Target-oriented time-lapse waveform inversion using a deep learning-assisted regularization (Li et al., 2020)

Detection of the property changes in the subsurface during production is important, yet it is also very challenging considering that these changes are often very settle. The quantitative evaluation of the subsurface property obtained by full waveform inversion (FWI) allows for better monitoring of these time-lapse changes. However, high-resolution inversion is usually accompanied with a large computational burden. Besides, the resolution of inversion is limited by the bandwidth and aperture of time-lapse seismic data.

We apply a target-oriented strategy through seismic redatuming to reduce the computational cost by focusing our high-resolution delineation on a relatively small target zone. The redatuming technique enables retrieving time-lapse virtual data for the target-oriented inversion. Considering the injection and production wells are often present in the target zone, we can incorporate the well information to the time-lapse inversion by using regularization to complement the resolution and illumination at the reservoir. We use a deep neural network (DNN) to learn the mathematical relationship between the inverted model and the facies interpreted from well logs. The trained network is employed to map the property changes extracted from the wells to the target inversion domain. We then perform another time-lapse inversion, in which we fit the predicted data difference to the redatumed one from observation, as well as fit the model to the predicted velocity changes.

Figures_for_Research2_Yuanyuan_AThe numerical results demonstrate that the proposed method is capable of inverting for the time-lapse changes effectively in the target zone by incorporating the learned model information from well logs.

Figures_for_Research2_Yuanyuan_B

References

Li Y., Alkhalifah T., and Guo Q., (2020), "Target-oriented time-lapse waveform inversion using a deep learning assisted regularization", SEG Technical Program Expanded Abstracts 2020.