Target-oriented time-lapse elastic full-waveform inversion constrained by deep learning-based prior model

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11 Scopus citations


Time-lapse (TL) seismic monitoring plays a vital role in reservoir characterization and management. Elastic full-waveform inversion (EFWI) has been applied to time-lapse seismic data to allow for a quantitative estimation of time-varying elastic properties. However, the high-resolution inversion can be computationally intense and ill-posed. To estimate the high-resolution time-lapse changes at a reasonable cost, we utilize two key techniques for the inversion: 1) we develop an elastic redatuming approach to retrieve the virtual elastic data for both base and monitor data at the target level using mainly a kinematically accurate velocity, thus, reducing the computational cost by focusing the high-resolution inversion on the target zone; 2) We integrate high-resolution well information and seismic data in the target-oriented inversion, where a high-resolution prior model is predicted by deep learning to regularize the inversion. A deep neural network (DNN) is capable of learning the mappings between the time-lapse seismic estimation and the facies interpreted from well information after the training process. Thus, we can derive a prior model for time-lapse changes by mapping the facies characterized by the property changes to the target inversion domain. We then implement the target-oriented TLEFWI regularized by the prior model, where the redatumed time-lapse elastic data and the prior model jointly contributes to the inversion result. The numerical examples validate that the proposed approach enables us to retrieve the time-lapse changes of elastic property in the target zone with improved resolution and well consistency.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Transactions on Geoscience and Remote Sensing
StatePublished - Jun 27 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-07-01
Acknowledgements: We would like to thank KAUST for all the support and SWAG members for the helpful discussions and suggestions. The Shaheen supercomputing Laboratory in KAUST provides the computational support

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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