A CNN-accelerated workflow for stochastic seismic property estimation

工作流程 财产(哲学) 计算机科学 估计 地质学 地震学 工程类 数据库 哲学 系统工程 认识论
作者
Haibin Di,Aria Abubakar
出处
期刊:Geophysics [Society of Exploration Geophysicists]
卷期号:: 1-48
标识
DOI:10.1190/geo2024-0152.1
摘要

As one of the major tools in resolving the non-uniqueness challenge in subsurface interpretation and reservoir characterization, stochastic inversion from post- and pre-stack 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, this study proposes 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 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, the proposed workflow can accommodate various use cases, including rock acoustic/elastic property estimation from 3D post-/angle-stack seismic and soil geotechnical properties from 2D ultra-high-resolution seismic. In all examples, the machine predictions match seismic patterns well and are of high lateral consistency.

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