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Conductivity anisotropy estimation method for inversion processing of measurements made by a transverse electromagnetic induction logging instrument

机译:横向电磁感应测井仪测量反演的电导率各向异性估算方法

摘要

A method for generating an improved estimate of horizontal conductivity, dip angle, azimuth and anisotropy parameter of an earth formation penetrated by a wellbore from dual-frequency transverse electromagnetic induction measurements, comprising generating an initial estimate of the horizontal conductivity, dip angle, azimuth and anisotropy parameter from the dual-frequency transverse induction measurements made at each one of a plurality of base frequencies. The initial estimates from each of the plurality of base frequencies are input into a primary trained neural network, and the improved estimate is calculated by the trained neural network. The network is trained by generating models of earth formations each having a known value of horizontal conductivity, anisotropy parameter, dip angle and azimuth. Voltages which would be measured by the transverse electromagnetic induction instrument in response to each model are synthesized. Initial estimates from the synthesized voltages are calculated and the initial estimates and known values from each of the models are input to the neural network to cause it to learn a relationship between the initial estimates and the known values.
机译:一种用于从双频横向电磁感应测量中生成井眼穿透的地层的水平电导率,倾角,方位角和各向异性参数的改进估计值的方法,该方法包括生成水平电导率,倾角,方位角和方向的初始估计值在多个基频中的每个基频处进行的双频横向感应测量得出的各向异性参数。来自多个基本频率中的每一个的初始估计被输入到初级训练神经网络中,并且改进的估计由训练神经网络计算。通过生成每个具有已知水平电导率,各向异性参数,倾角和方位角值的地层模型来训练网络。合成了将由横向电磁感应仪器根据每个模型测量的电压。计算来自合成电压的初始估计,并将来自每个模型的初始估计和已知值输入到神经网络,以使其学习初始估计和已知值之间的关系。

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