Physics-guided deep-learning inversion method for the interpretation of noisy logging-while-drilling resistivity measurements

Abstract

SUMMARY 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.