X凹陷P层组烃源岩测井评价方法研究
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摘要
烃源岩评价是含油气盆地油气资源评价的重要基础,但烃源岩样品的地化测试受样品来源、分布及测试费用等因素的共同限制,难以满足勘探的需求。系统阐述了烃源岩总有机碳含量(TOC)的测井定量评价方法,并对X凹陷P层组的一口取心井的TOC数据进行分析。首先描述了单因素法、PCA法、多元回归分析法以及神经网络法计算TOC含量的优缺点,其次结合研究区的取心样品数据以及研究区的地质特点,对4种模型计算的效果进行分析,TOC计算结果表明:神经网络模型效果最好,其次是PCA模型,再次是多元回归模型和单因素模型;计算平均均方根误差分别为2.08%,14.59%和15.23%。最终确定神经网络模型对研究区烃源岩TOC计算的效果更好。
Identification and evaluation of source rock is on the basis of geological study for hydrocarbon. But geochemical testing of source rock samples is limited by the source, distribution and testing cost of samples. It is difficult to meet the requirements of exploration. This paper systematically expounds the logging quantitative evaluation method of TOC in source rocks. It also analyses the calculation results in a coring well of P formation in X sag. Firstly, the advantages and disadvantages of single factor method, PCA method, multiple regression analysis method and neural network method for calculating TOC content are described. Secondly, it is combined with the core sample data of the study area and the geological characteristics of the study area. The calculation results of the four models are analyzed. The results show that the neural network model has the best effect, and the average root mean square error is 2.08%, followed by PCA model. The average root mean square errors are 14.59% and 15.23% respectively. It is proved that the TOC content calculated by the neural network model is more effective.
Identification and evaluation of source rock is on the basis of geological study for hydrocarbon. But geochemical testing of source rock samples is limited by the source, distribution and testing cost of samples. It is difficult to meet the requirements of exploration. This paper systematically expounds the logging quantitative evaluation method of TOC in source rocks. It also analyses the calculation results in a coring well of P formation in X sag. Firstly, the advantages and disadvantages of single factor method, PCA method, multiple regression analysis method and neural network method for calculating TOC content are described. Secondly, it is combined with the core sample data of the study area and the geological characteristics of the study area. The calculation results of the four models are analyzed. The results show that the neural network model has the best effect, and the average root mean square error is 2.08%, followed by PCA model. The average root mean square errors are 14.59% and 15.23% respectively. It is proved that the TOC content calculated by the neural network model is more effective.
引文
[1] 吴景富,杨树春,张功成,等.梁建设南海北部深水区盆地热历史及烃源岩热演化研究[J].地球物理学报,2013,56(1):170-180.
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[10] 邓虎成,周文,张娟.塔中地区奥陶系烃源岩测井定量识别评价[J].物探化探计算技术,2010,32(4):380-385.
[11] 徐思煌,朱义清.烃源岩有机碳含量的测井响应特征与定量预测模型——以珠江口盆地文昌组烃源岩为例[J].石油实验地质,2010,32(3):290-295.
[12] 陈小蓉,孙宝生,姜涛.测井技术在评价准噶尔盆地吉木萨尔凹陷烃源岩中的应用[J].四川地质学报,2011,31(1):105-108.
[13] 胡慧婷,卢双舫,刘超,等.测井资料计算源岩有机碳含量模型对比及分析[J].沉积学报,2011,29(6):1199-1205.
[14] 印长海,刘超.利用变系数△logR技术评价断陷地层烃源岩[J].测井技术,2016,40(1):104-107.
[15] 霍秋立,曾花森,付丽.△logR测井源岩评价方法的改进及其在松辽盆地的应用.吉林大学学报(地球科学版),2011,41(2):586-591.
[16] 韩科龙,夏冬冬,胡庆辉.基于主成分分析的烃源岩测井评价技术[J].中国矿业,2018,27(1):334-337.
[17] 郭龙,陈践发,苗中英.一种新的TOC含量拟合方法研究与应用[J].天然气地球科学,2009,20(6):951-956.
[18] Kamali M R,Mirshady A A.Total organic carbon content determined from well logs using △logR and Neuro Fuzzy techniques[J].Journal of Petroleum Science and Engineering,2004,45(3-4):141-148.
[2] Passey Q R,Creaney S,Kulla J B,et al.A practical model for organic richness from porosity and resistivity logs[J].AAPG Bulletin,1990,74(12):1777-1794.
[3] Schmoker J W.Determination of organic-matter content of appalachian devonian shales from gamma-ray logs [J].AAPG Bulletin,1981,65(7):1285-1298.
[4] Herron S L.A total organic carbon log for source rock evaluation[J].The Log Analyst,1987,28(6):50-58.
[5] 赵彦超,马正,姚光庆.碳氧比测井—一种潜在的生油岩评价工具[J].测井技术,1994(4):240-247.
[6] 朱光有,金强,张林晔.用测井信息获取烃源岩的地球化学参数研究[J].测井技术,2003,27(2):104-109.
[7] Mendelzon J D,Toksoz M N.Source rock characterization using multivariate analysis of log data[C]//SPWLA 26th Annual Logging Symposium.Dallas,Texas:Sociaty of Petrophysicists and Well-Log Analysts,1985.
[8] 岳炳顺,黄华,陈彬,等.东濮凹陷测井烃源岩评价方法及应用[J].石油天然气学报,2005,27(3):351-354.
[9] 黄晨,潘保芝.利用测井资料识别与评价大庆外围盆地烃源岩[J].吉林大学学报(地球科学版),2006,36(s2):162-165.
[10] 邓虎成,周文,张娟.塔中地区奥陶系烃源岩测井定量识别评价[J].物探化探计算技术,2010,32(4):380-385.
[11] 徐思煌,朱义清.烃源岩有机碳含量的测井响应特征与定量预测模型——以珠江口盆地文昌组烃源岩为例[J].石油实验地质,2010,32(3):290-295.
[12] 陈小蓉,孙宝生,姜涛.测井技术在评价准噶尔盆地吉木萨尔凹陷烃源岩中的应用[J].四川地质学报,2011,31(1):105-108.
[13] 胡慧婷,卢双舫,刘超,等.测井资料计算源岩有机碳含量模型对比及分析[J].沉积学报,2011,29(6):1199-1205.
[14] 印长海,刘超.利用变系数△logR技术评价断陷地层烃源岩[J].测井技术,2016,40(1):104-107.
[15] 霍秋立,曾花森,付丽.△logR测井源岩评价方法的改进及其在松辽盆地的应用.吉林大学学报(地球科学版),2011,41(2):586-591.
[16] 韩科龙,夏冬冬,胡庆辉.基于主成分分析的烃源岩测井评价技术[J].中国矿业,2018,27(1):334-337.
[17] 郭龙,陈践发,苗中英.一种新的TOC含量拟合方法研究与应用[J].天然气地球科学,2009,20(6):951-956.
[18] Kamali M R,Mirshady A A.Total organic carbon content determined from well logs using △logR and Neuro Fuzzy techniques[J].Journal of Petroleum Science and Engineering,2004,45(3-4):141-148.