Bi-fidelity Gradient-Based Approach for Nonlinear Well Logging Inverse Problems

Journal Article · Tue Jul 07 04:00:00 EDT 2020 · IEEE Journal on Multiscale and Multiphysics Computational Techniques

DOI:https://doi.org/10.1109/JMMCT.2020.3007839· OSTI ID:1638433

Lu, Han ^[1]; Shen, Qiuyang ^[2]; Chen, Jiefu ^[3]; Wu, Xuqing ^[3]; Fu, Xin ^[3]; Khalil, Mohammad ^[4]; Safta, Cosmin ^[4]; Huang, Yueqin ^[5]

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1. Univ. of Houston, TX (United States); Cyentech Consulting LLC
2. Cyentech Consulting LLC, Houston, TX (United States)
3. Univ. of Houston, TX (United States)
4. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
5. Cyentech Consulting LLC, Cypress, TX (United States)

Solving a non-linear inverse problem is challenging in computational science and engineering. Sampling based methods require a large number of model valuations; gradientbased methods require fewer model evaluations but only find the local minima. Multifidelity optimization combines the low fidelity model and the high-fidelity model to achieve both high accuracy and high efficiency. In this paper, we present a bi-fidelity approach to solve non-linear inverse problems. In the bi-fidelity inversion method, the low-fidelity model is used to acquire a good initial guess, and the high-fidelity model is used to locate the global minimum. Combined with a multi-start optimization scheme, the proposed approach significantly increases the possibility of finding the global minimum for nonlinear inverse problems with many local minima. The method is tested with two toy problems and then applied to an electromagnetic well logging inverse problem, which is difficult to solve using traditional gradient-based methods. The bi-fidelity method provides promising inversion results and can be easily applied to traditional gradient-based methods.

• Research Organization: Cyentech Consulting LLC, Houston, TX (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: SC0017033; NA0003525
• OSTI ID: 1638433
• Journal Information: IEEE Journal on Multiscale and Multiphysics Computational Techniques, Journal Name: IEEE Journal on Multiscale and Multiphysics Computational Techniques Vol. 5; ISSN 2379-8793
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
bi-fidelity
gradient-based inversion
inverse problem
multi-fidelity
polynomial chaos expansion
well logging