Estimating plume spreading in geological CO storage reservoirs is critical for several reasons including the assessment of pore space utilization efficiency, preferential CO migration pathways and trapping. However, plume spreading critically depends on lithological heterogeneity of the reservoir and CO injection rate. It might require numerous high fidelity full physics numerical simulations to constrain the uncertainty in plume spreading for a given reservoir. This might not always be practical due to computational limitations. Hence, reduced physics approaches, such as invasion-percolation method and machine learning, could be useful to answer certain questions on plume spreading in the subsurface. This study presents a new reduced physics approach based on graph theory for estimating probable CO plume migration under very low and very high injection rates. The two end-member scenarios are assessed by performing random walk in the 3D reservoir space to constrain 20,000 possible paths of CO flow away from the injection well. The resistance to CO flow associated with each path is computed for viscous, capillary and gravity forces. The resistances are then transformed into the likelihood of CO migration along the path. The algorithm was applied to 45 reservoir models with varying degrees of lithological heterogeneity and the results were compared to those from full physics and invasion percolation simulations. The graph theory results showed a close match with the results from full physics approach for both flow regimes and with results from invasion-percolation approach for capillary-gravity dominated flow regime. The algorithm was further applied to answer key questions on reservoir screening such as pore space utilization potential. The graph theory approach was also integrated with machine learning to predict CO saturation. Testing suggested that the graph theory approach can be as much as 50 and 20 times faster than the full physics numerical simulations and invasion-percolation simulations, respectively.

中文翻译：

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基于图论的岩性非均质性硅质碎屑储层中可能的 CO2 羽流扩散估计

出于多种原因，估计地质二氧化碳储存库中的羽流扩散至关重要，包括评估孔隙空间利用效率、优先二氧化碳迁移路径和捕集。然而，羽流扩散主要取决于储层岩性非均质性和二氧化碳注入速率。它可能需要大量高保真度全物理数值模拟来限制给定储层羽流扩散的不确定性。由于计算限制，这可能并不总是实用。因此，简化的物理方法，例如入侵渗透方法和机器学习，可能有助于回答有关羽流在地下扩散的某些问题。这项研究提出了一种基于图论的新的简化物理方法，用于估计极低和极高注入速率下可能的二氧化碳羽流迁移。通过在 3D 储层空间中执行随机游走来限制 CO2 从注入井流出的 20,000 条可能路径，从而评估两个端元情景。针对粘性力、毛细管力和重力计算与每条路径相关的 CO 流阻力。然后阻力转化为二氧化碳沿路径迁移的可能性。该算法应用于 45 个具有不同岩性非均质性程度的储层模型，并将结果与​​完整物理和侵入渗流模拟的结果进行了比较。图论结果显示与两种流态的完整物理方法的结果以及毛细管重力主导流态的侵入渗滤方法的结果密切匹配。该算法进一步应用于回答储层筛选的关键问题，例如孔隙空间利用潜力。图论方法还与机器学习相结合来预测二氧化碳饱和度。测试表明，图论方法的速度分别比完整物理数值模拟和入侵渗透模拟快 50 倍和 20 倍。