Facies and Petrophysical Modeling of Triassic Chang 6 Tight Sandstone Reservoir, Heshui Oil Field, Ordos Basin, China

Tight sandstone reservoirs are widely distributed worldwide. The Upper Triassic Chang 6 member of the Yanchang Formation is characterized by low permeability and porosity. The facies model offers a unique approach for understanding the characteristics of various environments also heterogeneity, scale, and control of physical processes. The role of subsurface facies features and petrophysical properties was unclear. Notable insufficient research has been conducted based on facies and petrophysical modeling and that demands to refine the role of reservoir properties. To tackle this problem, a reservoir model is to be estimated using various combinations of property modeling algorithms for discrete (facies) and continuous (petrophysical) properties. Chang 6 member consists of three main facies, i.e., channel, lobe main body, and lobe margin facies. The current research is aimed at comparing the applicability and competitiveness of various facies and petrophysical modeling methods. Further, well-log data was utilized to interpret unique facies and petrophysical models to better understand the reservoir architecture. Methods for facies modeling include indicator kriging, multiple-point geostatistics, surface-based method, and sequential indicator simulation. Overall, the indicator kriging method preserved the local variability and accuracy, but some facies are smoothed out. The surface-based method showed far better results by showing the ability to reproduce the geometry, extent, connectivity, and facies association. The multiple-point geostatistics (MPG) model accurately presented the facies profiles, contacts, geometry, and geomorphological features. Sequential indicator simulation (SIS) honored the facies spatial distribution and input statistical parameters. The porosity model built using sequential Gaussian simulation (SGS) showed low porosity (74% values <2%). Gaussian random function simulation (GRFS) models showed very low average porosity (8%-10%) and low permeability (less than 0.1 mD). These methods indicate that Chang 6 member is a typical unconventional tight sandstone reservoir with ultralow values of petrophysical properties.

An Uncertainty Assessment of Human Health Risk for Toxic Trace Elements Using a Sequential Indicator Simulation in Farmland Soils

Toxic trace elements in farmland soils are potential threats to human health. In this study, we collected soil samples from the farmlands of southern Guangzhou. We used a sequential indicator simulation (SIS) to deal with the problem of skewed distribution in the sample data. We assessed the human health risks, as well as the uncertainties, of five toxic trace elements: arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and mercury (Hg). The results were as follows: (1) The risk indices of two trace elements (Cd and Hg) were less than the standard threshold, which means that there was no human health risk due to Cd and Hg in the study area. However, the maximum risk indices of As, Cr, and Pb exceeded the standard threshold. In particular, the maximum risk index of Pb was twice the standard threshold; (2) The risk probabilities of As and Cr were less than 25% in most areas, and only a few parcels of farmland have a 100% risk probability. The risk map of Pb was used to identify contiguous areas of high-risk probability (i.e., 75%–100%) in the center of the study area. (3) E-type estimation by the SIS method overestimates the risk when the number of samples with concentrations above the threshold have a large proportion of total samples. Our conclusions are as follows: (1) The simulation results show that areas with high-risk indices were concentrated in the Panyu District, which is close to the Pearl River and the core urban area of Guangzhou; (2) Except for Pb, these trace elements are not likely to pose health risks in southern Guangzhou; (3) This study considers the risk probability found with the SIS method to be more reliable for visualizing regional risk.