Cyentech received the SBIR phase II award from DOE to develop a geosteering inversion and uncertainty quantification software product that provides proactive guidance for pre-job well placement planning and well trajectory adjustment while drilling.
Research References:
[1] Jiefu Chen, Shubin Zheng, and Yueqin Huang, “Borehole Electromagnetic Telemetry System: Theory, Modeling, and Applications, “Springer, 2019. article
[2] Qiuyang Shen, Jiefu Chen, Hanming Wang, Yueqin Huang, “Statistical Bayesian inversion of ultra-deep electromagnetic LWD data: trans-dimensional Markov chain Monte Carlo with Parallel tempering, ” IEEE International Symposium on Antennas and Propagation and USNC/ URSI National Radio Science Meeting, Atlanta, GA, USA, Jul. 2019. article
[3] Yuchen Jin, Xuqing Wu, Yueqin Huang, and Jiefu Chen, “A physics-driven deep neural network for inversion of electromagnetic logging in subsurface sensing and borehole characterization, ” IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting, Atlanta, GA, USA, Jul. 2019. article
[4] Yuchen Jin, Xuqing Wu, Jiefu Chen, and Yueqin Huang, “Using a physics-driven deep neural network to solve inverse problems for LWD azimuthal resistivity measurements,” The 60th Annual Symposium of Society of Petrophysicists and Well Log Analysts, The Woodlands, TX, USA, Jun. 2019. article
[5] Qiuyang Shen, Hanming Wang, Yueqin Huang and Jiefu Chen, “Using trans-dimensional Markov chain Monte Carlo method with parallel tempering technique for inversion of ultra-deep directional resistivity logging-while-drilling-data, “IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, Cambridge, MA, USA, May 2019. article
[6] Yuchen Jin, Xuqing Wu, Jiefu Chen, and Yueqin Huang, “A physics-driven deep learning network for subsurface inversion, ” National Radio Science Meeting, Boulder, CO, USA, Jan. 2019. article
[7] Qiuyang Shen, Xuqing Wu, Jiefu Chen, Zhu Han, and Yueqin Huang, “Solving geosteering inverse problems by stochastic hybrid Monte Carlo method, ” Journal of Petroleum Science and Engineering, vol. 161, pp. 9-16, Feb. 2018. article
[8] Yuchen Jin, Xuqing Wu, Jiefu Chen, and Yueqin Huang, “A physics-driven deep learning network for inversion of directional resistivity measurements, ” SPWLA Resistivity SIG Meeting, Houston, TX, USA, Nov. 2018.
[9] Yuchen Jin, Qiuyang Shen, Xuqing Wu, Yueqin Huang, and Jiefu Chen, “Affordable and fast geosteering inversion using a physics-driven deep learning network. ” Rice Data Science Conference, Houston, TX, USA. Oct. 2018.
[10] Yuchen Jin, Qiuyang Shen, Xuqing Wu, Yueqin Huang, and Jiefu Chen, ” Nonparametic machine learning and inverse problems for geosteering applications, ” Post Convention Workshop on Machine Learning and Data Analytics for Geosciences, The 88th SEG Annual Meeting, Anaheim, CA, USA, Oct.2018.
[11] Jiefu Chen, Yueqin Huang, Tommy L. Binford, and Xuqing Wu, “Managing Uncertainty in Large Scale Inversions for the Oil & Gas Industry, ” Guide to Bid Data Applications, edited by S. Srinivasan, Page 149-173. Springer, 2018. article
