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首页> 外文期刊>Petrophysics: The SPWLA Journal of Formation Evaluation and Reservoir Description >Using Digital Rock Technology to Quality Control and Reduce Uncertainty in Relative Permeability Measurements
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Using Digital Rock Technology to Quality Control and Reduce Uncertainty in Relative Permeability Measurements

机译:利用数字岩技术对质量控制,减少相对渗透率测量的不确定性

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Digital rock technology (DRT) has experienced tremendous progress in the last decade, with an increasing number of companies providing imaging hardware, modeling software and digital core analysis services. While prediction remains the most discussed application of DRT, this paper discusses its use to quality control water-displacing-oil relative permeability (k(r)) experimental measurements. The relative permeability data were collected from three wells over a span of seven years, and they showed a very large spread. To identify potential outliers, we performed micro-CT imaging on six samples that were selected based on similarity in rock properties but differences in measured relative permeability behavior. The three-phase segmentation process was guided by experimental values of porosity, permeability and clay. Consistency checks verified that we could reproduce permeability, drainage capillary pressure, and gas-oil relative permeability. Water-displacing-oil relative permeability was then calculated using pore-network models for water-wet and oil-wet conditions and used to establish a maximum range for each sample. This range was instrumental in identifying suspicious behavior, and reducing uncertainty in recovery predictions by confirming potential outliers and assisting in the upgrading of the experimental relative permeability data.
机译:数字摇滚技术(DRT)在过去十年中经历了巨大进展,越来越多的公司提供成像硬件,建模软件和数字核心分析服务。虽然预测仍然是DRT最讨论的讨论,但本文讨论了其用来质量控制水置换 - 油相对渗透率(K(R))实验测量。在七年的跨度范围内从三个井收集相对渗透性数据,它们显示出非常大的差异。为了识别潜在的异常值,我们在基于岩石性质中的相似性选择的六个样品上进行了微型CT成像,但在测量的相对渗透性行为中的差异。通过孔隙,渗透性和粘土的实验值引导三相分割过程。一致性检查证明我们可以再现渗透率,排水毛细管压力和气体油相对渗透性。然后使用孔网模型计算水置换油相对渗透率,用于水湿和油湿条件,并用于为每个样品建立最大范围。该范围是识别可疑行为的仪器,并通过确认潜在的异常值并协助升级实验相对渗透性数据来减少恢复预测中的不确定性。

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