Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements

TL;DR

This paper investigates how error control and the choice of loss functions critically impact the accuracy of deep learning methods used for inverting borehole resistivity measurements, emphasizing their importance for reliable results.

Contribution

It provides a theoretical and experimental analysis of error control and loss function selection in deep neural networks for borehole resistivity inversion, highlighting their significance.

Findings

Proper error control improves inversion accuracy.

Loss function choice significantly affects results.

Theoretical insights guide better neural network design.

Abstract

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.