录井专用型激光诱导击穿光谱仪测定岩屑中的8种元素
|
摘要
记录不同井深岩屑元素含量信息的岩屑录井工作是油气勘探开发的必要过程之一。本研究将西南某钻井现场的岩屑样品分为训练集和测试集,使用自行研制的小型台式激光诱导击穿光谱仪(LIBS-Trace)采集各岩屑样品谱图,对8种元素(Si、Al、Ca、K、Mg、Mn、Ti、Fe)优化建立基于支持向量机回归(SVR)的元素定量分析模型。将测试集数据用于算法模型评估验证,结果表明,8种元素预测的平均百分比误差(MPE)分别为Si 5.68%、Al 7.22%、Ca 7.45%、K 9.76%、Mg 8.79%、Mn 11.9%、Ti 11.4%和Fe 10.4%,可以满足录井工作的要求。此外,依据录井工作的流程与需求,对仪器软硬件进行修改,将优化确立的定量模型集成于仪器之中,使之成为录井专用型号。在录井工作现场进行测试,结果表明,算法模型结合仪器可快速完成岩屑元素定量分析,在油气勘探开发领域具有潜在的应用价值。
It is one of the necessary work in oil and gas exploration and development to record the logging of different depth cuttings. In this study,a laser induced breakdown spectrometer( LIBS-Trace) developed by our research group was used for the lithology logging in the field of oil and gas exploration and development. A total of 60 samples from a drilling site in the Southwest China were divided into training set and test set,and then element quantitative analysis model of eight elements of Si,Al,Ca,K,Mg,Mn,Ti and Fe was established based on support vector machine regression( SVR). The results showed that the mean percentage prediction errors( MPE) predicted by this method were Si 5.68%,Al 7.22%,Ca 7.45%,K 9.76%,Mg 8.79%,Mn 11. 9%,Ti 11. 4% and Fe 10. 4%,which met the requirements of logging work. In addition,according to the logging process and demand,the instrument hardware and software were modified,and the quantitative model integrated in the instrument was optimized. The results showed that the algorithm model combined with the instrument could quickly complete the quantitative analysis of rock debris samples,and exhibited potential application value and broad application prospect in oil and gas exploration and development.
It is one of the necessary work in oil and gas exploration and development to record the logging of different depth cuttings. In this study,a laser induced breakdown spectrometer( LIBS-Trace) developed by our research group was used for the lithology logging in the field of oil and gas exploration and development. A total of 60 samples from a drilling site in the Southwest China were divided into training set and test set,and then element quantitative analysis model of eight elements of Si,Al,Ca,K,Mg,Mn,Ti and Fe was established based on support vector machine regression( SVR). The results showed that the mean percentage prediction errors( MPE) predicted by this method were Si 5.68%,Al 7.22%,Ca 7.45%,K 9.76%,Mg 8.79%,Mn 11. 9%,Ti 11. 4% and Fe 10. 4%,which met the requirements of logging work. In addition,according to the logging process and demand,the instrument hardware and software were modified,and the quantitative model integrated in the instrument was optimized. The results showed that the algorithm model combined with the instrument could quickly complete the quantitative analysis of rock debris samples,and exhibited potential application value and broad application prospect in oil and gas exploration and development.
引文
1 ZHANG Shao-Huai.Mud Logging Engineering,2008,19(4):26-31张绍槐.录井工程,2008,1(4):26-31
2 TIAN Ye,WANG Zhen-Nan,HOU Hua-Ming,ZHAI Xiao-Wei,CI Xing-Hua,ZHENG Rong-Er.Spectroscopy and Sepctral Anaysis,2012,32(8):2027-2031田野,王振南,侯华明,翟晓微,慈兴华,郑荣儿.光谱学与光谱分析,2012,32(8):2027-2031
3 LI Yi-Chao,LI Chun-Shan,LIU De-Lun.Mud Logging Engineering,2008,19(1):1-8李一超,李春山,刘德伦.录井工程,2008,19(1):1-8
4 GUO Hong-Li,LIN Qing-Yu,WANG Shuai,XU Tao,GUO Guang-Meng,TIAN Di,DUAN Yi-Xiang.Chinese J.Anal.Chem.,2016,44(11):1639-1645郭红丽,林庆宇,王帅,许涛,郭广盟,田地,段忆翔.分析化学,2016,44(11):1639-1645
5 Hughes J D.Nature,2013,494(7437):307-308
6 Sarkar A,Karki V,Aggarwal S K,Maurya G S,Kumar R,Rai A K,Mao X,Russo R E.Spectrochim.Acta B,2015,108:8-14
7 ZOU Xiao-Heng,HAO Zhong-Ji,YI Rong-Xing,GUO Lian-Bo,SHEN Meng,LI Xiang-You,WANG Ze-Min,ZENG XiaoYan,LU Yong-Feng.Chinese J.Anal.Chem.,2015,43(2):181-186邹孝恒,郝中骐,易荣兴,郭连波,沈萌,李祥友,王泽敏,曾晓雁,陆永枫.分析化学,2015,43(2):181-186
8 Haddad J E,Bruyere D,Ismael A,Gallou G,Laperche V,Michel K,Canioni L,Bousquet B.Spectrochim.Acta B,2014,97(7):57-64
9 ZHANG Ying,LI Ying,GU Yan-Hong,GUO Hao,LI Na.Spectroscopy and Sepctral Anaysis,2016,36(7):2244-2248张莹,李颖,谷艳红,郭豪,黎娜.光谱学与光谱分析,2016,36(7):2244-2248
10 HU Li,ZHAO Nan-Jing,LIU Wen-Qing,FANG Li,WANG Yin,MENG De-Shuo,YU Yang,GU Yan-Hong,WANG Yuan-Yuan,MA Ming-Jun.Acta Optica Sinica,2015,36(6):314-320胡丽,赵南京,刘文清,方丽,王寅,孟德硕,余洋,谷艳红,王园园,马明俊.光学学报,2015,36(6):314-320
11 Cristoforetti G,Legnaioli S,Palleschi V.J.Anal.At.Spectrom.,2006,21(7):697-702
12 Zhang T,Liang L,Wang K,Tang H,Yang X,Duan Y,Li H.J.Anal.At.Spectrom.,2014,29(12):2323-2329
13 Multari R A,Cremers D A,Dupre J A,Gustafson J E.J.Agri.Food Chem.,2013,61(36):8687-8694
14 Li X,Wang Z,Fu Y.Appl.Spectrosc.,2014,68(9):955-963
15 Vapnik V,Chapelle O.Neural Comput.,2000,12(9):2013-2036
16 Gandhi A B,Joshi J B,Jayaraman V K.Chem.Eng.Sci.,2007,62(24):7078-7089
17 Chang C C,Lin C J.Acm Trans.Intel.Syst.Tec.,2011,2(3):1-27
2 TIAN Ye,WANG Zhen-Nan,HOU Hua-Ming,ZHAI Xiao-Wei,CI Xing-Hua,ZHENG Rong-Er.Spectroscopy and Sepctral Anaysis,2012,32(8):2027-2031田野,王振南,侯华明,翟晓微,慈兴华,郑荣儿.光谱学与光谱分析,2012,32(8):2027-2031
3 LI Yi-Chao,LI Chun-Shan,LIU De-Lun.Mud Logging Engineering,2008,19(1):1-8李一超,李春山,刘德伦.录井工程,2008,19(1):1-8
4 GUO Hong-Li,LIN Qing-Yu,WANG Shuai,XU Tao,GUO Guang-Meng,TIAN Di,DUAN Yi-Xiang.Chinese J.Anal.Chem.,2016,44(11):1639-1645郭红丽,林庆宇,王帅,许涛,郭广盟,田地,段忆翔.分析化学,2016,44(11):1639-1645
5 Hughes J D.Nature,2013,494(7437):307-308
6 Sarkar A,Karki V,Aggarwal S K,Maurya G S,Kumar R,Rai A K,Mao X,Russo R E.Spectrochim.Acta B,2015,108:8-14
7 ZOU Xiao-Heng,HAO Zhong-Ji,YI Rong-Xing,GUO Lian-Bo,SHEN Meng,LI Xiang-You,WANG Ze-Min,ZENG XiaoYan,LU Yong-Feng.Chinese J.Anal.Chem.,2015,43(2):181-186邹孝恒,郝中骐,易荣兴,郭连波,沈萌,李祥友,王泽敏,曾晓雁,陆永枫.分析化学,2015,43(2):181-186
8 Haddad J E,Bruyere D,Ismael A,Gallou G,Laperche V,Michel K,Canioni L,Bousquet B.Spectrochim.Acta B,2014,97(7):57-64
9 ZHANG Ying,LI Ying,GU Yan-Hong,GUO Hao,LI Na.Spectroscopy and Sepctral Anaysis,2016,36(7):2244-2248张莹,李颖,谷艳红,郭豪,黎娜.光谱学与光谱分析,2016,36(7):2244-2248
10 HU Li,ZHAO Nan-Jing,LIU Wen-Qing,FANG Li,WANG Yin,MENG De-Shuo,YU Yang,GU Yan-Hong,WANG Yuan-Yuan,MA Ming-Jun.Acta Optica Sinica,2015,36(6):314-320胡丽,赵南京,刘文清,方丽,王寅,孟德硕,余洋,谷艳红,王园园,马明俊.光学学报,2015,36(6):314-320
11 Cristoforetti G,Legnaioli S,Palleschi V.J.Anal.At.Spectrom.,2006,21(7):697-702
12 Zhang T,Liang L,Wang K,Tang H,Yang X,Duan Y,Li H.J.Anal.At.Spectrom.,2014,29(12):2323-2329
13 Multari R A,Cremers D A,Dupre J A,Gustafson J E.J.Agri.Food Chem.,2013,61(36):8687-8694
14 Li X,Wang Z,Fu Y.Appl.Spectrosc.,2014,68(9):955-963
15 Vapnik V,Chapelle O.Neural Comput.,2000,12(9):2013-2036
16 Gandhi A B,Joshi J B,Jayaraman V K.Chem.Eng.Sci.,2007,62(24):7078-7089
17 Chang C C,Lin C J.Acm Trans.Intel.Syst.Tec.,2011,2(3):1-27