Massive database generation for 2.5D borehole electromagnetic measurements using refined isogeometric analysis. (October 2021)

• Record Type: Journal Article
• Authors: Hashemian, Ali; Garcia, Daniel; Rivera, Jon Ander; Pardo, David
• Is Part Of: Computers & geosciences. Volume 155(2021)
• Publication Date: 2021-10
• Subjects: Geosteering -- Borehole resistivity measurements -- Refined isogeometric analysis -- 2.5D numerical simulation -- Deep learning inversion; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2021.104808
• Languages: English
• ISSNs: 0098-3004

Abstract

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. Highlights: We use rIGA discretizations for simulating 2.5D borehole electromagnetic measurements. We generate a synthetic database as a preliminary stage for deep learning inversion. Computational cost of rIGA is compared to that of IGA and FEA discretizations. rIGA generates the database O ( p ) times faster than high-continuity IGA discretization.