Massive database generation for 2.5D borehole electromagnetic measurements using refined isogeometric analysis

Ali Hashemian, Daniel Garcia, Jon Ander Rivera, David Pardo

Abstract

Borehole resistivity measurements are routinely inverted in real-time during geosteering operations. The inversion process can be efficiently performed with the help of advanced artificial intelligence algorithms such as deep learning. These methods require a massive dataset that relates multiple Earth models with the corresponding borehole resistivity measurements. In here, we propose to use an advanced numerical method — refined isogeometric analysis (rIGA) — to perform rapid and accurate 2.5D simulations and generate databases when considering arbitrary 2D Earth models. Numerical results show that we can generate a meaningful synthetic database composed of 100,000 Earth models with the corresponding measurements in 56 h using a workstation equipped with two CPUs.