基于地震属性技术的井壁稳定随钻预测新方法
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摘要
基于地震属性和测井数据之间存在非线性关系,提出一种综合利用地震和测井信息随钻预测井壁稳定性的新方法,即在钻前联合运用遗传算法和BP神经网络算法,从所提取的井旁原始地震属性中优选出对测井特征参数最敏感的地震属性组合,并利用已钻井资料建立起反映所研究区块不同地层中地震属性与测井数据之间关系的一系列神经网络映射模型,实钻时通过分析岩屑录井资料选择适当的映射模型,随钻预测出待钻井段的声波和密度测井数据,进一步预测出钻头下方地层的井壁稳定性。该预测方法具有良好的精确度和时效性,在塔西南地区的实际应用中取得了良好效果。
A new method of predicting borehole stability while drilling was presented by using seismic attributes and well logs together.This method is based on the close nonlinear relationships between seismic attributes and well logs.Before drilling the optimal attribute combinations which are sensitive to log properties are selected from original seismic attributes by using genetic algorithm and BP neural network algorithm together.Then a series of mapping models which reflect relationships between seismic attributes and well logs of various formation intervals in given area are constructed through neural network.With analysis of cutting logging data,the proper mapping model can be employed to predict acoustic and density log curves of impending drilling formation while drilling.Based on the analysis,the borehole stability of formation under bit can be predicted.The prediction precision and real-time operation ability of the proposed method are satisfactory,which have been proved in the practical application in south-west area of Tarim Oilfield.
A new method of predicting borehole stability while drilling was presented by using seismic attributes and well logs together.This method is based on the close nonlinear relationships between seismic attributes and well logs.Before drilling the optimal attribute combinations which are sensitive to log properties are selected from original seismic attributes by using genetic algorithm and BP neural network algorithm together.Then a series of mapping models which reflect relationships between seismic attributes and well logs of various formation intervals in given area are constructed through neural network.With analysis of cutting logging data,the proper mapping model can be employed to predict acoustic and density log curves of impending drilling formation while drilling.Based on the analysis,the borehole stability of formation under bit can be predicted.The prediction precision and real-time operation ability of the proposed method are satisfactory,which have been proved in the practical application in south-west area of Tarim Oilfield.
引文
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[11]TELESCA L,CUOMO V,LAPEN V,et al.Statisticalanalysis of fractal properties of point processes modelingseismic sequences[J].Phys Earth Planet In,2001,125(1/4):65-83.
[12]NATH S K,DEWANGAN P.Detection of seismic re-flections from seismic attributes through fractal analysis[J].Geophys Prospect,2002,50(3):341-360.
[13]李正文,李琼.岩性储集层的混沌识别技术研究[J].矿物岩石,1999,19(2):81-85.LI Zheng-wen,LI Qiong.Study on lithological reservoirchaos recognition technique[J].Journal of Mineralogyand Petrology,1999,19(2):81-85.
[14]CHO S.Towards creative evolutionary systems with in-teractive genetic algorithm[J].Appl Intelligence,2002,16(2):129-138.
[15]DURAND N,ALLIOTJ,MEDIONI F.Neural nets trainedby genetic algorithm for collision avoidance[J].Appl In-telligence,2000,13(3):205-213.
[16]张立明.人工神经网络的模型及其应用[M].上海:复旦大学出版社,1994.
[17]HAN D H,NUR A,MORGAN D.Effect of porosityand clay content on wave velocities in sandstone[J].Geophysics,1986,51(11):2093-2107.
[18]DEERE D U,MILLER R P.Engineering classificationand index properties for intact rock[R].ClearanceHouse for Federal Scientific and Technical Information,Dept of Commerce,Washington D C,U S,1966.
[19]COATES G R,DENOO S.Mechanical properties pro-gram using borehole analysis and Mohr's circle[C].Proc SPWLA22nd Ann Logging Symp,1981.
[20]EBERHART P,HAN D H,ZOBACK M D.Empiricalrelationships among seismic velocity,effective pressure,porosity and clay content in sandstone[J].Geophysics,1989,54(1):82-89.
[21]金衍.井壁稳定预测理论和应用研究[D].北京:石油大学石油天然气工程学院,2001.
[2]AADNOY B S,ONG S.Introduction to special issue onborehole stability[J].J Petrol Sci Eng,2003,38(3):79-82.
[3]CHEN X,TAN C,HABERFIELD C M.Guidelines forefficient wellbore stability analysis[J].Int J Rock MechMin Sci&Geomech Abstr,1997,34(3):418.
[4]金衍,陈勉,张旭东.钻前井壁稳定预测方法的研究[J].石油学报,2001,22(3):96-99.JIN Yan,CHEN Mian,ZHANG Xu-dong.Study on pre-diction for borehole stability before drilling[J].Acta Pe-trolei Sinica,2001,22(3):96-99.
[5]金衍,陈勉,杨小奇.利用层速度钻前预测安全泥浆密度窗口研究[J].岩石力学与工程学报,2004,23(14):2430-2433.JINYan,CHENMian,YANG Xiao-qi.Prediction of safemud density window by interval velocity before drilling[J].Chinese Journal of Rock Mechanics and Engineer-ing,2004,23(14):2430-2433.
[6]金衍,陈勉.利用地震记录钻前预测井壁稳定性研究[J].石油学报,2004,25(1):89-92.JIN Yan,CHEN Mian.Prediction of borehole stability byseismic records[J].Acta Petrolei Sinica,2004,25(1):89-92.
[7]卢宝坤,史謌.测井资料与地震属性关系研究综述[J].北京大学学报:自然科学版,2005,41(1):154-160.LU Bao-kun,SHI Ge.A review of study on relation be-tween well logging data and seismic attributes[J].ActaScicentiarum Naturalum Universitis Pekinensis,2005,41(1):154-160.
[8]TURCOTTE D L.Fractal in geology and geophysics[J].Pure Appl Geophys,1989,131(1/2):171-196.
[9]曾锦光,舒雅琴,钟勇.地震记录的分形和混沌性质[J].石油地球物理勘探,1995,30(6):743-748.ZENG Jin-guang,SHU Ya-qin,ZHONG Yong.Fractaland chaotic characteristics of seismic data[J].Oil Geo-physical Prospecting,1995,30(6):743-748.
[10]李海燕,彭仕宓.应用分形技术预测井间裂缝[J].石油大学学报:自然科学版,2002,26(6):33-35.LI Hai-yan,PENG Shi-mi.Prediction of inter-well frac-tures with fractal technique[J].Journal of the Universi-ty of Petroleum,China(Edition of Natural Science),2002,26(6):33-35.
[11]TELESCA L,CUOMO V,LAPEN V,et al.Statisticalanalysis of fractal properties of point processes modelingseismic sequences[J].Phys Earth Planet In,2001,125(1/4):65-83.
[12]NATH S K,DEWANGAN P.Detection of seismic re-flections from seismic attributes through fractal analysis[J].Geophys Prospect,2002,50(3):341-360.
[13]李正文,李琼.岩性储集层的混沌识别技术研究[J].矿物岩石,1999,19(2):81-85.LI Zheng-wen,LI Qiong.Study on lithological reservoirchaos recognition technique[J].Journal of Mineralogyand Petrology,1999,19(2):81-85.
[14]CHO S.Towards creative evolutionary systems with in-teractive genetic algorithm[J].Appl Intelligence,2002,16(2):129-138.
[15]DURAND N,ALLIOTJ,MEDIONI F.Neural nets trainedby genetic algorithm for collision avoidance[J].Appl In-telligence,2000,13(3):205-213.
[16]张立明.人工神经网络的模型及其应用[M].上海:复旦大学出版社,1994.
[17]HAN D H,NUR A,MORGAN D.Effect of porosityand clay content on wave velocities in sandstone[J].Geophysics,1986,51(11):2093-2107.
[18]DEERE D U,MILLER R P.Engineering classificationand index properties for intact rock[R].ClearanceHouse for Federal Scientific and Technical Information,Dept of Commerce,Washington D C,U S,1966.
[19]COATES G R,DENOO S.Mechanical properties pro-gram using borehole analysis and Mohr's circle[C].Proc SPWLA22nd Ann Logging Symp,1981.
[20]EBERHART P,HAN D H,ZOBACK M D.Empiricalrelationships among seismic velocity,effective pressure,porosity and clay content in sandstone[J].Geophysics,1989,54(1):82-89.
[21]金衍.井壁稳定预测理论和应用研究[D].北京:石油大学石油天然气工程学院,2001.
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