, storage, transportation and sales. In view of the low accuracy and low efficiency of traditional detection methods, near-infrared spectroscopy, which has been widely and effectively applied in the field of measurement, has been introduced into the research methods of crude oil components detection technology in recent years. In this paper, the underground crude oil was used as the research object, and the experimental samples of 39 components with crude oil ratio of 1%~20% were prepared by using the purified crude oil and water obtained from Daqing Oilfield to simulate the underground crude oil. In this paper, the basic principle of the measurement of crude oil components by near-infrared spectroscopy is studied. The near-infrared spectroscopy data measurement system of crude oil samples is integrated with the halogen source and supporting components by SW2520 near-infrared spectroscopy, and 39 groups are collected by this measurement after system standardization testing. Moving window smoothing Savitzky-Golay convolution smoothing and Savitzky-Golay convolution derivation method of near infrared spectrum data of crude oil near infrared spectral data is used to remove noise, two analysis methods of near-infrared spectra of crude sample partial least squares and support vector machine (SVM) regression method are used for retreatment modeling, crude oil component analysis prediction model is established respectively. The results show that the RMS predicted by partial least square method is 0.003 755 14, and the coefficient of determination R2 was 0.999 999. The prediction effect is very good. In this study, the near infrared spectroscopy (NIRS) technology is used to test and analyze the simulated test samples with different proportions, which provides a new idea for the detection of the components of the downhole crude oil. This method effectively solves the detection problem of the water content of the crude oil and provides technical support for the development of the real-time crude oil detection and analysis equipment in the field.
|
Received: 2019-12-18
Accepted: 2020-04-22
|
|
Corresponding Authors:
LIU Yu-juan, SONG Ying
E-mail: xuliuyujuan@163.com; 50367444@qq.com
|
|
[1] CAIA(中国分析测试协会). Review of the Development of Science and Technology Award of CAIA(中国分析测试协会科学技术奖发展回顾). Beijing: Beijing Science and Technology Press(北京:北京科学技术出版社),2015. 1.
[2] CHU Xiao-li, TIAN Song-bo, XU Yu-peng, et al(褚小立, 田松柏, 许育鹏,等). Petroleum Processing and Petrochemicals(石油炼制与化工), 2012, 43(1): 72.
[3] CHEN Pu, LI Jing-yan, CHU Xiao-li(陈 瀑, 李敬岩, 褚小立). Petroleum Processing and Petrochemicals(石油炼制与化工), 2016, 47(10): 98.
[4] Lucas Marcos A, Borges Gustavo R, da Rocha Inaura C C, et al. The Journal of Supercritical Fluids, 2016, 118:140.
[5] CHEN Pu, SUN Jian,ZHANG Feng-hua, et al(陈 瀑, 孙 健, 张凤华,等). Petroleum Processing and Petrochemicals(石油炼制与化工), 2014, 45(8): 97.
[6] Rohit Sharma, Vikas Mahto, Hari Vuthaluru. Fuel, 2019, 235(1): 1245.
[7] Amir Bagheri Garmarudi, Mohammadreza Khanmohammadi. Fuel, 2019, 236(2): 1093.
[8] Sameer Mhatre, Sébastien Simon, Johan Sjöblom, et al. Chemical Engineering Research and Design, 2018, 134(6): 117.
[9] Siller de Oliveira Honse, Khalil Kashefi, Rafael Mengotti Charin, et al. Colloides and Surfaces A: Physicochemical and Engineering Aspects, 2018, 538(2): 565.
[10] Rosa Elvira Correa Pabón, Carlos Roberto de Souza Filho. Fuel, 2019, 237(3): 1119.
[11] LIN Nan(李 娜). Modern Industrial Economy and Informationization(现代工业经济和信息化), 2016, 6(19): 40.
[12] LI Jing-yan, CHU Xiao-li, TIAN Song-bai(李敬岩, 褚小立, 田松柏). Acta Petrolei Sinica(石油学报), 2012, 28(3): 476.
[13] Azam Maleki-Ghahfarokhi, Iman Dianat, Hossein Feizi. Applied Ergonomics, 2019, 79(9): 9.
[14] Yukteshwar Baranwal, Andrés D Román-Ospino, Golshid Keyvan, et al. International Journal of Pharmaceutics, 2019, 565(6): 419. |
[1] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[2] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[3] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[4] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[5] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[6] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[7] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[8] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[9] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[10] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[11] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[12] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[13] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[14] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[15] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
|
|
|
|