Impedance Inversion Based on Domain Adaptation Technique With Reconstruction

Abstract

Acoustic impedance is an important seismic attribute for characterizing hydrocarbon reservoirs. With the increasing performance of machine learning (ML), many studies have tried to use ML for geophysical problems. ML-based impedance inversion can calculate impedance in an end-to-end process without a low-frequency model. However, because well log data are typically used as labeled training datasets, it is difficult to predict the impedance in areas far from the wells used to train the ML model. To overcome this problem, we propose the ML-based impedance inversion method using domain adaptation, which is a transfer learning method. Domain adaptation is an ML method that can be applied not only to a source domain with labeled data but also to a target domain without labeled data. Therefore, in this study, we predict the acoustic impedance of areas around the well and areas far from the wells, using domain adaptation. To generalize the ML model, we added a seismic data reconstruction process as a constraint and adopted a pseudo-labeling strategy. The proposed model was verified using field data from the Carnarvon Basin, WA, Australia. The domain adaptation model predicted the impedance much better than the conventional ML model. Therefore, impedance inversion using this model can be applied to the preliminary assessment of reservoirs with no well.