Target-oriented time-lapse waveform inversion using deep learning-assisted regularization

Abstract

Detection of the property changes in the reservoir during injection and production is important. However, the detection process is very challenging using surface seismic surveys because these property changes often induce subtle changes in the seismic signals. The quantitative evaluation of the subsurface property obtained by full-waveform inversion allows for better monitoring of these time-lapse changes. However, high-resolution inversion is usually accompanied with a large computational cost. Besides, the resolution of inversion is limited by the bandwidth and aperture of time-lapse seismic data. We have applied a target-oriented strategy through seismic redatuming to reduce the computational cost by focusing our high-resolution delineation on a relatively small zone of interest. The redatuming technique generates time-lapse virtual data for the target-oriented inversion. Considering that the injection and production wells are often present in the target zone, we can incorporate the well velocity information with the time-lapse inversion by using regularization to complement the resolution and illumination at the reservoir. We use a deep neural network to learn the statistical relationship between the inverted model and the facies interpreted from well logs. The trained network is used 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. The numerical results demonstrate that our method is capable of inverting for the time-lapse property changes effectively in the target zone by incorporating the learned model information from well logs.