BP神经网络法在三塘湖盆地芦草沟组页岩岩相识别中的应用
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摘要
对于复杂岩性页岩岩相的识别,传统的建立岩相图版的方法因未充分考虑到测井数据间的相似性造成的干扰以及与岩心实验数据尺度上的差异性,导致建立的识别图版中不同类别的样本点相互重叠、界限模糊,预测偏差较大。针对该问题,以三塘湖盆地马朗凹陷芦草沟组二段为例,在对储层特征充分认识的基础上,采用了一种基于主成分分析的BP神经网络方法,首先分析研究区岩心资料并对其进行归类组合,划分出富有机质纹层相、富碳酸盐纹层相和富凝灰质纹层相3种岩相类型,以便缩小与测井数据间的尺度误差;其次建立岩相图版并提取自然伽马、声波时差、补偿密度、补偿中子、电阻率等5条对岩相变化响应较为敏感的测井曲线,分析各主成分的因子载荷地质因素并优选出3个含有大量岩相信息的主成分PC2,PC3和PC4;最后建立起岩相与测井曲线间的映射关系,同时对研究区重点井芦1井进行了验证性的岩相识别。结果表明,与传统图版识别方法相比,将主成分分析与BP神经网络相结合的岩相识别方法可有效消除测井曲线相似性带来的干扰,解决因岩心数据与测井数据尺度不同所造成的预测偏差增大的问题,使岩相识别正确率得到明显提高。该方法对页岩岩相识别较为实用,具有一定的推广应用价值。
For the identification of shale lithofacies,the traditional method of establishing lithofacies chart does not fully take into account the interference caused by the similarity of logging data and the differences in the scale of experimental data,which results in the overlap of different types of sample points in the established identification chart,the ambiguity of boundaries and the large deviation of prediction. Aiming at this problem,taking thesecond member of Lucaogou Formation in Malang Sag of Santanghu Basin as an example,based on the full understanding of reservoir characteristics,a BP neutral network method based on principal component analysis was adopted. Firstly,the core data of the study area were used to classified the lithofacies into three types,such as organicrich laminar facies,carbonate-rich laminar facies and rich tuff-grain laminar facies,so as to reduce the scale error with the logging data. Secondly,the lithofacies chart was established to extract logging curves such as AC,GR,DEN,CNL,Rtand so on,which were sensitive to the response of lithofacies,the factor loading geological factors of each principal component were analyzed,and three principal components PC2,PC3,PC4 containing a large amount of lithofacies information were selected. Finally,the mapping relationship between lithofacies and logging curves was established,and the lithofacies identification of well Lu1,a key well in the study area,was carried out. The results show that compared with the traditional chart identification method,the lithofacies identification method combining principal component analysis with BP neural network can effectively eliminate the interference caused by the similarity of logging curves and reduce the error caused by the difference between the core data and the logging data,as so to improve the accuracy of lithofacies identification. This method is practical for shale lithofacies identification and has certain application value.
For the identification of shale lithofacies,the traditional method of establishing lithofacies chart does not fully take into account the interference caused by the similarity of logging data and the differences in the scale of experimental data,which results in the overlap of different types of sample points in the established identification chart,the ambiguity of boundaries and the large deviation of prediction. Aiming at this problem,taking thesecond member of Lucaogou Formation in Malang Sag of Santanghu Basin as an example,based on the full understanding of reservoir characteristics,a BP neutral network method based on principal component analysis was adopted. Firstly,the core data of the study area were used to classified the lithofacies into three types,such as organicrich laminar facies,carbonate-rich laminar facies and rich tuff-grain laminar facies,so as to reduce the scale error with the logging data. Secondly,the lithofacies chart was established to extract logging curves such as AC,GR,DEN,CNL,Rtand so on,which were sensitive to the response of lithofacies,the factor loading geological factors of each principal component were analyzed,and three principal components PC2,PC3,PC4 containing a large amount of lithofacies information were selected. Finally,the mapping relationship between lithofacies and logging curves was established,and the lithofacies identification of well Lu1,a key well in the study area,was carried out. The results show that compared with the traditional chart identification method,the lithofacies identification method combining principal component analysis with BP neural network can effectively eliminate the interference caused by the similarity of logging curves and reduce the error caused by the difference between the core data and the logging data,as so to improve the accuracy of lithofacies identification. This method is practical for shale lithofacies identification and has certain application value.
引文
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[2]欧成华,李朝纯.页岩岩相表征及页理缝三维离散网络模型.石油勘探与开发,2017,44(2):309-318.OU C H,LI C C.3D discrete network modeling of shale bedding fractures based on lithofacies characterization.Petroleum Exploration and Development,2017,44(2):309-318.
[3]张晋言.页岩油测井评价方法及其应用.地球物理学进展,2012,27(3):1154-1162.ZHANG J Y.Well logging evaluation method of shale oil reservoirs and its applications.Progress in Geophysics,2012,27(3):1154-1162.
[4]周成当,成菊安.模糊神经网络岩性识别系统.江汉石油学院学报,1993,15(4):40-44.ZHOU C D,CHENG J A.A lithology recognition system based on fuzzy neutral network.Journal of Jianghan Petroleum Institute,1993,15(4):40-44.
[5]张洪,邹乐君,沈晓华.BP神经网络在测井岩性识别中的应用.地质与勘探,2002,38(6):63-65.ZHANG H,ZOU L J,SHEN X H.The application of BP neutral network in well lithology identification.Geology and Prospecting,2002,38(6):63-65.
[6]罗伟平,范晓敏,陈军.利用一种有监督模糊ART人工神经网络进行测井岩性识别.吉林大学学报(地球科学版),2008,38(增刊1):137-139.LUO W P,FAN X M,CHEN J.Using supervised fuzzy ARTneutral network for lithology recognition in logging.Journal of Jilin University(Earth Science Edition),2008,38(Suppl 1):137-139.
[7]张平,潘保芝,张莹,等.自组织神经网络在火成岩岩性识别中的应用.石油物探,2009,48(1):53-56.ZHANG P,PAN B Z,ZHANG Y,et al.Application of selforganization maps network in identifying the lithology of igneous rock.Geophysical Prospecting for Petroleum,2009,48(1):53-56.
[8]朱怡翔,石广仁.火山岩岩性的支持向量机识别.石油学报,2013,34(2):312-322.ZHU Y X,SHI G R.Identification of lithologic characteristics of volcanic rocks by support vector machine.Acta Petrolei Sinica,2013,34(2):312-322.
[9]赵忠军,黄强东,石林辉,等.基于BP神经网络算法识别苏里格气田致密砂岩储层岩性.测井技术,2015,39(3):363-367.ZHAO Z J,HUANG Q D,SHI L H,et al.Identification of lithology in tight sandstone reservoir in Sulige gas field based on BPneutral net algorithm.Well Logging Technology,2015,39(3):363-367.
[10]胡嘉良,高玉超,余继峰,等.基于PCA-BP神经网络的非常规储层岩性识别研究.山东科技大学学报(自然科学版),2016,35(5):9-16.HU J L,GAO Y C,YU J F,et al.Lithology identification of unconventional reservoirs based on PCA-BP neural network.Journal of Shandong University of Science and Technology(Natural Science),2016,35(5):9-16.
[11]马峥,张春雷,高世臣.主成分分析与模糊识别在岩性识别中的应用.岩性油气藏,2017,29(5):127-133.MA Z,ZHANG C L,GAO S C.Lithology identification based on principal component analysis and fuzzy recognition.Lithologic Reservoirs,2017,29(5):127-133.
[12]刘毅,陆正元,吕晶,等.主成分分析法在泥页岩地层岩性识别中的应用.断块油气田,2017,24(3):360-363.LIU Y,LU Z Y,LYU J,et al.Application of principal component analysis method in lithology identification for shale formation.Fault-Block Oil&Gas Field,2017,24(3):360-363.
[13]祝鹏,林承焰,吴鹏,等.基于主成分分析法的成岩相测井定量识别:以五号桩油田桩62-66块沙三下Ⅰ油组储层为例.地球物理学进展,2015,30(5):2360-2365.ZHU P,LIN C Y,WU P,et al.Logging quantitative identification of diagenetic facies by using principal component analysis:a case of Es3x1in Zhuang 62-66 area,Wu Hao-zhuang Oilfield.Progress in Geophysics,2015,30(5):2360-2365.
[14]单敬福,陈欣欣,赵忠军,等.利用BP神经网络法对致密砂岩气藏储集层复杂岩性的识别.地球物理学进展,2015,30(3):1257-1263.SHAN J F,CHEN X X,ZHAO Z J,et al.Identification of complex lithology for tight sandstone gas reservoirs based on BPneural net.Progress in Geophysics,2015,30(3):1257-1263.
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[17]郭小波,黄志龙,涂小仙,等.马朗凹陷芦草沟组致密储集层复杂岩性识别.新疆石油地质,2013,34(6):649-652.GUO X B,HUANG Z L,TU X X,et al.Identification and application of complex lithology of Lucaogou tight reservoir in Malang Sag,Santanghu Basin.Xinjiang Petroleum Geology,2013,34(6):649-652.
[18]李新宁,马强,梁辉,等.三塘湖盆地二叠系芦草沟组二段混积岩致密油地质特征及勘探潜力.石油勘探与开发,2015,42(6):763-771.LI X N,MA Q,LIANG H,et al.Geological characteristics and exploration potential of diamictite tight oil the second member of the Permain Lucaogou Formation,Santanghu Basin,NWChina.Petroleum Exploration and Development,2015,42(6):763-771.
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[20]梁世君,黄志龙,柳波,等.马朗凹陷芦草沟组页岩油形成机理与富集条件.石油学报,2012,33(4):588-594.LIANG S J,HUANG Z L,LIU B,et al.Formation mechanism and enrichment conditions of Lucaogou Formation shale oil from Malang sag,Santanghu Basin.Acta Petrolei Sinica,2012,33(4):588-594.
[21]罗群,吴安彬,王井伶,等.中国北方页岩气成因类型、成气模式与勘探方向.岩性油气藏,2019,31(1):1-11.LUO Q,WU A B,WANG J L,et al.Genetic types,generation models,and exploration direction of shale gas in northern China.Lithologic Reservoirs,2019,31(1):1-11.
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