Crosswell electromagnetic (EM) method has fundamentally improved the horizontal detection ability of well logging and will become an increasingly promising approach for the secondary exploration of hydrocarbon reservoir. We applied orthogonal least squares (OLS) radial basis function neural network (RBFNN) based on improved Gram–Schmidt (G–S) procedure to three-dimensional (3D) crosswell EM inversion problems. In the inversion process of the simplified crosswell model with single-grid conductivity anomalies and normal oil reservoir, compared the inversion results of other five neural networks, OLS-RBFNN was proved to have the best global optimization ability and the fastest sample learning speed and the average inversion error of low conductivity anomalies model (4%) and oil reservoir model (9%) can meet the inversion requirements of crosswell EM method. Only the OLS-RBFNN could achieve ideal inversion results in the most concerned central area of crosswell model, and the inversion accuracy of this algorithm will be more outstanding when the model becomes more complex. Merely using the three-component time-domain crosswell EM data of two wells, the inversion of 3D medium conductivity in the crosswell dominant exploration area can be effectively realized through the nonlinear approximation of the OLS-RBFNN.

中文翻译：

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基于径向基函数神经网络的3D井间电磁反演

井间电磁法从根本上提高了测井水平探测能力，将成为油气藏二次勘探中越来越有前途的方法。我们将基于改进的Gram–Schmidt（GS）程序的正交最小二乘（OLS）径向基函数神经网络（RBFNN）应用到三维（3D）井间EM反演问题中。在具有单网格电导率异常和正常油藏的简化井间模型的反演过程中，比较了其他五个神经网络的反演结果，OLS-RBFNN具有最佳的全局优化能力和最快的样本学习速度，低电导率异常模型（4％）和油藏模型的平均反演误差（9％）可以满足Crosswell EM方法的反演要求。只有OLS-RBFNN可以在井间模型最关注的中心区域获得理想的反演结果，并且当模型变得更加复杂时，该算法的反演精度也会更加出色。仅使用两口井的三分量时域井间EM数据，即可通过OLS-RBFNN的非线性逼近有效地实现井间优势勘探区的3D介质电导率反演。当模型变得更加复杂时，该算法的反演精度将更加出色。仅使用两口井的三分量时域井间EM数据，即可通过OLS-RBFNN的非线性逼近有效地实现井间优势勘探区的3D介质电导率反演。当模型变得更加复杂时，该算法的反演精度将更加出色。仅使用两口井的三分量时域井间EM数据，即可通过OLS-RBFNN的非线性逼近有效地实现井间优势勘探区的3D介质电导率反演。