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arXiv · 2009.08132

Database Generation for Deep Learning Inversion of 2.5D Borehole Electromagnetic Measurements using Refined Isogeometric Analysis

Ali Hashemian·Daniel Garcia·Jon Ander Rivera·David Pardo
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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 large 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 hours using a workstation equipped with two CPUs.

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Ali Hashemian, Daniel Garcia, Jon Ander Rivera, David Pardo. 2020-09-17. Database Generation for Deep Learning Inversion of 2.5D Borehole Electromagnetic Measurements using Refined Isogeometric Analysis. https://doi.org/10.1016/j.cageo.2021.104808

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