Conductivity model for pyrite-bearing laminated and dispersed shaly sands based on a differential equation and the generalized Archie equation

The conductance of pyrite-bearing laminated and dispersed shaly sands is not well understood and resistivity models for pyrite-bearing shaly sands are nonexistent. Thus, we first synthesize clean pyrite-matrix samples, and quartz-matrix samples with variable laminated shale, dispersed shale, and pyrite content and then perform petrophysics experiments to assess the effect of pyrite content on the conductivity of pyrite-bearing shaly sands. Second, based on the differences in conductivity and conduction pathways and geometries because of the variable composition of the pyrite-bearing laminated and dispersed shaly sands, we divide the shaly sands into their components, i.e., laminated shale, quartz grains, pyrite grains, hydrocarbon, dispersed shale, microscopic capillary water, and mobile water. A generalized resistivity model is proposed to describe the conductivity of pyrite-bearing laminated and dispersed shaly sands, based on the combined conductivity differential equation and generalized Archie equation. In the generalized resistivity model, the conductivity differential equation is used to describe the conductivity of dispersed inclusions in a host, whereas the generalized Archie equation is used to describe the conductivity of two conducting phases. Moreover, parallel conductance theory is used to describe the conductivity of dispersed shaly sands and laminated shale. Theoretical analysis suggests that the proposed model satisfies the physical constraints and the model and experimental results agree. The resistivity and resistivity index of shaly sands decrease with increasing conductivity and pyrite. Finally, the accuracy of the resistivity model is assessed based on experimental data from 46 synthetic core samples with different oil saturation. The model can describe the conductivity of clean pyrite-matrix samples, and quartz-matrix samples with different volumes of laminated shale, dispersed shale, and pyrite. An accurate saturation model of pyrite-bearing laminated and dispersed shaly sands is thus obtained and the log data interpretation in complex shaly sands can improve with the proposed model.     

Mass-front velocity of dry granular flows influenced by the angle of the slope to the runout plane and particle size gradation

The mass-front velocities of granular flows results from the joint action of particle size gradations and the underlying surfaces.However,because of the complexity of friction during flow movement,details such as the slope-toe impedance effects and momentum-transfer mechanisms have not been completely explained by theoretical analyses,numerical simulations,or field investigations.To study the mass-front velocity of dry granular flows influenced by the angle of the slope to the runout plane and particle size gradations we conducted model experiments that recorded the motion of rapid and long-runout rockslides or avalanches.Flume tests were conducted using slope angles of 25°,35°,45°,and 55° and three particle size gradations.The resulting mass-front motions consisted of three stages:acceleration,velocity maintenance,and deceleration.The existing methods of velocity prediction could not explain the slowing effect of the slope toe or the momentum-transfer steady velocity stage.When the slope angle increased from 25° to 55°,the mass-front velocities dropped significantly to between 44.4% and59.6% of the peak velocities and energy lossesincreased from 69.1% to 83.7% of the initial,respectively.The velocity maintenance stages occurred after the slope-toe and mass-front velocity fluctuations.During this stage,travel distances increased as the angles increased,but the average velocity was greatest at 45°.At a slope angle of 45°,as the median particle size increased,energy loss around the slope toe decreased,the efficiency of momentum transfer increased,and the distance of the velocity maintenance stage increased.We presented an improved average velocity formula for granular flow and a geometrical model of the energy along the flow line.     

Practical Incorporation of Multivariate Parameter Uncertainty in Geostatistical Resource Modeling

There is a need to bridge theory and practice for incorporating parameter uncertainty in geostatistical simulation modeling workflows. Simulation workflows are a standard practice in natural resource and recovery modeling, but the incorporation of multivariate parameter uncertainty into those workflows is challenging. However, the objectives can be met without considerable extra effort and programming. The sampling distributions of statistics comprise the core theoretical notion with the addition of the spatial degrees of freedom to account for the redundancy in the spatially correlated data. Prior parameter uncertainty is estimated from multivariate spatial resampling. Simulation-based transfer of prior parameter uncertainty results in posterior distributions which are updated by data conditioning and the model domain extents and configuration. The results are theoretically tractable and practical to achieve, providing realistic assessments of uncertainty by accounting for large-scale parameter uncertainty, which is often the most important component impacting a project. A simulation-based multivariate workflow demonstrates joint modeling of intrinsic shale properties and uncertainty in estimated ultimate recovery in a shale gas project. The multivariate workflow accounts for joint prior parameter uncertainty given the current well locations and results in posterior estimates on global distributions of all modeled properties. This is achieved by transferring the joint prior parameter uncertainty through conditional simulations.     

Analysis of types and changes of village-level economy in rural Gongyi city,Henan Province since 1990

Based on the research on the rural living standard in China in terms of annual net income per capita, we define six types of village-level economy, i.e. ＂to be extremely poor＂, ＂to make a basic living＂, ＂to dress warmly and eat one＇s fill＂, ＂to try to enrich （to disengage poverty）＂, ＂to be well-off＂ and ＂to be affluent＂. The data of average annual net income of all the 292 villages between 1990 and 2004 in rural Gongyi City, Henan Province were collected, verified and classified. By using standard deviation, coefficient of variation and regression analysis, it is found that the Gongyi＇s rural economy has boosted up remarkably from the relative-poverty and absolute-poverty stages in 1990 to the well-off in 2004. However, the absolute differences between villages present a trend of enlargement, while the relative differences fluctuating. On the other hand, spatial analysis of village-level economy shows that most villages with relatively high economic development level were located along national expressway and most villages with absolute-poverty lay in remote mountainous areas in 1990. Since the 1990s, the rapid urbanization and industrialization have had strongly positive effects on rural economic growth. Initial economic foundation, natural resources and traditional techniques also contribute to village economy. From the perspective of geography, villages with location advantages, such as near urban center or industrial parks, have more chances for their economic development and the ＂core-periphery＂ economic structure has been presented in the process of rural development.     

Risk assessment of maize drought disaster in southwest China using the Environmental Policy Integrated Climate model

The East Asian monsoon has a tremendous impact on agricultural production in China. An assessment of the risk of drought disaster in maize-producing regions is therefore important in ensuring a reduction in such disasters and an increase in food security. A risk assessment model, EPIC(Environmental Policy Integrated Climate) model, for maize drought disasters based on the Erosion Productivity Impact Calculator crop model is proposed for areas with the topographic characteristics of the mountainous karst region in southwest China. This region has one of the highest levels of environmental degradation in China. The results showed that the hazard risk level for the maize zone of southwest China is generally high. Most hazard index values were between 0.4 and 0.5,accounting for 47.32% of total study area. However,the risk level for drought loss was low. Most of the loss rate was 0.1, accounting for 96.24% of the total study area. The three high-risk areas were mainlydistributed in the parallel ridge–valley areas in the east of Sichuan Province, the West Mountain area of Guizhou Province, and the south of Yunnan Province.These results provide a scientific basis and support for the reduction of agricultural drought disasters and an increase in food security in the southwest China maize zone.     

A Unified Model for Black Hole X-Ray Binary Jets?

We have recently put forward a ‘unified’ semi-empirical model for the coupling between accretion and jet production in galactic black hole X-ray binaries. In this paper, we summarise this model and briefly discuss relevant considerations that have arisen since its publication.     

An Analytical Model For Threshold Crossing Rates Of Concentration Fluctuations In Dispersing Plumes

A simple analytical model is developed for the meanupcrossing rate of plume concentration fluctuations assuming that thisprocess can be well approximated by a lognormal process. The resultingexpression requires only the specification of the in-plume fluctuationintensity and in-plume Taylor micro-time scale and, hence, does notexplicitly involve the joint probability density function of theconcentration and its derivative. The analytical model provides agood fit to some field measurements of the mean upcrossing rate ina dispersing plume.     

Intercalibration of Solar Soft X-Ray Broad Band Measurements from SOLRAD 9 through GOES-12

The two-band soft X-ray observations of solar flares made by the Naval Research Laboratory’s (NRL) SOLar RADiation (SOLRAD) satellites and by the Geostationary Orbiting Environmental Satellites (GOES) operated by the National Oceanic and Atmospheric Administration’s (NOAA) Space Weather Prediction Center have produced a nearly continuous record of solar flare observations over a period of more than forty years (1969 – 2011). However, early GOES observations (i.e., GOES-2) and later (GOES-8 and subsequent missions) are not directly comparable due to changes in the conversion of measured currents to integrated fluxes in the two spectral bands that were adopted: 0.05 – 0.3 (or 0.4) nm, which we refer to as XS and 0.1 – 0.8 nm (XL). Furthermore, additional flux adjustments, using overlapping data sets, were imposed to provide consistency of flare-flux levels from mission to mission. This article evaluates the results of these changes and compares experimental GOES-8/GOES-2 results with changes predicted from modeled flare spectra. The factors by which recent GOES observations can be matched to GOES-2 are then optimized by adapting a technique first used to extrapolate GOES X-ray fluxes above saturation using ionospheric VLF radio phase enhancements. A nearly 20% increase in published GOES-8 XL data would be required to match to GOES-2 XL fluxes, which were based on observed flare spectra. On the other hand, a factor of 1.07 would match GOES-8 and later flat-spectrum 0.1 – 0.8 nm fluxes to GOES-2 XL if the latter data were converted to a flat-spectrum basis. Finally, GOES-8 observations are compared to solar soft X-ray estimates made concurrently with other techniques. Published GOES-8 0.1 – 0.8 nm fluxes are found to be 0.59 of the mean of these other determinations. Rescaling GOES to a realistic flare spectrum and removing a 30% downward adjustment applied to the GOES-8 measurements during initial data processing would place GOES-8 and later GOES XL fluxes at 0.94 of this XL mean. GOES-2 on the same scale would lie at about 0.70 of this mean. Significant uncertainties in the absolute levels of broad band soft X-ray fluxes still remain, however.     

Impact factors identification of spatial heterogeneity of herbaceous plant diversity on five southern islands of Miaodao Archipelago in North China

Herbaceous plants are widely distributed on islands and where they exhibit spatial heterogeneity. Accurately identifying the impact factors that drive spatial heterogeneity can reveal typical island biodiversity patterns. Five southern islands in the Miaodao Archipelago, North China were studied herein. The spatial distribution of herbaceous plant diversity on these islands was analyzed, and the impact factors and their degree of impact on spatial heterogeneity were identified using CCA ordination and ANOVA. The results reveal 114 herbaceous plant species, belonging to 94 genera from 34 families in the 50 plots sampled. The total species numbers on different islands were significantly positively correlated with island area, and the average α diversity was correlated with human activities, while the β diversity among islands was more affected by island area than mutual distances. Spatial heterogeneity within islands indicated that the diversities were generally high in areas with higher altitude, slope, total nitrogen, total carbon, and canopy density, and lower moisture content, pH, total phosphorus, total potassium, and aspect. Among the environmental factors, pH, canopy density, total K, total P, moisture content, altitude, and slope had significant gross effects, but only canopy density exhibited a significant net effect. Terrain affected diversity by restricting plantation, plantation in turn influenced soil properties and the two together affected diversity. Therefore, plantation was ultimately the fundamental driving factor for spatial heterogeneity in herbaceous plant diversity on the five islands.