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.
翻译:成功地部署地质碳储存(GCS)需要广泛使用储油层模拟器来筛选、分级和优化储存地点,然而,全球碳储存系统的时间尺度尚不具备足够的长期数据来验证模拟器。因此,目前没有坚实的基础来评估大规模全球碳储存系统作业动态的预测质量。为了弥补这一知识差距,我们进行了一项主要的全球碳储存系统验证基准研究。为了实现合理的时间尺度,建立了一个实验室规模的地质储存结构(“浮标”),作为实验和计算工作的基础。在FluidFlower进行了由多次全球碳储存系统操作构成的验证实验,提供了我们定义的这个系统的真正物理动态。全世界9个不同的研究小组根据对氟化花砂的详细物理和石油物理特征进行了单独和协作的预测。为了实现合理的时间尺度,本文件的主要贡献是编制和讨论验证基准研究的结果,并辅之以对基准运行和计算过程和定量数据储量能力的描述。我们通过对各种基准和定量数据储量的预测,从每个参与的实验室和定量数据储量的用户和定量能力进行对比。