Deep-learning assisted regularized elastic full waveform inversion using the velocity distribution information from wells

Abstract

SUMMARY Elastic full waveform inversion (EFWI) can, theoretically, give high-resolution estimates of the subsurface. However, in practice, the resolution and illumination of EFWI are limited by the bandwidth and aperture of seismic data. The often-present wells in developed fields as well as some exploratory regions can provide complementary information of the target area. We, thus, introduce a regularization technique, which combines the surface seismic and well log data, to help improve the resolution of EFWI. We use deep neural networks to learn the statistical relations between some selected features of the inverted model and the facies interpreted from well logs. The selected features are the means and variances of the inverted velocities defined within Gaussian windows. Using multiple fully connected layers, we train our neural networks to identify the relation between the means and variances at the well location and those from the inverted model. The network is used to map the means and variances extracted from the well to the whole model domain. We then perform another EFWI in which we fit the predicted data to the observed ones as well as fit the model over a Gaussian window to the predicted means and variances. The tests on synthetic and real seismic data demonstrate that the proposed method can effectively improve the resolution and illumination of deep-buried reservoirs, which often encounter poor illumination from seismic data.