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An improved technique for computing permeability from NMR measurements in mudstones

机译:一种通过核磁共振测量泥岩渗透率来计算渗透率的改进技术

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We develop a technique for extending nuclear magnetic resonance (NMR) permeability estimation to clay‐rich sediments. Our technique builds on the Schlumberger‐Doll Research (SDR) equation by using porosity, grain size, specific surface, and magnetic susceptibility data to yield more accurate permeability estimation in mudstones with large pore surface areas and complex mineralogies. Based on measurements of natural sediments as well as resedimented laboratory mixtures of silica, bentonite, and kaolinite powders, we find that our method predicts permeability values that match measured values over four orders of magnitude and among lithologies that vary widely in grain size, mineralogy, and surface area. Our results show that the relationship between NMR data and permeability is a function of mineralogy and grain geometry, and that permeability predictions in clay‐rich sediments can be improved with insights regarding the nature of the pore system made by the Kozeny theory. This technique extends the utility of NMR measurements beyond typical reservoir‐quality rocks to a wide range of lithologies.
机译:我们开发了一种将核磁共振(NMR)渗透率估算扩展到富含粘土的沉积物的技术。我们的技术建立在Schlumberger-Doll Research(SDR)方程的基础上,利用孔隙度,晶粒尺寸,比表面积和磁化率数据,可以对大孔隙表面积和复杂矿物学的泥岩进行更准确的渗透率估算。根据对天然沉积物以及二氧化硅,膨润土和高岭石粉的重新沉淀的实验室混合物的测量结果,我们发现我们的方法预测的渗透率值与四个值大小的测量值相匹配,并且在晶粒度,矿物学,和表面积。我们的结果表明,NMR数据与渗透率之间的关系是矿物学和晶粒几何形状的函数,并且通过对由Kozeny理论得出的孔隙系统性质的见解,可以改善富含粘土的沉积物中的渗透率预测。这项技术将NMR测量的实用性从典型的储层质量的岩石扩展到了广泛的岩性。

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