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Adaptive Regularization Iterative Inversion of Array Multicomponent Induction Well Logging Datum in a Horizontally Stratified Inhomogeneous TI Formation

机译:水平分层非均匀TI组中阵列多分量感应测井数据的自适应正则迭代反演

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摘要

An adaptive regularization iterative inversion of array multicomponent induction well logging datum is established to simultaneously reconstruct the horizontal and vertical conductivities of both invasion zone and origin formation, invasion radius, and the interface depth of each bed in the horizontally stratified inhomogeneous transversally isotropic (TI) formation. Applying numerical mode matching method, we can obtain a much compact semianalytic expression of the electromagnetic tensor Green''s functions by magnetic current source in the inhomogeneous TI formation. Then, using the perturbation principles, an efficient computation of Fréchet derivatives of the multicomponent induction logging response is set up with respect to all the model parameters. After that, the combination of Morozev''s discrepancy principle with Cholesky''s decomposition is applied to adaptively select regularization factor during inversion so that stabilization of inversion solution is assured as well as realization of best fit of the input data with the modeling logs. Finally, the numerical tests validate the algorithm.
机译:建立了阵列多分量感应测井数据的自适应正则化迭代反演,以同时重建水平分层非均质横向各向同性(TI)中侵入区的水平和垂直电导率以及原点形成,侵入半径和每个层的界面深度编队。应用数值模式匹配方法,通过不均匀TI层中的磁电流源,可以得到电磁张量格林函数的非常紧凑的半解析表达式。然后,使用摄动原理,针对所有模型参数,建立了多分量感应测井响应的Fréchet导数的有效计算。之后,将Morozev差异原理与Cholesky分解相结合,以在反演期间自适应地选择正则化因子,从而确保反演解决方案的稳定性以及输入数据与建模日志的最佳拟合。最后,数值测试验证了该算法。

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