基于图论有限速率侵渗模型的地震羽流观测下多层CO₂运移的贝叶斯反演
Bayesian inversion of multilayer $\mathrm{CO}_2$ migration from seismic plume observations using a graph-based finite-rate invasion-percolation model
- Norwegian University of Science and Technology（挪威科技大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
该研究针对层状砂岩储层CO₂运移的页岩屏障属性未知问题，开发了结合图论有限速率侵渗模型的贝叶斯框架，可通过时移地震羽流观测反演参数并预测CO₂运移，适用于主动注入时的快速预测修正。
中文摘要 AI 辅助
层状砂岩储层中CO₂的垂向运移受薄页岩屏障控制，而这些屏障的属性通常难以准确获知。我们开发了一种贝叶斯框架，利用时移地震羽流观测来估计控制CO₂侧向与垂向运移的有效参数。该正向模型是一种快速的基于图论的侵渗（invasion-percolation, IP）模型，它扩展了传统IP模型，同时表征了受毛细管控制的页岩屏障下方构造圈闭的充填过程，以及CO₂通过页岩屏障的有限速率传递过程。我们通过近似贝叶斯计算和序贯蒙特卡洛采样来推断参数，并将后验样本传播至预测中。将该框架应用于Sleipner的实际数据时，后验模拟重现了CO₂在9个砂层单元的广泛分布，以及其在2010年至2023年期间的再分布情况。相比之下，准静态模型无法重现这种时间演化过程。补充的合成实验评估了参数恢复、预测、监测时长及模型误设问题。这些实验表明，监测获得的信息取决于所捕获的运移事件，其中突破及突破后的再分布能提供特别强的约束。快速模拟、概率更新与可解释有效参数的结合，使得该框架非常适合在主动注入期间进行反复的预测修正，尤其是当全物理推断的计算需求过高时。
英文摘要
Vertical migration of $\mathrm{CO}_2$ in layered sandstone reservoirs is controlled by thin shale barriers whose properties are often poorly known. We develop a Bayesian framework that uses time-lapse seismic plume observations to estimate effective parameters governing lateral and vertical $\mathrm{CO}_2$ migration. The forward model is a fast graph-based invasion-percolation model that extends conventional IP by representing both capillary-controlled filling of structural traps beneath shale barriers and finite-rate transfer through them. Parameters are inferred with approximate Bayesian computation and sequential Monte Carlo sampling, and the posterior samples are propagated into forecasts. Applied to real data from Sleipner, posterior simulations reproduce the broad distribution of $\mathrm{CO}_2$ across nine sand units and its redistribution between 2010 and 2023. In contrast, the quasi-static model fails to reproduce this temporal evolution. Complementary synthetic experiments assess parameter recovery, forecasting, monitoring duration, and model misspecification. These experiments show that the information gained from monitoring depends on the migration events captured, with breakthrough and post-breach redistribution providing particularly strong constraints. This combination of fast simulation, probabilistic updating, and interpretable effective parameters makes the framework well suited to repeated forecast revision during active injection, especially when full-physics inference is too computationally demanding.
