基于贝叶斯正则化改进BP神经网络的页岩气有机碳含量预测模型
|
摘要
页岩气总有机碳(TOC)含量是评价岩性气藏的关键指标,受复杂地质及岩心采集等多种因素的影响,常规室内测试分析获得的TOC含量的数据有限且结果有失准确。为合理准确预测页岩气TOC含量,本文首先通过对页岩气储层TOC含量测井资料综合分析选取8条测井曲线,并结合主成分分析法(Principal Component Analysis,PCA)提取四个主成分;其次基于贝叶斯正则化(Bayesian Regularization)改进的BP神经网络方法建立页岩气TOC含量预测的BR-BP模型;最后利用该模型对研究区A区页岩气TOC含量进行预测,并与常规的LM-BP神经网络模型的预测结果进行对比。结果表明:BRBP模型有较强的非线性拟合能力,能够真实地反映出页岩气TOC含量与各测井参数之间的非线性关系,其模型预测结果与实际值基本吻合,与常规的LM-BP神经网络模型相比,其数据敏感性增强,预测精度有所提高,该研究方法具有一定的理论意义和参考价值,为我国TOC含量预测提供了一种新的技术方法和手段。
Total organic carbon( TOC) content in shale gas is a key indicator for evaluating lithologic gas reservoirs. The data of this parameter from conventional laboratory analysis are limited in amount with poor accuracy owing to many factors such as complex geology and core recovery. This work attempted to solve this problem. We selects eight logging curves by comprehensive analysis of logging data of TOC content in shale gas reservoirs and four principal components were extracted by Principal Component Analysis( PCA) from these curves. Then,a BR-BP model was established to predict TOC content in shale gas based on improved BP neural network with Bayesian regularization. Finally,the model was used to predict the TOC content of shale gas in the area A under the study,and compared with the prediction results by the conventional LM-BP neural network model. The results show that the BR-BP model has strong nonlinear fitting ability which can truly reflect the nonlinear relationship between the TOC content of shale gas and each logging parameter and the model prediction largely accords with the actual values. Compared with the conventional LM-BP neural network,the data sensitivity of this model is enhanced and the prediction accuracy is improved. This research method has certain theoretical significance and reference value,which provides a new technique for the prediction of TOC content in hydrocarbon exploration.
Total organic carbon( TOC) content in shale gas is a key indicator for evaluating lithologic gas reservoirs. The data of this parameter from conventional laboratory analysis are limited in amount with poor accuracy owing to many factors such as complex geology and core recovery. This work attempted to solve this problem. We selects eight logging curves by comprehensive analysis of logging data of TOC content in shale gas reservoirs and four principal components were extracted by Principal Component Analysis( PCA) from these curves. Then,a BR-BP model was established to predict TOC content in shale gas based on improved BP neural network with Bayesian regularization. Finally,the model was used to predict the TOC content of shale gas in the area A under the study,and compared with the prediction results by the conventional LM-BP neural network model. The results show that the BR-BP model has strong nonlinear fitting ability which can truly reflect the nonlinear relationship between the TOC content of shale gas and each logging parameter and the model prediction largely accords with the actual values. Compared with the conventional LM-BP neural network,the data sensitivity of this model is enhanced and the prediction accuracy is improved. This research method has certain theoretical significance and reference value,which provides a new technique for the prediction of TOC content in hydrocarbon exploration.
引文
Cui Rongguo,Chen Qishen,Guo Juan,Guo Zhenhua,Xiao Yuping.2018.Current situation of consumption of renewable energy across the world[J].Geology and Exploration,54(6):1135-1140(in Chinese with English abstract)．
Hao Jianfei,Zhou Cancan,Li Xia,Cheng Xiangzhi,Li Chaoliu,Song Lianteng.2012.Summary if shale gas evaluation applying geophysica logging[J].Progress in Geophysics,27(4):1624-1632(in Chinese with English abstract)．
Hu Xi,Wang Xingzhi,Li Yizhen,Feng Mingyou,Wang Juebo.2016.Using log data to calculate the organic matter abundance in shale:Acase study from Longmaxi Formation in Changning area,southern Sichuan Basin[J].Lithologic Reservoirs,28(5):107-112(in Chinese with English abstract)．
Li Hongxia,Xu Shiguo,Fan Chuiren.2006.Long-term prediction o runoff based on Bayesian regulation neural network.[J].Journal o Dalian University of Technology,46(z1):174-177(in Chinese with English abstract)．
Li Shengjie,Cui Zhe,Jiang Zhenxue,Shao Yu,Liao Wei,Li Li.2016.New method for prediction of shale gas content in continental shale formation using well logs[J].Applied Geophysics,13(2):393-405．
Li Tao,Pan Yun,Lou Huajun,Li Bo,Wang Hong,Zou Lizhi.2005.Application of the artificial neural network in land subsidence prediction in the urban area of Tianjin municipality,China[J].Geological Bulletin of China,24(7):677-681(in Chinese with English abstract)．
Li Wuguang,Yang Shenglai,Xu Jing,Dong Qian.2012.A new model for shale adsorptive gas amount under a certain geological conditions of temperature and pressure[J].Natural Gas Geoscience,23(4):791-796(in Chinese with English abstract)．
Li Zhiqiang,Xu Qiang,Li Shu,Kou Pinglang,Zhang Xianlin.2017.Study on water-rock/soil interaction in loess irrigation area based on the principal component analysis[J].Science Technology and Engineering,23:161-167(in Chinese with English abstract)．
Liu Yi,Lu Zhengyuan,LüJing,Xie Runcheng.2017.Application of principal component analysis method in lithology identification for shale formation[J].Fault-Block Oil and Gas Field,24(3):360-363(in Chinese with English abstract)．
Liu Zhigang,Xiao Dianshi,Xu Shaohua.2017.Total organic carbon content prediction of shale reservoirs based on discrete process neural network[J].Journal of China University of Petroleum(Edition of Natural Science),41(2):80-87(in Chinese with English abstract)．
Luo Zhenhua,Zhong Mengfan,Zhang Nian,Lou Ming,Pan Haize,He Jian,Li Hu.2018.Integrated assessment of environmental impacts from shale gas development based on the mutational progression method[J].Geology and Exploration,54(1):174-182(in Chinese with English abstract)．
Meng Zhaoping,Guo Yansheng,Liu Wei.2015.Relationship between organic carbon content of shale gas reservoir and logging parameters and its prediction model[J].Journal of China Coal Society,40(2):247-253(in Chinese with English abstract)．
Qu Yansheng,Zhong Ningning,Liu Yan,Li Yuanyuan,Peng Bo.2011.Using logging methods to calculate organic matter abundance of source rocks and its influencing factors[J].Lithologic Reservoirs,23(2):80-84(in Chinese with English abstract)．
Schmoker.1981.Determination of organic-matter content of appalachian devonian shales from gamma-ray logs[J].AAPG Bulletin,65(7):1285-1298．
Shen Jianbo,Lei Xiangdong,Li Yutang,Lan Ying.2018.Prediction mean height for larix olgensis plantation based on Bayesian regularization BP neural network[J].Journal of Nanjing Forestry University(Natural Science Edition),42(02):147-154(in Chinese with English abstract)．
Tan Maojin,Zhang Songyang.2010.Progress in geophysieal logging technology for shale gas researvoirs[J].Progress in Geophysics,25(6):2024-2030(in Chinese with English abstract)．
Tan Maojin,Liu Qiong,Zhang Songyang.2013.A dynamic adaptive radial basis function approach for total organic carbon content prediction in organic shale[J].Geophysics,78(6):445-459(in Chinese with English abstract)．
Wang Guiwei,Zhu Zhenyu,Zhu Guangyu.2002.Logging identification and evaluation of Cambrian-Ordovician source rocks in syneclise of Tarim basin[J].Petroleum Exploration and Development,29(4):50-52(in Chinese with English abstract)．
Wang Jianguo,Li Zhonggang,Zhu Zhi,Song Lie.2015.Calculation of the shale TOC extents based on the well logging methods[J].Prtroleum Geology and Oilfield Development in Daqing,34(3):170-174(in Chinese with English abstract)．
Wang Zhuyue,Ding Wenlong,Wang Zhe,Li Ang,He Jianhua,Yin Shuai.2015.Progress of geophysical well logging in shale gas reservoir evaluation[J].Progress in Geophysics,30(1):228-241(in Chinese with English abstract)．
Wei Dong,Zhang Minglian,Jiang Zhijian,Sun Ming.2005.Neural network non-linear modelling based on Bayesian methods[J].Computer Engineering and Applications,41(11):5-8(in Chinese with English abstract)．
Xiao Dongfeng,Yang Chunjie,Song Zhihuan.2005.The forecasting model of blast furnace gas output based on improved BP network[J].Tournal of Zhejiang University(Engineering Science),24(7):677-681(in Chinese with English abstract)．
Xing Lei,Zhang Chaomo,Zhang Chong,Xie Bing,Ding Yi,Han Shumin.2014.Research on logging evaluation method of TOC content of shale gas reservoir in A area[J].Lithologic Reservoirs,26(3):74-78(in Chinese with English abstract)．
Yang Zhihao,Li Zhiping.2017.A new method for choice of water-control fracturing segments in horizontal wells based on the BP neural network system[J].Geology and Exploration,53(4):818-824(in Chinese with English abstract)．
Yu Shenghua,Deng Juan.2011.GDP Prediction based on principal component analysis and Bayesian regularization BP neural network[J].Journal of Hunan University(Social Sciences),25(6):42-45(in Chinese with English abstract)．
Zhang Han,Lu Shuangfang,Li Wen-hao,Tian Weichao,Hu Ying,He Taohua,Tan Zhaozhao.2017.Application of△LogR technology and BP neural network in organic evaluation in the complex lithology tight stratum[J].Progress in Geophysics,32(3):1308-1313(in Chinese with English abstract)．
Zhao Xingqi,Chen Jianfa,Guo Wang,Liu Gaozhi,Chen Feiran,Zhang Wen.2013.The application of BP network to the source rocks evaluation in Xihu sag[J].Well Logging Technology,37(5):567-571(in Chinese with English abstract)．
Zhong Yihua,Li Rong.2009.Application of principal component analysis and least square support vector machine to lithology identification[J].Well Logging Technology,33(5):425-429(in Chinese with English abstract)．
Zhu Guangyou,Jin Qiang,Zhang Linye.2003.Using log information to analyze the geochemical characteristics of source rocks in Jiyang depression[J].Well Logging Technology,27(2):104-109,146(in Chinese with English abstract)．
Zhu Zhenyu,Wang Guiwen,Zhu Guangyu.2002.The application of artificial neural network to the source rock’s evaluation[J].Progress in Geophysics,17(1):137-140(in Chinese with English abstract)．
崔荣国，陈其慎，郭娟，郭振华，肖宇评．2018．全球可再生能源消费现状分析[J]．地质与勘探，54(6):1135-1140．
郝建飞，周灿灿，李霞，程相志，李潮流，宋连腾．2012．页岩气地球物理测井评价综述[J]．地球物理学进展，27(4):1624-1632．
胡曦，王兴志，李宜真，冯明友，王珏博．2016．利用测井信息计算页岩有机质丰度---以川南长宁地区龙马溪组为例[J]．岩性油气藏，28(5):107-112．
李红霞，许士国，范垂仁．2006．基于贝叶斯正则化神经网络的径流长期预报[J]．大连理工大学学报，46(z1):174-177．
李涛，潘云，娄华君，李波，王宏，邹立芝．2005．人工神经网络在天津市区地面沉降预测中的应用[J]．地质通报，24(7):677-681．
李武广，杨胜来，徐晶，董谦．2012．考虑地层温度和压力的页岩吸附气含量计算新模型[J]．天然气地球科学，23(4):791-796．
李志强，许强，李姝，寇平浪，张先林．2017．按主成分分析法研究黄土灌溉区水-岩(土)相互作用[J]．科学技术与工程，23:161-167．
刘毅，陆正元，吕晶，谢润成．2017．主成分分析法在泥页岩地层岩性识别中的应用[J]．断块油气田，24(3):360-363．
刘志刚，肖佃师，许少华．2017．基于离散过程神经网络页岩油气储层有机碳含量预测[J]．中国石油大学学报:自然科学版，41(2):80-87．
罗振华，钟蒙繁，张念，娄明，潘海泽，贺建，李虎．2018．基于突变级数法的页岩气探采综合环境影响评价研究[J]．地质与勘探，54(1):174-182．
孟召平，郭彦省，刘尉．2015．页岩气储层有机碳含量与测井参数的关系及预测模型[J]．煤炭学报，40(2):247-253．
曲彦胜，钟宁宁，刘岩，李园园，彭波．2011．烃源岩有机质丰度的测井计算方法及影响因素探讨[J]．岩性油气藏，23(2):80-84．
沈剑波，雷相东，李玉堂，兰莹．2018．基于贝叶斯正则化BP神经网络的长白落叶松人工林平均高预测[J]．南京林业大学学报(自然科学版)，42(2):147-154．
谭茂金，张松扬．2010．页岩气储层地球物理测井研究进展[J]．地球物理学进展，25(6):2024-2030．
王贵文，朱振宇，朱广宇．2002．烃源岩测井识别与评价方法研究[J]．石油勘探与开发，29(4):50-52．
王建国，李忠刚，朱智，宋磊．2015．基于测井方法的页岩有机碳含量计算[J]．大庆石油地质与开发，34(3):170-174．
王濡岳，丁文龙，王哲，李昂，何建华，尹帅．2015．页岩气储层地球物理测井评价研究现状[J]．地球物理学进展，30(1):228-241．
魏东，张明廉，蒋志坚，孙明．2005．基于贝叶斯方法的神经网络非线性模型辨识[J]．计算机工程与应用，41(11):5-8．
肖冬峰，杨春节，宋执环．2005．基于改进BP网络的高炉煤气发生量预测模型[J]．浙江大学学报(工学版)，46(11):2103-2108．
熊镭，张超谟，张冲，谢冰，丁一，韩淑敏．2014.A地区页岩气储层总有机碳含量测井评价方法研究[J]．岩性油气藏，26(3):74-78．
杨志浩，李治平．2017．基于BP神经网络的底水油藏控水压裂选段新方法[J]．地质与勘探，53(4):818-824．
喻胜华，邓娟．2011．基于主成分分析和贝叶斯正则化BP神经网络的GDP预测[J]．湖南大学学报(社会科学版)，25(6):42-45．
张晗，卢双舫，李文浩，田伟超，胡莹，何涛华，谭昭昭．2017．ΔLogR技术与BP神经网络在复杂岩性致密层有机质评价中的应用[J]．地球物理学进展，32(03):1308-1313．
赵兴齐，陈践发，郭望，刘高志，陈斐然，张文．2013.BP神经网络在西湖凹陷烃源岩评价中的应用[J]．测井技术，37(5):567-571．
钟仪华，李榕．2009．基于主成分分析的最小二乘支持向量机岩性识别方法[J]．测井技术，33(5):425-429．
朱光有，金强，张林晔．2003．用测井信息获取烃源岩的地球化学参数研究[J]．测井技术，27(2):104-109．
朱振宇，王贵文，朱广宇．2002．人工神经网络法在烃源岩测井评价中的应用[J]．地球物理学进展，17(1):137-140.
Hao Jianfei,Zhou Cancan,Li Xia,Cheng Xiangzhi,Li Chaoliu,Song Lianteng.2012.Summary if shale gas evaluation applying geophysica logging[J].Progress in Geophysics,27(4):1624-1632(in Chinese with English abstract)．
Hu Xi,Wang Xingzhi,Li Yizhen,Feng Mingyou,Wang Juebo.2016.Using log data to calculate the organic matter abundance in shale:Acase study from Longmaxi Formation in Changning area,southern Sichuan Basin[J].Lithologic Reservoirs,28(5):107-112(in Chinese with English abstract)．
Li Hongxia,Xu Shiguo,Fan Chuiren.2006.Long-term prediction o runoff based on Bayesian regulation neural network.[J].Journal o Dalian University of Technology,46(z1):174-177(in Chinese with English abstract)．
Li Shengjie,Cui Zhe,Jiang Zhenxue,Shao Yu,Liao Wei,Li Li.2016.New method for prediction of shale gas content in continental shale formation using well logs[J].Applied Geophysics,13(2):393-405．
Li Tao,Pan Yun,Lou Huajun,Li Bo,Wang Hong,Zou Lizhi.2005.Application of the artificial neural network in land subsidence prediction in the urban area of Tianjin municipality,China[J].Geological Bulletin of China,24(7):677-681(in Chinese with English abstract)．
Li Wuguang,Yang Shenglai,Xu Jing,Dong Qian.2012.A new model for shale adsorptive gas amount under a certain geological conditions of temperature and pressure[J].Natural Gas Geoscience,23(4):791-796(in Chinese with English abstract)．
Li Zhiqiang,Xu Qiang,Li Shu,Kou Pinglang,Zhang Xianlin.2017.Study on water-rock/soil interaction in loess irrigation area based on the principal component analysis[J].Science Technology and Engineering,23:161-167(in Chinese with English abstract)．
Liu Yi,Lu Zhengyuan,LüJing,Xie Runcheng.2017.Application of principal component analysis method in lithology identification for shale formation[J].Fault-Block Oil and Gas Field,24(3):360-363(in Chinese with English abstract)．
Liu Zhigang,Xiao Dianshi,Xu Shaohua.2017.Total organic carbon content prediction of shale reservoirs based on discrete process neural network[J].Journal of China University of Petroleum(Edition of Natural Science),41(2):80-87(in Chinese with English abstract)．
Luo Zhenhua,Zhong Mengfan,Zhang Nian,Lou Ming,Pan Haize,He Jian,Li Hu.2018.Integrated assessment of environmental impacts from shale gas development based on the mutational progression method[J].Geology and Exploration,54(1):174-182(in Chinese with English abstract)．
Meng Zhaoping,Guo Yansheng,Liu Wei.2015.Relationship between organic carbon content of shale gas reservoir and logging parameters and its prediction model[J].Journal of China Coal Society,40(2):247-253(in Chinese with English abstract)．
Qu Yansheng,Zhong Ningning,Liu Yan,Li Yuanyuan,Peng Bo.2011.Using logging methods to calculate organic matter abundance of source rocks and its influencing factors[J].Lithologic Reservoirs,23(2):80-84(in Chinese with English abstract)．
Schmoker.1981.Determination of organic-matter content of appalachian devonian shales from gamma-ray logs[J].AAPG Bulletin,65(7):1285-1298．
Shen Jianbo,Lei Xiangdong,Li Yutang,Lan Ying.2018.Prediction mean height for larix olgensis plantation based on Bayesian regularization BP neural network[J].Journal of Nanjing Forestry University(Natural Science Edition),42(02):147-154(in Chinese with English abstract)．
Tan Maojin,Zhang Songyang.2010.Progress in geophysieal logging technology for shale gas researvoirs[J].Progress in Geophysics,25(6):2024-2030(in Chinese with English abstract)．
Tan Maojin,Liu Qiong,Zhang Songyang.2013.A dynamic adaptive radial basis function approach for total organic carbon content prediction in organic shale[J].Geophysics,78(6):445-459(in Chinese with English abstract)．
Wang Guiwei,Zhu Zhenyu,Zhu Guangyu.2002.Logging identification and evaluation of Cambrian-Ordovician source rocks in syneclise of Tarim basin[J].Petroleum Exploration and Development,29(4):50-52(in Chinese with English abstract)．
Wang Jianguo,Li Zhonggang,Zhu Zhi,Song Lie.2015.Calculation of the shale TOC extents based on the well logging methods[J].Prtroleum Geology and Oilfield Development in Daqing,34(3):170-174(in Chinese with English abstract)．
Wang Zhuyue,Ding Wenlong,Wang Zhe,Li Ang,He Jianhua,Yin Shuai.2015.Progress of geophysical well logging in shale gas reservoir evaluation[J].Progress in Geophysics,30(1):228-241(in Chinese with English abstract)．
Wei Dong,Zhang Minglian,Jiang Zhijian,Sun Ming.2005.Neural network non-linear modelling based on Bayesian methods[J].Computer Engineering and Applications,41(11):5-8(in Chinese with English abstract)．
Xiao Dongfeng,Yang Chunjie,Song Zhihuan.2005.The forecasting model of blast furnace gas output based on improved BP network[J].Tournal of Zhejiang University(Engineering Science),24(7):677-681(in Chinese with English abstract)．
Xing Lei,Zhang Chaomo,Zhang Chong,Xie Bing,Ding Yi,Han Shumin.2014.Research on logging evaluation method of TOC content of shale gas reservoir in A area[J].Lithologic Reservoirs,26(3):74-78(in Chinese with English abstract)．
Yang Zhihao,Li Zhiping.2017.A new method for choice of water-control fracturing segments in horizontal wells based on the BP neural network system[J].Geology and Exploration,53(4):818-824(in Chinese with English abstract)．
Yu Shenghua,Deng Juan.2011.GDP Prediction based on principal component analysis and Bayesian regularization BP neural network[J].Journal of Hunan University(Social Sciences),25(6):42-45(in Chinese with English abstract)．
Zhang Han,Lu Shuangfang,Li Wen-hao,Tian Weichao,Hu Ying,He Taohua,Tan Zhaozhao.2017.Application of△LogR technology and BP neural network in organic evaluation in the complex lithology tight stratum[J].Progress in Geophysics,32(3):1308-1313(in Chinese with English abstract)．
Zhao Xingqi,Chen Jianfa,Guo Wang,Liu Gaozhi,Chen Feiran,Zhang Wen.2013.The application of BP network to the source rocks evaluation in Xihu sag[J].Well Logging Technology,37(5):567-571(in Chinese with English abstract)．
Zhong Yihua,Li Rong.2009.Application of principal component analysis and least square support vector machine to lithology identification[J].Well Logging Technology,33(5):425-429(in Chinese with English abstract)．
Zhu Guangyou,Jin Qiang,Zhang Linye.2003.Using log information to analyze the geochemical characteristics of source rocks in Jiyang depression[J].Well Logging Technology,27(2):104-109,146(in Chinese with English abstract)．
Zhu Zhenyu,Wang Guiwen,Zhu Guangyu.2002.The application of artificial neural network to the source rock’s evaluation[J].Progress in Geophysics,17(1):137-140(in Chinese with English abstract)．
崔荣国，陈其慎，郭娟，郭振华，肖宇评．2018．全球可再生能源消费现状分析[J]．地质与勘探，54(6):1135-1140．
郝建飞，周灿灿，李霞，程相志，李潮流，宋连腾．2012．页岩气地球物理测井评价综述[J]．地球物理学进展，27(4):1624-1632．
胡曦，王兴志，李宜真，冯明友，王珏博．2016．利用测井信息计算页岩有机质丰度---以川南长宁地区龙马溪组为例[J]．岩性油气藏，28(5):107-112．
李红霞，许士国，范垂仁．2006．基于贝叶斯正则化神经网络的径流长期预报[J]．大连理工大学学报，46(z1):174-177．
李涛，潘云，娄华君，李波，王宏，邹立芝．2005．人工神经网络在天津市区地面沉降预测中的应用[J]．地质通报，24(7):677-681．
李武广，杨胜来，徐晶，董谦．2012．考虑地层温度和压力的页岩吸附气含量计算新模型[J]．天然气地球科学，23(4):791-796．
李志强，许强，李姝，寇平浪，张先林．2017．按主成分分析法研究黄土灌溉区水-岩(土)相互作用[J]．科学技术与工程，23:161-167．
刘毅，陆正元，吕晶，谢润成．2017．主成分分析法在泥页岩地层岩性识别中的应用[J]．断块油气田，24(3):360-363．
刘志刚，肖佃师，许少华．2017．基于离散过程神经网络页岩油气储层有机碳含量预测[J]．中国石油大学学报:自然科学版，41(2):80-87．
罗振华，钟蒙繁，张念，娄明，潘海泽，贺建，李虎．2018．基于突变级数法的页岩气探采综合环境影响评价研究[J]．地质与勘探，54(1):174-182．
孟召平，郭彦省，刘尉．2015．页岩气储层有机碳含量与测井参数的关系及预测模型[J]．煤炭学报，40(2):247-253．
曲彦胜，钟宁宁，刘岩，李园园，彭波．2011．烃源岩有机质丰度的测井计算方法及影响因素探讨[J]．岩性油气藏，23(2):80-84．
沈剑波，雷相东，李玉堂，兰莹．2018．基于贝叶斯正则化BP神经网络的长白落叶松人工林平均高预测[J]．南京林业大学学报(自然科学版)，42(2):147-154．
谭茂金，张松扬．2010．页岩气储层地球物理测井研究进展[J]．地球物理学进展，25(6):2024-2030．
王贵文，朱振宇，朱广宇．2002．烃源岩测井识别与评价方法研究[J]．石油勘探与开发，29(4):50-52．
王建国，李忠刚，朱智，宋磊．2015．基于测井方法的页岩有机碳含量计算[J]．大庆石油地质与开发，34(3):170-174．
王濡岳，丁文龙，王哲，李昂，何建华，尹帅．2015．页岩气储层地球物理测井评价研究现状[J]．地球物理学进展，30(1):228-241．
魏东，张明廉，蒋志坚，孙明．2005．基于贝叶斯方法的神经网络非线性模型辨识[J]．计算机工程与应用，41(11):5-8．
肖冬峰，杨春节，宋执环．2005．基于改进BP网络的高炉煤气发生量预测模型[J]．浙江大学学报(工学版)，46(11):2103-2108．
熊镭，张超谟，张冲，谢冰，丁一，韩淑敏．2014.A地区页岩气储层总有机碳含量测井评价方法研究[J]．岩性油气藏，26(3):74-78．
杨志浩，李治平．2017．基于BP神经网络的底水油藏控水压裂选段新方法[J]．地质与勘探，53(4):818-824．
喻胜华，邓娟．2011．基于主成分分析和贝叶斯正则化BP神经网络的GDP预测[J]．湖南大学学报(社会科学版)，25(6):42-45．
张晗，卢双舫，李文浩，田伟超，胡莹，何涛华，谭昭昭．2017．ΔLogR技术与BP神经网络在复杂岩性致密层有机质评价中的应用[J]．地球物理学进展，32(03):1308-1313．
赵兴齐，陈践发，郭望，刘高志，陈斐然，张文．2013.BP神经网络在西湖凹陷烃源岩评价中的应用[J]．测井技术，37(5):567-571．
钟仪华，李榕．2009．基于主成分分析的最小二乘支持向量机岩性识别方法[J]．测井技术，33(5):425-429．
朱光有，金强，张林晔．2003．用测井信息获取烃源岩的地球化学参数研究[J]．测井技术，27(2):104-109．
朱振宇，王贵文，朱广宇．2002．人工神经网络法在烃源岩测井评价中的应用[J]．地球物理学进展，17(1):137-140.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |