应用一种改进BP神经网络算法预测密度曲线
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摘要
利用测井数据与地震数据二者相结合进行综合分析,是地震勘探工作者的重要工作。可以通过分析井位处的地震数据与测井数据,提取地震的多个属性,建立一个与测井属性的统计关系。选取已改进的三层网络结构BP神经网络算法,在应用一个实际例子后表明,该算法的主要特点是收敛速度快、计算简单,同时还具有跳出局部最小的能力。应用此神经网络算法对某油田的二维地震数据进行了处理,提取了多种地震属性,并在井位置建立了地震属性与密度曲线的非线性关系,成功预测了剖面密度曲线,为了解储层状况提供了有益的资料。
One of important works of seismologist is the integration of well-log and seismic data. We can derive the relationship between seismic data and well-log by analyzing training data at the position of well. The statistical relation will be constructed by extracting seismic data attributes to predict logs. This paper chooses an improved BP neural network with a three-layers structure. An example shows that the features of the network algorithm are of rapid speed, mathematical simplicity and ability of avoiding local minima. Applying this network to one 2-D seismic data section at practice,density log is predicted successfully by constructing the nonlinear relation between extracted seismic multi-attribute and log, which greatly benefit our understanding of the reservoirs.
One of important works of seismologist is the integration of well-log and seismic data. We can derive the relationship between seismic data and well-log by analyzing training data at the position of well. The statistical relation will be constructed by extracting seismic data attributes to predict logs. This paper chooses an improved BP neural network with a three-layers structure. An example shows that the features of the network algorithm are of rapid speed, mathematical simplicity and ability of avoiding local minima. Applying this network to one 2-D seismic data section at practice,density log is predicted successfully by constructing the nonlinear relation between extracted seismic multi-attribute and log, which greatly benefit our understanding of the reservoirs.
引文
[1]DANIEL P,HAMPSON.Use of multiattribute trans-forms to predict log properties from seismic data[J].Geophysics,2001,66(1):220.
[2]CHEN Q,SIDNEY S.Seismic attribute technology for reservoir forecasting and monitoring[J].The Leading Edge.1997,16:445.
[3]何碧竹,周杰,汪功怀.利用多元地震属性预测储层信息[J].石油地球物理勘探,2003,38(3):258.
[4]侯伯刚,杨池银,武站国,等.地震属性及其在储层预测中的影响因素[J].石油地球物理勘探2004,39(5):553.
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[6]黄绪德.地震勘探直接找油气在国外的最新发展,勘探地球物理进展[J].2004,27(3):218.
[7]武妍,王守觉.一种新的快速收敛的反向传播算法[J].同济大学学报(自然科学版),2004,32(8):1092.
[8]LIU Z,LIU J.Seismic-controlled nonlinear extrapola-tion of well parameters using neural networks[J].Geo-physics,1998,63:2035.
[9]黄玲,辜彬.用人工神经网络综合测井和地震资料预测岩性[J].成都理工学院学报,1994,21(2):89.
[10]乐友喜.地震属性与储层特征的相关性研究[J].石油大学学报,2004,28(1):26.
[2]CHEN Q,SIDNEY S.Seismic attribute technology for reservoir forecasting and monitoring[J].The Leading Edge.1997,16:445.
[3]何碧竹,周杰,汪功怀.利用多元地震属性预测储层信息[J].石油地球物理勘探,2003,38(3):258.
[4]侯伯刚,杨池银,武站国,等.地震属性及其在储层预测中的影响因素[J].石油地球物理勘探2004,39(5):553.
[5]郭栋.地震属性分析及其在资料解释中应用[J].油气地球物理,2004,2(3):46.
[6]黄绪德.地震勘探直接找油气在国外的最新发展,勘探地球物理进展[J].2004,27(3):218.
[7]武妍,王守觉.一种新的快速收敛的反向传播算法[J].同济大学学报(自然科学版),2004,32(8):1092.
[8]LIU Z,LIU J.Seismic-controlled nonlinear extrapola-tion of well parameters using neural networks[J].Geo-physics,1998,63:2035.
[9]黄玲,辜彬.用人工神经网络综合测井和地震资料预测岩性[J].成都理工学院学报,1994,21(2):89.
[10]乐友喜.地震属性与储层特征的相关性研究[J].石油大学学报,2004,28(1):26.
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