High-resolution reservoir characterization using deep learning aided elastic full-waveform inversion: The North Sea field data example
Type
ArticleAuthors
Zhang, Zhendong
Alkhalifah, Tariq Ali

KAUST Department
Earth Science and Engineering ProgramPhysical Science and Engineering (PSE) Division
Seismic Wave Analysis Group
Date
2020-05-08Online Publication Date
2020-05-08Print Publication Date
2020-07-01Submitted Date
2019-05-27Permanent link to this record
http://hdl.handle.net/10754/661506
Metadata
Show full item recordAbstract
Reservoir characterization is an essential component of oil and gas production, as well as exploration. Classic reservoir characterization algorithms, both deterministic and stochastic, are typically based on stacked images and rely on simplifications and approximations to the subsurface (e.g., assuming linearized reflection coefficients). Elastic full-waveform inversion, which aims to match the waveforms of pre-stack seismic data, potentially provide more accurate high-resolution reservoir characterization from seismic data. However, full-waveform inversion can easily fail to characterize deep-buried reservoirs due to illumination limitations. We present a deep learning aided elastic full-waveform inversion strategy using observed seismic data and available well logs in the target area. Five facies are extracted from the well and then connected to the inverted P- and S-wave velocities using trained neural networks, which correspond to the subsurface facies distribution. Such a distribution is further converted to the desired reservoir-related parameters such as velocities and anisotropy parameters using a weighted summation. Finally, we update these estimated parameters by matching the resulting simulated wavefields to the observed seismic data, which corresponds to another round of elastic full-waveform inversion aided by the a priori knowledge gained from the predictions of machine learning. A North Sea field data example, the Volve Oil Field data set, indicates that the use of facies as prior helps resolve the deep-buried reservoir target better than the use of only seismic data.Citation
Zhang, Z., & Alkhalifah, T. (2020). High-resolution reservoir characterization using deep learning aided elastic full-waveform inversion: The North Sea field data example. GEOPHYSICS, 1–47. doi:10.1190/geo2019-0340.1Sponsors
We thank Jeffrey Shragge, Weichang Li, Wenyi Hu and two anonymous reviewers, for the effort put into the review of the manuscript. We also want to thank Equinor and the former Volve license partners ExxonMobil E&P Norway AS and Bayerngas Norge AS, for the release of the Volve data. The views expressed in this paper are the views of the authors and do not necessarily reflect the views of Equinor and the former Volve field license partners. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.Publisher
Society of Exploration GeophysicistsJournal
GEOPHYSICSAdditional Links
https://library.seg.org/doi/10.1190/geo2019-0340.1ae974a485f413a2113503eed53cd6c53
10.1190/geo2019-0340.1