[论文解读] The FluidFlower International Benchmark Study: Process, Modeling Results, and Comparison to Experimental Data

Bernd Flemisch, Jan M. Nordbotten+31 more|arXiv (Cornell University)|Feb 9, 2023

Reservoir Engineering and Simulation Methods被引用 11

一句话总结

本研究通过协调九组的双盲预测试验并将预测结果与 FluidFlower 实验进行比较，来将地质碳储存建模与实验基准进行对照。

ABSTRACT

Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the "FluidFlower"), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations.

研究动机与目标

• 评估用于地质碳储存的油藏模拟器对物理基准（FluidFlower）的预测能力。
• 提供双盲预测练习，以量化预测的可重复性和对实验数据的准确性。
• 描述基准过程、参与模型与评估指标，以为模型开发和不确定性提供信息。

提出的方法

• 在基准框架中说明验证与确认与校准的区别。
• 描述 FluidFlower 实验设置，包含六种砂型、断层和 CO2 注入实验。
• 使用共同的 SRQ 和标准化的时空输出，收集并比较来自九组的预测。
• 使用密集映射（CO2 饱和度和浓度）和时间序列 SRQ（CO2 总质量、压力、相组成、对流）。
• 应用 Wasserstein 度量来比较空间分布，同时在结果之间处理质量归一化。

实验结果

研究问题

• RQ1 FluidFlower 基准测试中不同组的预测有多相似？
• RQ2 多个 SRQ 下预测结果与实验 FluidFlower 数据的吻合程度如何？
• RQ3 在预测的 CO2 羽流、溶解和对流中，关键不一致来源是什么？
• RQ4 当组在同步阶段更新预测时，预测的再现性如何？

主要发现

• 在某些条件下，预测显示的羽流形状通常相似，但在溶解速率和断层区 CO2 到达范围上存在差异，反映出不同的本构关系和网格分辨率。
• Wasserstein 距离分析量化空间差异；某些组（Heriot-Watt、Stanford）由于不同的溶解行为而表现出更大的距离。
• 对所有组，注入后总 CO2 质量大致保持不变，存在因质量离开域或数值效应导致的偏差。
• Box A 与 Box B 中的对流和相占据显示出显著的组间变异性，原因在于毛细压力关系和网格相关的溶解突发。
• 时间压力响应基本相似，但少数组在早期时刻显示出与注入相关的显著压力激增。