A convolutional neural network-accelerated workflow for stochastic seismic property estimation

As one of the major tools in resolving the nonuniqueness challenge in subsurface interpretation and reservoir characterization, stochastic inversion from post- and prestack seismic data remains challenging, which not only requires heavy computational resources but also relies on intensive manual supervision. Inspired by the recent advances in deep learning, particularly convolutional neural networks (CNNs) for interdisciplinary data integration, we develop a deep-learning workflow that enables stochastic property estimation by efficiently integrating seismic images with sparse wells. It starts with sampling a set of property prior models (PPMs) from densely measured properties at well locations and corrupting local seismic patterns with Gaussian noise. The core idea is to train a structure-guided CNN by mapping the contaminated seismic data with the sampled PPMs while enforcing structural consistency to avoid overfitting in the presence of sparse wells. Finally, the baseline and uncertainty of target properties are estimated by running multiple realizations of the trained CNN. As demonstrated by three examples, with minimum efforts of CNN architecture customization according to data availability, our workflow can accommodate various use cases, such as rock acoustic/elastic property estimation from 3D post-/angle-stack seismic data and soil geotechnical properties from 2D ultrahigh-resolution seismic data. In all examples, the machine predictions match seismic patterns well and are of high lateral consistency.