Time-lapse (TL) monitoring of the elastic property changes in the reservoir of interest is important for optimizing the reservoir interpretation and development plan. Given that elastic full-waveform inversion (EFWI) provides quantitative estimations of the elastic properties (Vp and Vs), its application to time-lapse elastic data is of considerable interest. For practical applications in reservoir monitoring, we need EFWI to provide high-resolution reservoir information at a reasonable cost. Thus, we develop an elastic redatuming technique to provide the required virtual elastic data for a target-oriented inversion, thus improving the computational efficiency by focusing our full-band inversion on the target zone. To improve the inversion resolution, we combine the well information and seismic data in the proposed time-lapse inversion approach using a regularized objective function. To derive the required prior model, we train a deep neural network (DNN) to learn the connection between the seismic estimation and the facies interpreted from well logs. We then apply the trained network to the target inversion domain to predict a prior model. Given the prior model, we perform another time-lapse inversion. We fit the simulated data difference for the virtual survey to the redatumed one from the surface recording and fit the model changes to the predicted prior model. The numerical results demonstrate that the proposed method enables the recovery of the time-lapse changes effectively in the target zone by incorporating the learned model changes from well logs.
Target-oriented time-lapse elastic full-waveform inversion assisted by deep learning with prior information
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Li, Yuanyuan, Bakulin, Andrey, Nivlet, Philippe, Smith, Robert, and Tariq Alkhalifah. "Target-oriented time-lapse elastic full-waveform inversion assisted by deep learning with prior information." Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, Denver, Colorado, USA and online, September 2021. doi: https://doi.org/10.1190/segam2021-3581711.1
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