基于机器学习的感应日志快速正演建模卷积方法

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引用次数: 0

摘要

我们使用机器学习(ML)建立了一个卷积模型来计算一维(1D)地球模型的感应对数响应。与解析正演模型相比,卷积模型的速度非常快。基于ml的卷积从具有层电阻率和层边界的地球模型中找到精确的感应工具响应。对于单元感应工具2C40, 101点、50英尺窗口卷积模型在井斜角为60的情况下效果令人满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine-Learning-Based Convolution Method for Fast Forward Modeling of Induction Log
We built a convolution model using machine learning (ML) to calculate induction log responses for one-dimensional (1D) earth models. Compared to analytical forward modeling, the convolution model is extremely fast. ML-based convolution finds accurate induction tool responses from an earth model with layer resistivity and layer boundaries. For a unit induction tool 2C40, the 101-point, 50-ft window convolution model works satisfactorily for a well deviation angle of 60.
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