Pore-to-core linkages and upscaling for gas injection (CO2 Storage, EOR) in carbonates

碳酸盐岩注气（CO2 封存，EOR）的孔隙与岩心联系和升级

• 批准号: 2872477
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2872477
• 关键词: Pore; core; linkages; upscaling; gas

The research will focus on the application of the multi scale workflow to carbonate rocks with varying pore structures and wetting states. The experimental activity will comprise a set of multi scale observations (pore scale resolution across a field of view of deca cm) of steady state drainage and imbibition in both water wet and mixed wet carbonate rocks with distinct multi scale heterogeneity. Theoretical development and rigorous validation will be performed on quarry rocks selected for pore structure characteristics. The bulk of the activity will be spent analysing scores of samples from one or more reservoirs of interest to Shell. The work will also be closely linked to project 2.2 in which machine learning pore structure classification techniques will be developed to further enhance these workflows. Significant time will be spent on the development of the data analysis workflows for application to gas injection in carbonate rocks. The optimal approach for image segmentation of pore scale rock and fluid properties in carbonates with multi scale heterogeneity, including micro porous regions, will be evaluated. Image processing workflows will be validated against independent observations, e.g., average porosity estimates from segmentation will be evaluated against inference from medical CT imagery. Workflows to construct numerical whole core models from pore and continuum scale observations will be developed with particular reference to gas injection in carbonates with varying wetting states.

研究将集中于多尺度工作流在具有不同孔隙结构和润湿状态的碳酸盐岩中的应用。实验活动将包括一组具有明显多尺度非均质性的水湿碳酸盐岩和混合湿碳酸盐岩的稳态排水和吸水的多尺度观测(孔尺度分辨率为10厘米)。将对根据孔隙结构特征选择的采石场岩石进行理论开发和严格验证。大部分工作将用于分析壳牌感兴趣的一个或多个储集层的大量样本。这项工作还将与项目2.2密切相关，在该项目中，将开发机器学习孔结构分类技术，以进一步加强这些工作流程。将花费大量时间开发用于碳酸盐岩注气的数据分析工作流程。对具有多尺度非均质性(包括微孔区域)的碳酸盐岩中孔隙尺度岩石和流体性质的图像分割的最佳方法进行了评估。图像处理工作流程将根据独立的观察结果进行验证，例如，将根据医学CT图像的推断评估分割得出的平均孔隙率估计值。根据孔隙和连续介质尺度的观测建立整个岩心数值模型的工作流程将特别参考具有不同润湿状态的碳酸盐岩中的气体注入。