What is it about?
Liu et al. present a hybrid time-lapse methodology for monitoring CO2 sequestration that incorporates physics-based full-waveform inversion and available well logs into a data-driven machine-learning technique to mitigate the data-scarcity issue. Tests for a model based on the Kimberlina reservoir demonstrate the potential of the proposed method in predicting long-term time-lapse changes and capturing the high-resolution spatial and temporal dynamics of CO2 movement.
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Why is it important?
1. We propose an efficient ``hybrid'' workflow for real-time monitoring of the spatial and temporal dynamics of CO_2 injection that combines physics-based full-waveform inversion of seismic data with data-driven inversion using neural networks (machine learning). 2. The common problem of scarcity of training data available for ML-based time-lapse inversion is mitigated by applying a new data-generation technique that employs physics constraints. 3. We develop a convolution neural network that simultaneously predicts the spatial and temporal variations in velocity and saturation and achieves high spatial resolution in the presence of realistic noise in the input data.
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This page is a summary of: Joint Physics-Based and Data-Driven Time-Lapse Seismic Inversion: Mitigating Data Scarcity, Geophysics, October 2022, Society of Exploration Geophysicists, DOI: 10.1190/geo2022-0050.1.
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