Dealing With The Spatial Synthetic Heterogeneity Of Aquifers In The North China Plain

The complexity of alluvial-pluvial fan depositional systems make detailed characterization of their heterogeneity difficult, yet such detailed characterizations are commonly needed for construction of reliable groundwater models. Traditional models mainly focus on using a single aquifer property to qualitatively or semi-quantitatively characterize heterogeneity of aquifer; so that they unable to quantitatively reflect synthetic heterogeneity of all aquifer properties. In this paper, we proposed the heterogeneity synthetic index (HSI) for the quantitative characterization of synthetic heterogeneity of aquifer. The proposed calculation process involves four steps:(1) estimation of the hydraulic conductivity of sediment sample using the Cloud-Markov model;(2) establishment of the sedimentary microfacies distribution model through the Markov chain;(3) characterization of the distribution model of hydrogeology parameter using the improved sequential simulation method according to the'facies-controlled modeling'technique; and (4) application of the entropy weight method to calculate weight coefficient of the above aquifer properties, the HSI of aquifer was calculated by superposition of these models according to the corresponding weight coefficient. This approach was applied to Luancheng aquifer deposit in the southeast Hutuo River alluvial-pluvial fan in the North China Plain (NCP).(1) In study area, grain size has a wide range of variation, so that it is difficult to estimate hydraulic conductivity using one or two empirical formulas. The Cloud model, only through established uncertain reasoning rules between grain size distribution and measured hydraulic conductivity to estimate hydraulic conductivity of sediment samples, avoided studying the determination of the functional relationship between grain size distribution, porosity, and hydraulic conductivity. Results demonstrate that the prediction error of the cloud model has Markov characteristics. This was mainly because sedimentary microfacies in the study area also had stronger Markov characteristics and these have dominant effects on the distribution of hydrogeology parameter. Since the weight Markov could accurately reflect distribution characteristics of prediction error, made the calculation precision of the Cloud-Markov model obtain significant improvement.(2) Sediment microfacies of samples were identified according to grain size parameter, R and Q factor analysis, outcrops, log data, and probability cumulative curve. Therefore, the dietribution models of aquifer properties could be established using stochastic modeling technique. As a whole, the spatial variability of sedimentary microfacies, porosity, and hydraulic conductivity was consistent. However, significant differences were found in some local areas. For instances, the sedimentary microfacies was point bar at the southwest and central study areas. At the corresponding positions, there were many "numerical" lens in hydrogeology parameter distribution model. At the northeast of study area, the sedimentary microfacies was riverbed retention deposition, the grain was thick, porosity value of0.20-0.25, but hydraulic conductivity of less than5m/d, which represents a significant difference. The consistency and difference of microfacies, porosity, and hydraulic conductivity indicates that there is some degree of correlation among these models, but it is not a linear relationship. This is visual embody of "facies-controlled modeling" theory. In the3D model, the correlation between sedimentary microfacies and porosity is stronger than that between microfacies and hydraulic conductivity. There is not obvious linear relationship between porosity and hydraulic conductivity, mainly because hydraulic conductivity is affected by others factors such as mean pore throat radius, saturation, and throat structure.(3) Aquifer1was formed in Holocene and had a porosity value of0.30-0.45, hydraulic conductivity of1-10m/d. Its synthetic heterogeneity was weak, with an HSI of0.40-0.75; Aquifer2was formed in late Pleistocene and had the highest porosity value among the four aquifers with better continuity. Hydraulic conductivity was0.04-7.5m/d, and the distribution range of hydrogeology parameter was narrow. The heterogeneity of this aquifer was weak, with an HSI of0.40-0.80; Aquifer3was formed in middle Pleistocene and had a porosity of0.20-0.30, hydraulic conductivity of1-20m/d, and wide distribution range for porosity and hydraulic conductivity. The main sedimentary microfacies consisted of depressions between alluvial-pluvial fan and riverbed retention deposition. It had the strongest heterogeneity with an HSI of0.25-0.75. Aquifer4was formed in early Pleistocene and had a porosity of0.20-0.45and hydraulic conductivity of0.04-20m/d. Their distribution range was wide and the continuity was poor. This aquifer had the second strongest synthetic heterogeneity with HSI of0.35-0.75.(4) The results demonstrated that the synthetic heterogeneity of each aquifer is different. This could be attributed to the several reasons. In middle Pleistocene epoch, the Huai River flooded for many times, thus forming many small-scale alluvial-pluvial fans with abruptly changing sedimentary microfacies, and resulting in the spatial continuity of hydrogeology parameter was poor. Heterogeneity of aquifer3was stronger. In Holocene and late Pleistocene age, the sedimentary microfacies distribution model of aquifer1and2was similar, mean porosity was higher in aquifer2, but the porosity distribution was more homogeneous than aquifer1. More importantly, the Xiao River was formed in the late Pleistocene, but its most active period was in Holocene. The alluvial-pluvial fans of the Hutuo, Huai, and Xiao Rivers overlapped at the central of the Luancheng aquifer. Thus the heterogeneity of aquifer1of Holocene was stronger than that of aquifer2of the late Pleistocene. In aquifer4, the porosity distribution was more homogeneous, so that its contribution to the synthetic heterogeneity of the aquifer was small. In contrast, sedimentary microfacies abruptly changed, so that its contribution to the synthetic heterogeneity of aquifer was more significant. Therefore, the weight coefficient of porosity was smaller than that of microfacies, which were0.279and0.398, respectively. The weight coefficient of sedimentary microfacies was the highest in all aquifers, indicating that microfacies contribute significantly to the synthetic heterogeneity of aquifer. Although the previous researches have obtained the same conclusion after qualitative analysis of sedimentary microfacies, the present study is the first time to quantitatively describe this conclusion. Findings of our study demonstrate that HSI could accurately describe the effect of each aquifer property on the synthetic heterogeneity of aquifer. During the construction of a contaminant transport model, aquifer property should be the focus of investigation because it has greater weight coefficient.