Monitoring the rock-physics properties of the subsurface is of great importance for reservoir management. For either oil and gas applications or CO2 storage, seismic data are a valuable source of information for tracking changes in elastic properties which can be related to fluids saturation and pressure changes within the reservoir. Changes in elastic properties can be estimated with time-lapse full-waveform inversion. Monitoring rock-physics properties, such as saturation, with time-lapse full-waveform inversion is usually a two-step process: first, elastic properties are estimated with full-waveform inversion, then, the rock-physics properties are estimated with rock-physics inversion. However, multiparameter time-lapse full-waveform inversion is prone to crosstalk between parameter classes across different vintages. This leads to leakage from one parameter class to another, which, in turn, can introduce large errors in the estimated rock-physics parameters. To avoid inaccuracies caused by crosstalk and the two-step inversion strategy, we reformulate time-lapse full-waveform inversion to estimate directly the changes in the rock-physics properties. Using Gassmann's model, we adopt a new parameterization containing porosity, clay content, and water saturation. In the context of reservoir monitoring, changes are assumed to be induced by fluid substitution only. The porosity and clay content can thus be kept constant during time-lapse inversion. We compare this parameterization with the usual density-velocity parameterization for different benchmark models. Results indicate that the proposed parameterization eliminates crosstalk between parameters of different vintages, leading to more accurate estimation of saturation changes. We also show that using the parameterization based on porosity, clay content, and water saturation, the elastic changes can be monitored more accurately.
Source footprint elimination in full-waveform inversion by model extension: Application to elastic guided waves recorded by distributed acoustic sensing in unconventional reservoir
Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks
Universidade Federal do Espírito Santo（巴西联邦圣埃斯皮里图大学）；Instituto Federal do Espírito Santo（圣埃斯皮里图联邦理工学院）；Exploração e Produção Petróleo Brasileiro S.A.（巴西国家石油公司勘探与生产部门）
$\mathbf{\mathbb{E}^{FWI}}$: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Rutgers University（罗格斯大学）；Colorado School of Mines（科罗拉多矿业学院）；University of Pennsylvania（宾夕法尼亚大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；Microsoft（微软）
