概率神经网络储层流体密度反演及应用
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摘要
概率神经网络是一种基于概率统计思想的神经网络,利用概率神经网络进行储层的流体密度反演,通过它的非线性扩展进行多个属性的优选组合,完成神经网络的训练学习和概率估算,有效地剔除个别数据的不利影响,使反演过程更加稳定,减少反演结果的多解性。川西某气藏的概率神经网络储层流体密度反演结果表明,该反演方法准确性很高,反演结果与实际试气结果对应良好,能解决一些常规地震勘探油气检测方法不能解决的问题,并可对储层的含气性进行定量分析,为储层预测、气水识别、油气藏描述提供重要的数据支持。
The fluid-density in reservoir rock porosity can reflect the reservoir characteristics directly.This is one of the key parameters of today′s seismic exploration for hydrocarbon.Probabilistic neural network(PNN) is a neural net work based on probability and statistics idea.The probabilistic neural network inversion reservoir fluid-density is used for the preferred combination of multi ple attributes to complete the training of neural network learning and probabili ty estimation through its non-linear expansion.This can effectively eliminate t he adverse effects of individual data,stabilize the inversion process and reduc e the inversion multi-solvability.The gas reservoir fluid-density inversion res ult of a gas field in western Sichuan shows that the PNN inversion method,with its high accuracy and correspondence with the actual test gas well,can solve so me problems that some conventional seismic hydrocarbon detection methods are una ble to solve.It can conduct a quantitative analysis for reservoir gas-bearing, and provide an important data support for reservoir prediction,gas-water identi fication and gas reservoir description.
The fluid-density in reservoir rock porosity can reflect the reservoir characteristics directly.This is one of the key parameters of today′s seismic exploration for hydrocarbon.Probabilistic neural network(PNN) is a neural net work based on probability and statistics idea.The probabilistic neural network inversion reservoir fluid-density is used for the preferred combination of multi ple attributes to complete the training of neural network learning and probabili ty estimation through its non-linear expansion.This can effectively eliminate t he adverse effects of individual data,stabilize the inversion process and reduc e the inversion multi-solvability.The gas reservoir fluid-density inversion res ult of a gas field in western Sichuan shows that the PNN inversion method,with its high accuracy and correspondence with the actual test gas well,can solve so me problems that some conventional seismic hydrocarbon detection methods are una ble to solve.It can conduct a quantitative analysis for reservoir gas-bearing, and provide an important data support for reservoir prediction,gas-water identi fication and gas reservoir description.
引文
[1]李庆忠.走向精确勘探的道路:高分辨率地震勘探系统工程剖析[M].北京:石油工业出版社,1993.
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[7]Amos N,Gary M,Jack D,et al.Critical porosity:A key torelating physical properties to porosity in rocks[J].The Lead-ing Edge,1998,17:357-362.
[8]葛瑞.马沃可,塔潘.木克基,杰克.德沃金,等.岩石物理手册:孔隙介质中地震分析工具[M].徐海滨,戴建春,译.合肥:中国科学技术大学出版社,2008.
[9]王昊,张波,田蔚风.一种基于概率神经网络多信息融合的移动目标跟踪算法[J].上海交通大学学报,2007,41(5):792-796.
[10]王辉,杨林,丁金华,等.基于概率神经网络板材纹理分类识别的研究[J].大连工业大学学报,2009,28(5):387-390.
[11]苏君毅,过仲阳,邱洁,等.PNN网络在预测MCS移动中的应用[J].上海地质,2004(3):27-30.
[12]蔡成国,顾汉明,李宗杰,等.波阻抗反演方法在塔河碳酸盐储层预测中的应用[J].地质科技情报,2009,28(4):91-95.
[13]李海燕,彭仕宓.应用遗传神经网络研究碎屑岩储集流动单元[J].地质科技情报,2007,26(3):56-60.
[14]鲁红英,徐天吉,甘其刚,等.川西坳陷深层须家河组裂缝模型含气响应研究[J].石油物探,2007,46(3):254-260,271.
[15]李显贵,徐天吉,甘其刚.新场气田须家河气藏含气性地震检测研究[J].石油物探,2006,45(9):186-191.
[2]宁宁,王红岩,雍洪,等.中国非常规天然气资源基础与开发技术[J].天然气工业,2009,29(9):9-12.
[3]曹树春.储集层精细描述的新思路[J].地质科技情报,2001,20(1):39-43.
[4]Biot M A.Theory of propagation of elastic waves in a fluid-saturated porous solid[J].The Journal of the Acoustical Socie-ty of America,1956,28(3):169-191.
[5]张璐,印兴耀,孙成禹.双相介质的AVO正演横拟[J].地球物理学进展,2005,20(2):319-322.
[6]李勇,李正文,李志荣,等.储层密度预测技术研究[J].石油地球物理勘探,2007,42(2):216-219,225.
[7]Amos N,Gary M,Jack D,et al.Critical porosity:A key torelating physical properties to porosity in rocks[J].The Lead-ing Edge,1998,17:357-362.
[8]葛瑞.马沃可,塔潘.木克基,杰克.德沃金,等.岩石物理手册:孔隙介质中地震分析工具[M].徐海滨,戴建春,译.合肥:中国科学技术大学出版社,2008.
[9]王昊,张波,田蔚风.一种基于概率神经网络多信息融合的移动目标跟踪算法[J].上海交通大学学报,2007,41(5):792-796.
[10]王辉,杨林,丁金华,等.基于概率神经网络板材纹理分类识别的研究[J].大连工业大学学报,2009,28(5):387-390.
[11]苏君毅,过仲阳,邱洁,等.PNN网络在预测MCS移动中的应用[J].上海地质,2004(3):27-30.
[12]蔡成国,顾汉明,李宗杰,等.波阻抗反演方法在塔河碳酸盐储层预测中的应用[J].地质科技情报,2009,28(4):91-95.
[13]李海燕,彭仕宓.应用遗传神经网络研究碎屑岩储集流动单元[J].地质科技情报,2007,26(3):56-60.
[14]鲁红英,徐天吉,甘其刚,等.川西坳陷深层须家河组裂缝模型含气响应研究[J].石油物探,2007,46(3):254-260,271.
[15]李显贵,徐天吉,甘其刚.新场气田须家河气藏含气性地震检测研究[J].石油物探,2006,45(9):186-191.
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