Deep-Learning Inversion Method for the Interpretation of Noisy Logging-While-Drilling Resistivity Measurements
This is an incremental improvement for oil and gas drilling navigation, addressing a specific robustness issue in a niche domain.
The paper tackled the problem of measurement noise affecting deep learning inversion for logging-while-drilling resistivity interpretation, and found that three noise-handling approaches improved inversion results, with a combination of data augmentation and a noise layer performing best.
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 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 the three approaches produce a denoising effect, yielding better inversion results in both predicted earth model and measurements compared not only to the basic DL inversion but also to traditional gradient based inversion results. A combination of the second and third approaches delivers the best results. The proposed methods can be readily generalized to multi dimensional DL inversion.