Injection of CO₂ into brine-saturated reservoir rocks reduces their elastic moduli and thus changes the seismic response. However, estimation of the stiffness reduction and 3D plume morphology have large uncertainty for regular Signal-to-Noise Ratio (SNR) and amount of prior geological information. This paper examines achievable accuracy of the time-lapse seismic inversion based on Stage 2C of the CO2CRC Otway Project, which was specifically designed to test the sensitivity of seismic monitoring. Thanks to rich geological dataset, we could build a set of adequate subsurface models to optimise the inversion workflow and assess its capability. Firstly, 1D stochastic simulations and analytical models are used to estimate the effects of imperfect repeatability and limited bandwidth of the seismic (SNR ≈ 4). Then, we test a prototype inversion workflow on a virtual seismic survey - full-scale time-lapse synthetic seismic produced by 3D simulations in a detailed full-earth model. This test shows that the errors associated with approximate nature of the seismic inversion algorithm reduces SNR to 2.1. Furthermore, detectable thickness of the plume reduces to 10 m when the plume is laterally finite. The key findings of the synthetic study help design the inversion workflow for the Stage 2C field data. Inverted parameters of the CO₂ plume agree well with independent measurements, including repeat pulsed-neutron logging, in- and above-zone pressure monitoring and borehole seismic. To extract the plume body from the noisy inversion output, we use a Neyman-Pearson detector augmented by a spatial connectivity constraint. The proposed extraction criterion is then employed to compare history-matched reservoir with the monitoring data. For the simulated CO₂ distribution, the proposed workflow would detect 70% of the plume areal footprint. The missed samples correspond to thin parts of the plume with low saturation, and hence their effect on the estimated total mass of CO₂ in the reservoir is minimal.

向饱和盐水储层中注入CO ₂会降低其弹性模量，从而改变地震响应。但是，对于规则的信噪比（SNR），刚度降低和3D羽状形态的估计具有很大的不确定性）和先前的地质信息量。本文基于CO2CRC Otway项目第2C阶段，研究了延时地震反演可达到的精度，该阶段是专门为测试地震监测的敏感性而设计的。得益于丰富的地质数据集，我们可以构建一组适当的地下模型来优化反演工作流程并评估其能力。首先，使用一维随机模拟和分析模型来估计不完美的可重复性和地震有限带宽（SNR）的影响。≈4）。然后，我们在虚拟地震勘测中测试原型反演工作流程-由详细的全地球模型中的3D模拟产生的完整时移合成地震。该测试表明，与地震反演算法的近似性质相关的误差使SNR降低到2.1。此外，当羽流在横向上是有限的时，羽流的可检测厚度减小到10 m。综合研究的主要发现有助于设计Stage 2C现场数据的反演工作流程。CO _2的倒置参数羽状流与独立的测量非常吻合，包括重复的脉冲中子测井，区域内和区域以上的压力监测以及井眼地震。为了从嘈杂的反演输出中提取羽状体，我们使用了由空间连通性约束增强的Neyman-Pearson检测器。然后采用提出的提取标准将历史匹配的储层与监测数据进行比较。对于模拟的CO ₂分布，建议的工作流程将检测到70％的羽状面积。漏掉的样本对应于饱和度低的羽流的稀薄部分，因此，它们对储层中估计的CO ₂总质量的影响极小。