A Physics-Driven Deep-Learning Network for Solving Nonlinear Inverse Problems
- Univ. of Houston, TX (United States); Cyentech Consulting LLC
- Univ. of Houston, TX (United States)
- Cyentech Consulting LLC, Cypress, TX (United States)
Geosteering inversion, which can be viewed as a nonlinear inverse problem, is an important technique used by directional drilling. Traditional methods that rely on iterative procedures and regularization are sensitive to the selection of initial values and can be slow due to convergence issues. In industrial applications, a lookup table is used to produce fast predictions. However, performance is not guaranteed by this approach due to the limitation of the hardware. In this paper, we propose a novel physics-driven deep-learning framework for providing a fast, accurate surrogate to solve the inverse problem. Particularly, leveraged by the forward physical model and 1D convolutional neural network (1D-CNN), the proposed method provides more reliable solutions to the inverse problem with improved performance. In addition, a new physics-driven loss function is introduced to accommodate both the model misfit and the data misfit. In conclusion, our experiments demonstrate the effectiveness of our method.
- Research Organization:
- Cyentech Consulting LLC, Cypress, TX (United States); Univ. of Houston, TX (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
- Grant/Contract Number:
- SC0017033;
- OSTI ID:
- 1598532
- Journal Information:
- Petrophysics, Journal Name: Petrophysics Journal Issue: 01 Vol. 61; ISSN 1529-9074
- Publisher:
- Society of Professional Well Log AnalystsCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Hardening of DOE UQ Codes for Ultra-Deep Proactive Geosteering in Oilfield Service
Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data