Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements
This work addresses the need for accurate real-time inversion in oil and gas geosteering operations, though it is incremental as it focuses on optimizing existing deep learning methods for a specific application.
The paper tackled the problem of achieving reliable and robust deep learning inversion for borehole resistivity measurements by investigating error control and loss function selection, resulting in improved accuracy as demonstrated through theoretical analysis and numerical experiments.
Deep learning (DL) is a numerical method that approximates functions. Recently, its use has become attractive for the simulation and inversion of multiple problems in computational mechanics, including the inversion of borehole logging measurements for oil and gas applications. In this context, DL methods exhibit two key attractive features: a) once trained, they enable to solve an inverse problem in a fraction of a second, which is convenient for borehole geosteering operations as well as in other real-time inversion applications. b) DL methods exhibit a superior capability for approximating highly-complex functions across different areas of knowledge. Nevertheless, as it occurs with most numerical methods, DL also relies on expert design decisions that are problem specific to achieve reliable and robust results. Herein, we investigate two key aspects of deep neural networks (DNNs) when applied to the inversion of borehole resistivity measurements: error control and adequate selection of the loss function. As we illustrate via theoretical considerations and extensive numerical experiments, these interrelated aspects are critical to recover accurate inversion results.