Estimation of optimum fine-scale permeability with image logs and core photographs

利用图像测井和岩心照片估算最佳细尺度渗透率

• 批准号: 507806-2016
• 负责人: Leung, Juliana
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=610483
• 关键词: Estimation; optimum; fine; scale; permeability

Accurate prediction of in-situ recovery performance is important to optimize reservoir development and resource management. Permeability modeling is a key element in construction of geological model for reservoir forecast, as it controls fluid flow and steam/solvent efficiency in most heavy oil recovery processes. However, obtaining representative rock samples for permeability testing in oil sands deposits is difficult and expensive. Furthermore, in-situ temperature and pressure conditions are different from those under which core plug permeability is measured. The key problem addressed by this research proposal is to make reliable estimations of permeability at the scale of high-resolution flow simulation grid cells.

准确预测原位采收率对于优化油藏开发和资源管理具有重要意义。渗透率建模是构建油藏预测地质模型的关键要素，因为它控制着大多数稠油采收过程中的流体流动和蒸汽/溶剂效率。然而，获取用于油砂矿床渗透率测试的代表性岩石样品是困难且昂贵的。此外，现场温度和压力条件与测量岩心塞渗透率的条件不同。本研究提案解决的关键问题是在高分辨率流动模拟网格单元的尺度上对渗透率进行可靠的估计。