A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements
This work addresses the need for real-time subsurface mapping in oil and gas drilling, but it is incremental as it applies an existing deep learning method to a specific domain without novel methodological breakthroughs.
The paper tackles the problem of real-time inversion of borehole resistivity measurements for geosteering by using a Deep Neural Network (DNN) to approximate the inverse problem, enabling rapid delivery of subsurface models from synthetic data.
We use borehole resistivity measurements to map the electrical properties of the subsurface and to increase the productivity of a reservoir. When used for geosteering purposes, it becomes essential to invert them in real time. In this work, we explore the possibility of using Deep Neural Network (DNN) to perform a rapid inversion of borehole resistivity measurements. Herein, we build a DNN that approximates the following inverse problem: given a set of borehole resistivity measurements, the DNN is designed to deliver a physically meaningful and data-consistent piecewise one-dimensional layered model of the surrounding subsurface. Once the DNN is built, we can perform the actual inversion of the field measurements in real time. We illustrate the performance of DNN of logging-while-drilling measurements acquired on high-angle wells via synthetic data.