期刊
GEOPHYSICAL JOURNAL INTERNATIONAL
卷 235, 期 1, 页码 150-165出版社
OXFORD UNIV PRESS
DOI: 10.1093/gji/ggad217
关键词
Electrical properties; Machine learning; Downhole methods; Inverse theory; Neural networks
Deep learning inversion is a promising method for real-time interpretation of logging-while-drilling (LWD) resistivity measurements. We develop a method to enhance the robustness of DL inversion methods in the presence of noisy LWD resistivity measurements by generating training data sets and constructing DL architectures.
Deep learning (DL) inversion is a promising method for real-time interpretation of logging-while-drilling (LWD) resistivity measurements for well-navigation applications. In this context, measurement noise may significantly affect inversion results. Existing publications examining the effects of measurement noise on DL inversion results are scarce. We develop a method to generate training data sets and construct DL architectures that enhance the robustness of DL inversion methods in the presence of noisy LWD resistivity measurements. We use two synthetic resistivity models to test the three approaches that explicitly consider the presence of noise: (1) adding noise to the measurements in the training set, (2) augmenting the training set by replicating it and adding varying noise realizations and (3) adding a noise layer in the DL architecture. Numerical results confirm that each of the three approaches enhances the noise-robustness of the trained DL inversion modules, yielding better inversion results-in both the predicted earth model and measurements-compared to the basic DL inversion and also to traditional gradient-based inversion results. A combination of the second and third approaches delivers the best results.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据