利用测井和岩心资料计算页岩地层渗透率参数的神经网络方法

Y. Soares, S. Nogueira, A. Carrasco
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引用次数: 0

摘要

神经网络可以学习复杂的非线性关系,即使输入信息是噪声和不太精确。它在分类、模式识别和过程建模方面取得了进展。众所周知,泥质地层通过降低有效孔隙度来降低储层的储集能力,并通过降低渗透率来降低流体的输送能力。储层中粘土的存在对岩石物理测井有两个影响:降低电阻率,导致孔隙度测井(声波、中子和密度)通常记录的孔隙度过高(Asquith, 1990)。神经网络应用方面,以巴西Campos盆地Namorado油田3口井的测井和岩心数据为参考,采用反向传播网络,并以岩心数据为参考,进行了误差分析。
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Application of neural networking for calculation of permeability parameters in shaly formations using well logging and core data
Neural networks can learn complex non-linear relationship, even when the input information is noise and less precise. It has made advances in classification, pattern recognition and process modeling. It is well know that shaly formations gives some effects on the reservoir as reduction in storage capacity by reducing effective porosity and reduces the ability to transmit fluids by lowering permeability. The presence of clay in a reservoir has two effects on petrophysical logs: lowers resistivity and it causes the porosity logs (sonic, neutron and density) to generally record too high a porosity (Asquith, 1990). For neural application, the back propagation network was used, taking as reference the well logging and core data from three wells of Namorado Field of Campos Basin (Brazil) Finally, and error analysis was done taking the permeability values obtained from cores as reference.
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