基于地震属性的煤层厚度预测模型及其应用
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摘要
地震属性技术在岩性和构造解释等方面得到了越来越广泛的应用,特别是在煤、油气资源勘探中具有重要的作用.基于淮南矿区谢桥1区131煤层地震勘探资料,提取了28种地震属性数据;通过地震属性的分析,优选出平均峰值振幅、振幅的峰态、最大绝对振幅、瞬时频率斜率等4种地震属性作为煤层厚度预测模型基本参数,结合已知钻孔资料,利用多元多项式回归以及BP人工神经网络方法,求出了各属性与煤厚之间的多元多项式回归模型及人工神经网络模型,并对模型进行了误差分析和应用结果对比分析,反映出人工神经网络模型在煤厚预测中具有好的应用效果.
As a technology used widely in lithology and structural interpretation, seismic attribute technology has been playing an important role in coal and oil exploration. Based on 3_D seismic exploration data of coal seam 13-1 in the Xieqiao colliery, Huainan coal field of China, 28 seismic attributes are extracted. Through analysis of seismic attributes, four usable seismic attributes, such as average-peak-amplitude, kurtosis-in-amplitude, maximum-absolute-amplitude and slope rate of instantaneous-frequency, are selected as the basic analysis parameters of prediction models of coal thickness. Combined with the real drill data, the prediction models between coal thickness and multi attributes are established by using analytical methods of multivariant polynomial regression and BP neural networks(BPNN), and error analysis of predicting coal thickness is carried out. From the comparison of prediction results of coal thickness of coal seam 13-1 in the Xieqiao colliery, Huainan coal field of China by using these two models, it is concluded that the BPNN model has higher accuracy in predicting coal thickness.
As a technology used widely in lithology and structural interpretation, seismic attribute technology has been playing an important role in coal and oil exploration. Based on 3_D seismic exploration data of coal seam 13-1 in the Xieqiao colliery, Huainan coal field of China, 28 seismic attributes are extracted. Through analysis of seismic attributes, four usable seismic attributes, such as average-peak-amplitude, kurtosis-in-amplitude, maximum-absolute-amplitude and slope rate of instantaneous-frequency, are selected as the basic analysis parameters of prediction models of coal thickness. Combined with the real drill data, the prediction models between coal thickness and multi attributes are established by using analytical methods of multivariant polynomial regression and BP neural networks(BPNN), and error analysis of predicting coal thickness is carried out. From the comparison of prediction results of coal thickness of coal seam 13-1 in the Xieqiao colliery, Huainan coal field of China by using these two models, it is concluded that the BPNN model has higher accuracy in predicting coal thickness.
引文
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[3]ChenQ,SidneyS.Seismicattributetechnologyforreservoir forecastingandmonitoring.TheLeadingEdge,1997,16(5):445～450
[4]乐友喜.利用模型技术研究地震属性的地质意义.物探与化探,2001,25(3):191～197LeYX.Geologicalsignificanceofseismicattributionresearchwith modeltechnology.GeophysicalandGeochemicalExploration(in Chinese),2001,25(3):191～197
[5]陆基孟.地震勘探原理.北京:石油大学出版社,1993LiuJM.TheoryofSeismicMethod(inChinese).Beijing:China PetroleumUniversityPress,1993
[6]崔若飞,李晋平,庞留彦等.地震属性技术在煤田地震勘探中的应用研究.中国矿业大学学报,2002,31(5):267～270CuiRF,LiJP,PangLY,etal.Applicationofseismicattribution techniquesincoalseismicmethod.JournalofChinaUniversityof MiningTechnology(inChinese),2002,31(5):267～270
[7]熊晓军,尹成,张白林.高阶统计量油气检测方法研究.地球物理学报,2004,47(5):920～927XiongXJ,YinC,ZhangBL.Methodresearchofforecastingoil andgasusinghigher orderstatistics.ChineseJ.Geophys.(in Chinese),2004,47(5):920～927
[8]孙春岩,牛滨华,文鹏飞等.海上E区天然气水合物地质、地震、地球化学特征综合研究与成藏远景预测.地球物理学报,2004,47(6):1076～1085SunCY,NiuBH,WenPF,etal.Anintegratedstudymethodfor explorationofgashydratereservoirsinmarineareas.ChineseJ.Geophys.(inChinese),2004,47(6):1076～1085
[9]BrianR,HampsonD.Multiattributeseismicanalysis.TheLeading Edge,1997,16(10):1439～1443
[10]HillKB.Sandthicknesspredictionfrom3Dseismicdata:Acase studyoftheUpperJurassicFriscoCitySandofsouthwestAlabama.TheLeadingEdge,2001,20(9):950～964
[11]李春峰,ChristopherLiner.基于小波多尺度分析的奇性指数:一种新地震属性.地球物理学报,2005,48(4):882～888LiCF,ChristopherL.Singularityexponentfromwavelet based multiscaleanalysis:Anewseismicattribute.ChineseJ.Geophys.(inChinese),2005,48(4):882～888
[12]郭颜省,孟召平,杨瑞召等.地震属性及其在煤层厚度预测中的应用.中国矿业大学学报,2004,(5):557～562GuoYS,MengZP,YangRZ,etal.Seismicattributionsanalysis andit’sapplicationinpredictingthicknessofcoal.JournalofChina UniversityofMiningTechnology(inChinese),2004,(5):557～562
[13]刘文岭,牛彦良,李刚等.多信息储层预测地震属性提取与有效性分析方法.石油物探,2002,41(1):100～106LiuWL,NiuYL,LiG,etal.Seismicattributeextractionand effectivenessanalysisofmulti attributereservoirprediction.GeophysicalProspectingforPetroleum(inChinese),2002,41(1):100～106
[14]AlistairRB.Seismicattributesandtheirclassification.TheLeading Edge,1996,15(10):1090
[15]赵林明,胡浩云,魏德华等.多层前向人工神经网络.郑州:黄河水利出版社,1999.18～26ZhaoLM,HuHY,WeiDH,etal.ForwardMulti layersArtificial NeuralNetworks(inChinese).Zhengzhou:YellowRiverWater ConservancyPress,1999.18～26
[16]LooneyCG.PatternRecognitionUsingNeuralNetwork:Theoryand AlgorithmsforEngineerandScientists.NewYork:OxfordUniversity Pres,Inc.1997
[17]郭文兵,邓喀中,邹友峰.地表下沉系数计算的人工神经网络方法研究.岩土工程学报,2003,25(2):212～215GuoWB,DengKZ,ZouYF.Studyonartificialneuralnetwork methodforcalculationofsubsidencecoefficient.ChineseJ.GeotechnicalEngineering(inChinese),2003,25(2):212～215
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