How Porosity and Permeability Vary Spatially With Grain Size, Sorting, Cement Volume, and Mineral Dissolution In Fluvial Triassic Sandstones: The Value of Geostatistics and Local Regression
How Porosity and Permeability Vary Spatially With Grain Size, Sorting, Cement Volume, and Mineral Dissolution In Fluvial Triassic Sandstones: The Value of Geostatistics and Local Regression
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DOI：
10.2110/jsr.2011.71
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发表时间：
2011-12
影响因子：
2
通讯作者：
J. McKinley;P. Atkinson;C. Lloyd;A. Ruffell;R. Worden
中科院分区：
文献类型：
--
作者：
J. McKinley;P. Atkinson;C. Lloyd;A. Ruffell;R. Worden
尽管众所周知，砂岩的孔隙度和渗透率受一系列参数控制，如粒度和分选、成岩胶结物的数量、类型和位置、压实的程度和类型以及粒间和粒内次生孔隙的产生，这些控制参数如何在岩石体积中联系起来，（在层内和层之间）以及它们如何在空间上相互作用以确定孔隙度和渗透率。为了解决这些未知因素，本研究采用现场测井、200个点的探头渗透率测定和网格化岩石表面100个点的采样，研究了英国三叠纪河流砂岩露头。这些现场观察还得到了激光粒度分析、原生和成岩矿物学的薄片点计数分析、定量XRD矿物分析以及对所有100个样本的SEM/EDAX分析的补充。这些数据进行了分析，使用全球回归，变差，克里金，条件模拟，地理加权回归研究孔隙度和渗透率及其潜在的控制之间的空间关系。整个露头数据集的双变量分析（全局回归）结果表明，渗透率、孔隙度及其成岩和沉积控制因素之间的相关性很弱，并提供了关于主要结构结构（如粒度和分选）作用的非常有限的信息。进一步细分数据集的层理单元揭示了更多的孔隙度和渗透率的局部控制的细节。一种替代的地质统计学方法结合当地的建模技术（地理加权回归; GWR）随后被用来检查孔隙度和渗透率的空间变异性及其控制。GWR的使用不需要事先了解层理单元之间的划分，但GWR的结果与层理单元的回归分析结果大致一致，并更清楚地说明孔隙度和渗透率及其控制因素如何横向和纵向变化。每个层的沉积岩相、成岩作用、渗透率和孔隙度之间的密切关系表明，它们相互影响，反过来，如何通过整合古环境重建、地层学、矿物学和地质统计学来增强对储层性质的理解。
Although it is well known that sandstone porosity and permeability are controlled by a range of parameters such as grain size and sorting, amount, type, and location of diagenetic cements, extent and type of compaction, and the generation of intergranular and intragranular secondary porosity, it is less constrained how these controlling parameters link up in rock volumes (within and between beds) and how they spatially interact to determine porosity and permeability. To address these unknowns, this study examined Triassic fluvial sandstone outcrops from the UK using field logging, probe permeametry of 200 points, and sampling at 100 points on a gridded rock surface. These field observations were supplemented by laser particle-size analysis, thin-section point-count analysis of primary and diagenetic mineralogy, quantitative XRD mineral analysis, and SEM/EDAX analysis of all 100 samples. These data were analyzed using global regression, variography, kriging, conditional simulation, and geographically weighted regression to examine the spatial relationships between porosity and permeability and their potential controls. The results of bivariate analysis (global regression) of the entire outcrop dataset indicate only a weak correlation between both permeability porosity and their diagenetic and depositional controls and provide very limited information on the role of primary textural structures such as grain size and sorting. Subdividing the dataset further by bedding unit revealed details of more local controls on porosity and permeability. An alternative geostatistical approach combined with a local modeling technique (geographically weighted regression; GWR) subsequently was used to examine the spatial variability of porosity and permeability and their controls. The use of GWR does not require prior knowledge of divisions between bedding units, but the results from GWR broadly concur with results of regression analysis by bedding unit and provide much greater clarity of how porosity and permeability and their controls vary laterally and vertically. The close relationship between depositional lithofacies in each bed, diagenesis, permeability, and porosity demonstrates that each influences the other, and in turn how understanding of reservoir properties is enhanced by integration of paleoenvironmental reconstruction, stratigraphy, mineralogy, and geostatistics.