How to Illuminate the Reservoir from Surface Seismic Data? Integrated Deep Learning Aided Waveform Inversion
Reservoir characterization is an essential component of oil and gas production, as well as prediction. Classic reservoir characterization algorithms, both deterministic and stochastic, are typically based on stacked images and rely on simplifications and approximations to the subsurface. Elastic full-waveform inversion, which aims to match the waveforms of pre-stack seismic data, can potentially provide more accurate high-resolution reservoir characterization from seismic data. However, full-waveform inversion can easily fail to characterize deep-buried reservoirs due to illumination limitations. We present a deep learning aided elastic full-waveform inversion strategy using observed seismic data and well logs available in the target area. Five facies are extracted from the well and then connected to the inverted P- and S-wave velocities using trained neural networks, which corresponds to the distribution of facies in the subsurface. Such a distribution is further converted to the desired reservoir-related parameters such as velocities and anisotropy parameters using a weighted summation. Finally, we further update these estimated parameters by matching the resulting simulated wavefields to the observed seismic data, which corresponds to another round of elastic full-waveform inversion aided by the a priori knowledge gained from the predictions of machine learning. A North Sea field data example, the Volve Oil Field data set, is used to demonstrate our proposed method.
AAPG Datapages/Search and Discovery Article #90364 © 2019 AAPG Middle East Geoscience Technology Workshop, Integrated Emerging Exploration Concepts: Challenges, Future Trends and Opportunities, Dhahran, Saudi Arabia, December 2-4, 2019