Geophysical quantitative prediction of TOC content in source rocks of Madingo Formation in Block A,Lower Congo Basin
Ji Shaocong1,2, Yang Xianghua1,2, Zhu Hongtao1,2, Deng Yunhua3, Kang Hongquan3, Wang Bo1,2
1. Faculty of Earth Resources, China University of Geosciences(Wuhan), Wuhan, Hubei 430074, China; 2. Key Laboratory of Tectonics and Petroleum Resources of the Ministry of Education, China University of Geosciences(Wuhan), Wuhan, Hubei 430074, China; 3. CNOOC Research Institute Co. Ltd., Beijing 100027, China
Abstract:TOC content is an important content of evaluation of organic matter abundance and hydrocarbon generation potential.There are few samples available for testing TOC content in source rocks of Madingo Formation in Block A, Lower Congo Basin due to oil-based mud pollution.And the distribution of these samples is nonuniform, so it is difficult to carry out quantitative evaluation.Geophysical data contain a variety of geochemical information in source rocks and can be used to quantitatively and effectively predict TOC content.Based on measured TOC content and logging data in study area, we find out some logging parameters with good correlations with TOC by cross-plot analysis.The predicted TOC content was calculated by multiple regression analysis, BP neural network and modified ΔlgR.And the best algorithm for single well TOC content prediction based on logging data is selected.A neural network model near the well is established based on the relation between the predicted TOC content of single well and seismic attributes on 3D seismic data, and this model is used to calculate a TOC data volume.The results show that well logging parameters with good correlations with measured TOC include density, natural gamma and interval transit time.BP neural network method has the best prediction and the correlation coefficient between predicted TOC content and measured TOC content of single well is up to 0.9542.
陈建平,梁狄刚,张水昌等.中国古生界海相烃源岩生烃潜力评价标准与方法.地质学报,2012,86(7):1132-1142.Chen Jianping,Liang Digang,Zhang Shuichang et al.Evaluation criterion and methods of the hydrocarbon generation potential for China's Paleozoic marine source rocks.Acta Geological Sinica,2012,86(7):1132-1142.
[2]
朱光有,金强.烃源岩的非均质性研究——以东营凹陷牛38井为例.石油学报,2002,23(5):34-39.Zhu Guangyou,Jin Qiang.Study on source rock heterogeneity:A case of Niu-38 Well in Dongying depression.Acta Petrolei Sinica,2002,23(5):34-39.
[3]
杨涛涛,范国章,吕福亮等.烃源岩测井响应特征及识别评价方法.天然气地球科学,2013,24(2):414-422.Yang Taotao,Fan Guozhang,Lv Fuliang et al.The logging features and identification methods of source rock.Natural Gas Geosciences,2013,24(2):414-422.
[4]
Schmoker J W.Determination of organic-matter content of Appalachian Devonian shales from gamma-ray logs.AAPG Bulletin,1981,65(7):1285-1298.
[5]
Schmoker J W.Determination of organic content of Appalachian Devonian shales from formation-density logs.AAPG Bulletin,1979,63(9):1505-1537.
[6]
王健,石万忠,舒志国等.富有机质页岩TOC含量的地球物理定量化预测.石油地球物理勘探,2016,51(3):596-604.Wang Jian,Shi Wanzhong,Shu Zhiguo et al.TOC content quantitative prediction in organic-rich shale.OGP,2016,51(3):596-604.
[7]
李松峰,毕建霞,曾正清等.普光地区须家河组烃源岩地球物理预测.断块油气田,2015,22(6):705-710.Li Songfeng,Bi Jianxia,Zeng Zhengqing et al.Geophysical prediction of Xujiahe Formation source rock in Puguang Area.Fault-Block Oil & Gas Field,2015,22(6):705-710.
[8]
黄薇,张小莉,李浩等.鄂尔多斯盆地中南部延长组7段页岩有机碳含量解释模型.石油学报,2015,36(12):1508-1515.Huang Wei,Zhang Xiaoli,Li Hao et al.Interpretation model of organic carbon content of shale in Member 7 of Yanchang Formation,central-southern Ordos Basin.Acta Petrolei Sinica,2015,36(12):1508-1515.
[9]
Passey Q R,Creaney S,Kulla J B et al.A practical model for organic richness from porosity and resisti-vity logs.AAPG Bulletin,1990,74(12):1777-1794.
[10]
张寒,朱光有.利用地震和测井信息预测和评价烃源岩——以渤海湾盆地富油凹陷为例.石油勘探与开发,2007,34(1):55-59.Zhang Han,Zhu Guangyou.Using seismic and log information to predict and evaluate hydrocarbon source rocks:An example from rich oil depressions in Bohai Bay.Petroleum Exploration and Development,2007,34(1):55-59.
[11]
Huang Z,Williamson M A.Artificial neural network modelling as an aid to source rock characterization.Marine and Petroleum Geology,1996,13(2):277-290.
[12]
孟召平,郭彦省,刘尉.页岩气储层有机碳含量与测井参数的关系及预测模型.煤炭学报,2015,40(2):247-253.Meng Zhaoping,Guo Yansheng,Liu Wei.Relationship between organic carbon content of shale gas reservoir and logging parameters and its prediction model.Journal of China Coal Society,2015,40(2):247-253.
[13]
贺聪,苏奥,张明震等.鄂尔多斯盆地延长组烃源岩有机碳含量测井预测方法优选及应用.天然气地球科学,2016,27(4):754-764.He Cong,Su Ao,Zhang Mingzhen et al.Optimal selection and application of prediction means for organic carbon content of source rocks based on logging data in Yanchang Formation,Ordos Basin.Natural Gas Geosciences,2016,27(4):754-764.
[14]
Løseth H,Wensaas L,Gading M et al.Can hydrocarbon source rocks be identified on seismic data? Geology,2011,39(12):1167-1170.
[15]
刘军,汪瑞良,舒誉等.烃源岩TOC地球物理定量预测新技术及在珠江口盆地的应用.成都理工大学学报(自然科学版),2012,39(4):415-419.Liu Jun,Wang Ruiliang,Shu Yu et al.Geophysical quantitative prediction technology about the total organic carbon in source rocks and application in Pearl River Mouth Basin,China.Journal and Chengdu University of Technology (Science & Technology Edition),2012,39(4):415-419.
[16]
徐新德,陶倩倩,曾少军等.基于地化-测井-地震联合反演的优质烃源岩研究方法及其应用——以涠西南凹陷为例.中国海上油气,2013,25(3):13-18.Xu Xinde,Tao Qianqian,Zeng Shaojun et al.A method to evaluate high-quality source rocks based on geochemistry-logging-seismic joint-inversion and its application:A case of Weixinan sag in Beibuwan basin.China Offshore Oil and Gas,2013,25(3):13-18.
[17]
李松峰,徐思煌,薛罗等.稀井区烃源岩有机碳的地球物理预测方法——珠江口盆地恩平凹陷恩平组烃源岩勘探实例.石油地球物理勘探,2014,49(2):369-374.Li Songfeng,Xu Sihuang,Xue Luo et al.Source-rock organic carbon prediction with geophysical approach in the sparsely-drilled area:A case study of Enping depression,the Pearl Mouth Basin.OGP,2014,49(2):369-374.
[18]
陶倩倩,李达,杨希冰等.利用分频反演技术预测烃源岩.石油地球物理勘探,2015,50(4):706-713.Tao Qianqian,Li Da,Yang Xibing et al.Hydrocarbon source rock prediction with frequency-divided inversion.OGP,2015,50(4):706-713.
[19]
曹军,钟宁宁,邓运华等.下刚果盆地海相烃源岩地球化学特征、成因及其发育的控制因素.地球科学与环境学报,2014,36(4):87-98.Cao Jun,Zhong Ningning,Deng Yunhua et al.Geochemical characteristics,origin and factors controlling formation of marine source rock in lower Congo Basin.Journal of Earth Sciences and Envionment,2014,36(4):87-98.
[20]
Cole G A,Requejo A G,Ormerod D et al.Petroleum geochemical assessment of the lower Congo basin.Petroleum Systems of South Atlantic Margins,AAPG,2000,325-339.
[21]
朱伟林.非洲含油气盆地.北京:科学出版社,2013,205-229.
[22]
刘琼,陶维祥,于水等.西非下刚果-刚果扇盆地圈闭类型和分布特征.地质科技情报,2013,32(3):107-112.Liu Qiong,Tao Weixiang,Yu shui et al.Trap types and distribution of Lower Congo-Congo Fan Basin in West Africa.Geological Science and Technology Information,2013,32(3):107-112.
[23]
郑应钊.西非海岸盆地带油气地质特征与勘探潜力分析[学位论文].北京:中国地质大学(北京),2012,65-78.Zheng Yingzhao.Petroleum Geology Features and Exploration Potential Analysis in the Coastal Basins of West Africa[D].China University of Geosciences (Beijing),Beijing,2012,65-78.
[24]
赵桂萍,李良.杭锦旗地区基于测井响应特征的泥质烃源岩有机质丰度评价研究.石油物探,2016,55(6):879-886.Zhao Guiping,Li Liang.Evaluation on abundance of organic matter for shaly source rocks based on well log responses in Hangjinqi area,Ordos Basin.GPP,2016,55(6):879-886.
[25]
于建国,韩文功,于正军等.济阳拗陷孔店组烃源岩的地震预测方法.石油地球物理勘探,2005,40(3):318-321.Yu Jianguo,Han Wengong,Yu Zhengjun et al.Seismic prediction of Kongdian Group source rock in Jiyang depression.OGP,2005,40(3):318-321.
[26]
牛聪,刘志斌,王彦春等.应用地球物理技术定量评价辽西凹陷沙河街组烃源岩.石油地球物理勘探,2017,52(1):131-137.Niu Cong,Liu Zhibin,Wang Yanchun et al.Quantitative evaluation of Shahejie formation source rocks in Liaoxi Sag with geophysical approaches.OGP,2017,52(1):131-137.
[27]
王贵文,朱振宇,朱广宇.烃源岩测井识别与评价方法研究.石油勘探与开发,2002,29(4):50-52.Wang Guiwen,Zhu Zhenyu,Zhu Guangyu.Logging identification and evaluation of Cambrian-Ordovician source rocks in syneclise of Tarim basin.Petroleum Exploration and Development,2002,29(4):50-52.
[28]
张治国.人工神经网络及其在地学中的应用研究[学位论文].吉林长春:吉林大学,2006,62-74.Zhang Zhiguo.Study on Artificial Neural Networks and Their Applications in Geoscience[D].Jilin University,Changchun,Jilin,2006,62-74.
[29]
严鸿,管燕萍.BP神经网络隐层单元数的确定方法及实例.控制工程,2009,16(增刊2):100-102.Yan Hong,Guan Yanping.Method to determine the quantity of internal nodes of black propagation neural networks and its demonstration.Control Engineering of China,2009,16(S2):100-102.