Quantitative identification of hydrocarbon concentration in drilling fluid based on laser Raman spectroscopy
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摘要: 钻井液中的烃能够显示出地层的含油气情况,地层含油气浓度的检测对识别真假油气显示,特别是准确解释和评价油气层具有重要意义。基于激光拉曼光谱技术具有连续、快速、直接检测样品的独特优势,开展了激光拉曼光谱技术应用于钻井液中含烃浓度定量识别的研究。实验室条件下搭建的激光拉曼在线检测系统对C7~C14正构烷烃及苯进行了检测与振动模式指认,在水基钻井液中优选正辛烷作为标志烃,基于最小二乘法建立了水基钻井液中正辛烷不同特征峰数学模型,在1 298 cm-1频移处振动强度与正辛烷含量具有良好的线性关系;在柴油基钻井液中优选苯作为标志烃,基于最小二乘法建立了油基钻井液中苯的不同特征峰数学模型,在986 m-1频移处振动强度与添加苯含量具有良好线性关系。实验结果表明:激光拉曼光谱技术可用于钻井液中含烃浓度的检测,为反演地层含油气浓度,提高油气层判识精度提供了一种新的途径。Abstract: The hydrocarbons in the drilling fluid can indicate the hydrocarbon content of the formation.The detection of oil and gas concentration in the stratum is of great significance for the identification of true and false oil and gas, especially for the accurate interpretation and evaluation of oil and gas layers.Based on the unique advantages of continuous, rapid and direct detection of samples by laser Raman spectroscopy, we carried out researches on quantitative Raman detection of hydrocarbon concentration in drilling fluid.The laser Raman online detection system was built under laboratory conditions which was used to detect C7-C14 normal paraffin and benzene.The n-octane was selected as the marker of alkanes in water-based drilling fluid and the mathematical models of different characteristic peaks of n-octane in water-based drilling fluid were established based on the least square method.The vibration intensity at the frequency shift of 1 298 cm-1 has a good linear relationship with n-octane content.The benzene was selected as the marker hydrocarbon in the diesel oil drilling fluid and the mathematical model of different characteristic peaks of benzene in oil drilling fluid were established based on the least square method.The vibration intensity at the frequency shift of 986 m-1 has a good linear relationship between the vibration intensity and the content of added benzene.The experimental results show that the laser Raman spectroscopy can be used for in-situ detection of hydrocarbon concentration in drilling fluid and has good stability and repeatability in order to provide a new way for the inversion of oil and gas concentration and improving the accuracy of oil and gas reservoir identification.
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Keywords:
- laser Raman /
- drilling fluid /
- oil and gas concentration /
- alkanes /
- benzene
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表 1 烷烃主要拉曼特征峰频移
Table 1 Main Raman characteristic peak frequency shifts of alkanes
化学结构 相应光谱谱带/cm-1 C-C骨架伸缩振动 800~890 C-C伸缩振动 1 430~1 450 CH2面内摇摆振动 1 200~1 310 CH2面内扭转振动 1 290~1 305 CH3对称变形振动 1 385~1 370 CH3变形振动 1 465~1 470 CH3反对称伸缩振动 2 960~2 970 CH2反对称伸缩振动 2 910~2 930 CH3对称伸缩振动 2 980~2 990 CH2对称伸缩振动 2 950~2 960 C-H伸缩振动 2 800~3 000 表 2 苯主要拉曼特征峰频移
Table 2 Main Raman characteristic peak frequency shifts of benzene
化学结构 相应光谱谱带/cm-1 面外环变形振动 200~400 面内环变形振动 400~1 000 环呼吸振动 600~620 C-H面外弯曲振动 650~910 环伸缩振动 990~1 000 C=C伸缩和C-H摇摆耦合振动 1 000~1 700 C-H面内弯曲振动 1 100~1 200 C-H摇摆振动 1 150~1 450 C=C伸缩振动(苯环骨架振动) 1 560~1 600 C-H伸缩振动 3 000~3 100 表 3 正辛烷与水基钻井液澄清液混合溶液对应特征峰检测强度
Table 3 Characteristic peak test intensity of mixture solution of n-octane and water-base drilling fluid clarification
体积分数/% 质量分数/% 检测强度 特征峰1 298 cm-1 特征峰1 439 cm-1 特征峰2 870 cm-1 10 8.19 1 242 2 864 563 20 16.71 1 308 2 866 713 30 25.59 1 413 3 095 858 40 34.85 1 537 3 160 902 50 44.52 1 593 3 179 1 004 60 54.62 1 729 3 333 1 381 70 65.19 1 802 3 349 2 016 80 76.25 1 843 3 323 3 608 表 4 正辛烷与水基钻井液澄清液混合液中正辛烷不同特征峰频移强度拟合方程
Table 4 Fitting equation of n-octane with different characteristic peak frequency shift strengths in mixture solution of n-octane and water-base drilling fluid clarification
正辛烷特征峰频移 一元二次回归方程 拟合优度 置信度 特征峰1 298 cm-1 y=1 116.79+1 332.23x-469.31x2 0.989 0 5.44×10-6 特征峰1 439 cm-1 y=2 689.78+1 714.72x-1 123.74x2 0.923 0 7.10×10-4 特征峰2 870 cm-1 y=1 075.21-4 261.12x+9 470.15x2 0.929 6 5.67×10-4 表 5 苯与柴油基不同体积比混合溶液对应特征峰检测强度
Table 5 Characteristic peaks of mixture solution of benzene and diesel with different volume ratios
添加苯体积分数 添加苯质量分数 检测强度 特征峰599 cm-1 特征峰986 cm-1 特征峰3 057 cm-1 2% 2.10% 647 5 213 1 005 4% 4.20% 675 5 695 1 082 6% 6.30% 732 6 170 1 147 8% 8.39% 764 6 501 1 215 10% 10.47% 774 6 949 1 299 12% 12.25% 995 7 400 1 796 14% 14.63% 932 7 864 1 712 16% 16.70% 893 8 336 1 607 表 6 苯与柴油基混合液中苯不同特征峰频移强度移拟合方程
Table 6 Fitting equation of benzene with different characteristic peak frequency intensity shifts in mixture of benzene and diesel
苯特征峰频移 一元二次回归方程 拟合优度 置信度 特征峰599 cm-1 y=554.50+3 386.16x-644.86x2 0.717 8 1.83×10-2 特征峰986 cm-1 y=4 831.68+19 698.75x+7 062.02x2 0.998 5 3.96×10-8 特征峰3 057 cm-1 y=826.77+6 107.33x-3 959.97x2 0.735 7 1.55×10-2 -
[1] 吴刚.重烃组分在现场录井中输送方法探讨[J].中国石油和化工标准与质量, 2012, 32(S1):210. http://www.cnki.com.cn/Article/CJFDTotal-HGBJ2012S1199.htm WU Gang. Discussion on transportation method of heavy hydrocarbon components in field logging[J].China Petroleum and Chemical Standard and Quality, 2012, 32(S1):210. http://www.cnki.com.cn/Article/CJFDTotal-HGBJ2012S1199.htm
[2] 蒋淑英.大庆油田废钻井液生物毒性及生物效应的研究[D].大庆: 大庆石油学院, 2007. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y1224931 JIANG Shuying. The study of bio-Toxicoty and biological effects of the wastes drilling fluids from Daqing Oilfield[D].Daqing: Northeast Petroleum University, 2007. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y1224931
[3] 刘一丹.轻烃检测分析技术在吉林油田的应用[J].石油知识, 2014(5): 30-31. doi: 10.3969/j.issn.1003-4609.2014.05.013 LIU Yidan. Application of light hydrocarbon detection and analysis technology in Jilin Oilfield[J].Petroleum Knowledge, 2014(5): 30-31. doi: 10.3969/j.issn.1003-4609.2014.05.013
[4] 吕玉祥, 董肖节, 郭峰.基于红外差分检测的甲烷气体传感器[J].应用光学, 2012, 33(4):747-751. http://www.yygx.net/CN/abstract/abstract10036.shtml LV Yuxiang, DONG Xiaojie, GUO Feng. Methane gas sensor based on infrared difference detection[J].Journal of Applied Optics, 2012, 33(4):747-751. http://www.yygx.net/CN/abstract/abstract10036.shtml
[5] CUDDY, STEVE.The 49th SPWLA annual symposium[J].Petrophysics, 2008, 48(5):409-412.
[6] 陈代伟, 袁伯琰, 杨君.钻井液中烃类气体含量的检测[J].油气田地面工程, 2007, 26(7):52. doi: 10.3969/j.issn.1006-6896.2007.07.034 CHEN Daiwei, YUAN Boyan, YANG Jun. Detection of hydrocarbon gas content in drilling fluid[J].Oil-Gasfield Surface Engineering, 2007, 26(7):52. doi: 10.3969/j.issn.1006-6896.2007.07.034
[7] TANG M M, ZHAGN J L.Raman spectra of hydrocarbon in returned drilling fluid using co-focal laser Raman microscopy[J].Advanced Materials Research, 2013, 616:715-719. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.4028/www.scientific.net/AMR.616-618.715
[8] 郭忠.微型拉曼光谱仪的结构设计与数据处理方法研究[D].重庆: 重庆大学, 2010. http://cdmd.cnki.com.cn/Article/CDMD-10611-2010217933.htm GUO Zhong.Design the Micro-Raman spectroscopy and study on data processing of Raman spectrum[D].Chongqing: Chongqing University, 2010. http://cdmd.cnki.com.cn/Article/CDMD-10611-2010217933.htm
[9] 张正勇, 桂冬冬, 马蕴文, 等.高维激光拉曼光谱的构建与降噪处理评价研究[J].应用激光, 2018, 38(3):468-473. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyjg201803024 ZHANG Zhengyong, GUI Dongdong, MA Yunwen, et al. Evaluation on the construction and noise reduction of high-dimensional laser Raman spectroscopy[J].Applied Laser, 2018, 38(3):468-473. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyjg201803024
[10] 李柏霖.光谱法研究高分子薄膜及水溶性体系的结构与性能[D].杭州: 浙江理工大学, 2014. LI Bailin. Structures and properties of polymer thin films and water-soluble polymer mixtures studied by spectroscopy[D].Hangzhou: Zhejiang Sci-Tech University, 2014.
[11] 刘建美, 李存磊, 高鹏, 等.基于激光拉曼检测技术的输油管道原油鉴别方法[J].应用光学, 2018, 39(3):436-441. http://www.yygx.net/CN/abstract/abstract11129.shtml LIU Jianmei, LI Cunlei, GAO Peng, et al.Identification method of crude oil in petroleum pipeline based on laser Raman detection technology[J].Journal of Applied Optics, 2018, 39(3):436-441. http://www.yygx.net/CN/abstract/abstract11129.shtml
[12] PRIVALOV V E, SHEMANIN V G.Measurement of the concentration of hydrocarbon molecules by Raman light lidar[J]. Measurement Techniques, 2016, 59(9):933-938. doi: 10.1007/s11018-016-1070-6
[13] 杜玲玲, 陈伟根, 顾朝亮, 等.应用拉曼光谱分析变压器油中溶解乙酸含量[J].光谱学与光谱分析, 2017, 37(9):2774-2779. http://d.old.wanfangdata.com.cn/Periodical/gpxygpfx201709021 DU Lingling, CHEN Weigen, GU Chaoliang, et al.Application of Raman spectroscopy to analyze acetic acid content dissolved in transformer oil[J].Spectroscopy and Spectral Analysis, 2017, 37(9):2774-2779. http://d.old.wanfangdata.com.cn/Periodical/gpxygpfx201709021
[14] 陶家友, 徐代升, 梅孝安, 等.苯的激光拉曼光谱检测研究[J].应用光学, 2010, 31(2):273-276. doi: 10.3969/j.issn.1002-2082.2010.02.024 TAO Jiayou, XU Daisheng, MEI Xiaoan, et al. Laser-Raman spectrum for detecting benzene[J].Journal of Applied Optics, , 2010, 31(2):273-276. doi: 10.3969/j.issn.1002-2082.2010.02.024
[15] LIU W, DAI L H. Raman spectral analysis of low-content benzene concentration in gasoline with partial least squares based on interference peak subtraction[J]. Analytical Sciences, 2016, 32(8):861-866. doi: 10.2116/analsci.32.861
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