多属性概率神经网络技术在ML油田岩性油气藏预测中的应用
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摘要
在ML油田,由于地震资料品质差、井数据缺乏、开发程度低等原因,采用常规阻抗反演进行油气预测效果不理想,为此应用多属性概率神经网络技术进行油气预测。在研究区首先进行多属性分析,优选出振幅包络、泊松比等7种地震属性,建立起地震属性与油气之间的非线性关系;然后对已钻遇岩性油气藏砂体进行油气预测,将预测结果和实际测井数据进行对比说明预测结果真实可靠;最后对潜在的岩性油气藏目标砂体进行油气预测,得到目标砂体的油气分布概率以及厚度图,从而指导油田岩性油气藏的勘探与开发。
Due to poor quality of seismic data,rare well log data and low degree of development in ML oilfield,the hydrocarbon prediction is not satisfactory by using conventional impedance inversion.The multi-attribute probabilistic neural network can take advantage of pre-stack and post-stack seismic data,and it is applied for seismic attribute technique to enhance the seismic data utilization ratio.Firstly,the multi-attribute analysis has been carried out to optimize the amplitude envelope,poisson's ratio and some other seven attributes.And then,probabilistic neural network algorithm is used to establish the nonlinear relationship between seismic attributes and hydrocarbon,and after that the distribution of hydrocarbon at the drilled sand has been predicted.It has been proved the prediction results are reliable for comparing the forecast results and the actual logging data.Finally,the distribution of hydrocarbon for the target sand body has been predicted and the probability and the thickness of the oil distribution can be achieved so as to provide guidance for the exploration and the development of lithologic reservoirs.The teleconology has achieved good effects in study area,and it is also worthy of learning for exploration and exploitation to the similar lithologic reservoirs.
Due to poor quality of seismic data,rare well log data and low degree of development in ML oilfield,the hydrocarbon prediction is not satisfactory by using conventional impedance inversion.The multi-attribute probabilistic neural network can take advantage of pre-stack and post-stack seismic data,and it is applied for seismic attribute technique to enhance the seismic data utilization ratio.Firstly,the multi-attribute analysis has been carried out to optimize the amplitude envelope,poisson's ratio and some other seven attributes.And then,probabilistic neural network algorithm is used to establish the nonlinear relationship between seismic attributes and hydrocarbon,and after that the distribution of hydrocarbon at the drilled sand has been predicted.It has been proved the prediction results are reliable for comparing the forecast results and the actual logging data.Finally,the distribution of hydrocarbon for the target sand body has been predicted and the probability and the thickness of the oil distribution can be achieved so as to provide guidance for the exploration and the development of lithologic reservoirs.The teleconology has achieved good effects in study area,and it is also worthy of learning for exploration and exploitation to the similar lithologic reservoirs.
引文
[1]H Khoshdel,M A Riahi.3D porosity estimation usingmulti-attribute analysis methods in one of the Persiangulf oil fields[J].Society of petroleum engineers,2007,11(14):1-9.
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[4]马朋善,方贇,王向阳,等.利用地震多属性变换预测储层分布[J].河南石油,2004,18(4):16-20.
[5]陆光辉,吴官生,朱玉波,等.地震属性信息预测储层厚度[J].河南石油,2003,17(2):10-12.
[6]张喜,乔向阳,王玉艳,等.神经网络多属性分析技术[J].油气田地面工程,2007,26(8):1-3.
[2]张邵红.概率神经网络技术在非均质地层岩性反演中的应用[J].石油学报,2008,29(4):549-552.
[3]Daniel P Hampson.Use of multiattribute transformsto predict log propertied from seismic data[J].Geo-physics,2001,66(1):220-236.
[4]马朋善,方贇,王向阳,等.利用地震多属性变换预测储层分布[J].河南石油,2004,18(4):16-20.
[5]陆光辉,吴官生,朱玉波,等.地震属性信息预测储层厚度[J].河南石油,2003,17(2):10-12.
[6]张喜,乔向阳,王玉艳,等.神经网络多属性分析技术[J].油气田地面工程,2007,26(8):1-3.
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