Introducing a Novel Approach for Oil-Oil Correlation based on Asphaltene Structure:X-ray Diffraction

Asphaltenes have always been an attractive subject for researchers. However, the application of this fraction of the geochemical field has only been studied in a limited way. In other words, despite many studies on asphaltene structure, the application of asphaltene structures in organic geochemistry has not so far been assessed. Oil-oil correlation is a well-known concept in geochemical studies and plays a vital role in basin modeling and the reconstruction of the burial history of basin sediments, as well as accurate characterization of the relevant petroleum system. This study aims to propose the X-ray diffraction (XRD) technique as a novel method for oil-oil correlation and investigate its reliability and accuracy for different crude oils. To this end, 13 crude oil samples from the Iranian sector of the Persian Gulf region, which had previously been correlated by traditional geochemical tools such as biomarker ratios and isotope values, in four distinct genetic groups, were selected and their asphaltene fractions analyzed by two prevalent methods of XRD and Fourier-transform infrared spectroscopy (FTIR). For oil-oil correlation assessment, various cross-plots, as well as principal component analysis (PCA), were conducted, based on the structural parameters of the studied asphaltenes. The results indicate that asphaltene structural parameters can also be used for oil-oil correlation purposes, their results being completely in accord with the previous classifications. The average values of distance between saturated portions (dr) and the distance between two aromatic layers (dm) of asphaltene molecules belonging to the studied oil samples are 4.69? and 3.54?, respectively. Furthermore, the average diameter of the aromatic sheets (La), the height of the clusters (Lc), the number of carbons per aromatic unit (Cau), the number of aromatic rings per layer (Ra), the number of sheets in the cluster (Me) and aromaticity (fa) values of these asphaltene samples are 10.09?, 34.04?, 17.42?, 3.78?, 10.61? and 0.26?, respectively. The results of XRD parameters indicate that plots of dr vs. dm, dr vs. Me, dr vs. fa, dm vs. Lc, Lc vs. La, and fa vs. La perform appropriately for distinguishing genetic groups. A comparison between XRD and FTIR results indicated that the XRD method is more accurate for this purpose. In addition, decision tree classification, one of the most efficacious approaches of machine learning, was employed for the geochemical groups of this study for the first time. This tree, which was constructed using XRD data, can distinguish genetic groups accurately and can also determine the characteristics of each geochemical group. In conclusion, the obtaining of structural parameters for asphaltene by the XRD technique is a novel, precise and inexpensive method, which can be deployed as a new approach for oil-oil correlation goals. The findings of this study can help in the prompt determination of genetic groups as a screening method and can also be useful for assessing oil samples affected by secondary processes.     

Consistency of seven different GNSS global ionospheric mapping techniques during one solar cycle

In the context of the International GNSS Service (IGS), several IGS Ionosphere Associated Analysis Centers have developed different techniques to provide global ionospheric maps (GIMs) of vertical total electron content (VTEC) since 1998. In this paper we present a comparison of the performances of all the GIMs created in the frame of IGS. Indeed we compare the classical ones (for the ionospheric analysis centers CODE, ESA/ESOC, JPL and UPC) with the new ones (NRCAN, CAS, WHU). To assess the quality of them in fair and completely independent ways, two assessment methods are used: a direct comparison to altimeter data (VTEC-altimeter) and to the difference of slant total electron content (STEC) observed in independent ground reference stations (dSTEC-GPS). The main conclusion of this study, performed during one solar cycle, is the consistency of the results between so many different GIM techniques and implementations.     

A new rating system approach for risk analysis of rock slopes

In this paper, an approach is presented to analyze the stability risk of rock slopes based on a new rating system. Three factors are used to estimate the risk level of rock slopes: (1) failure probability, (2) element at risk rating, and (3) vulnerability rating. Element at risk and vulnerability ratings are both given a range from 0 to 10, and the probability of failure is varied between 0 and 1, so the risk rating ranges between 0 and 100. This risk rating can be used to determine both the quantitative and qualitative risk levels of slopes at the same time. The method is tested on the western sector of the slopes facing Songun copper plant phase III, Iran, to clarify its procedures and assess its validity. Deterministic kinematic analyses showed that the slope has a potential for circular failure. Risk assessments revealed that the risk levels of the slope in both static and pseudo-static conditions are “very low” and “high,” respectively.     

Assessment of the hurricane-induced power outages from a demographic,socioeconomic, and transportation perspective

Natural disasters have devastating effects on the infrastructure and disrupt every aspect of daily life in the regions they hit. To alleviate problems caused by these disasters, first an impact assessment is needed. As such, this paper focuses on a two-step methodology to identify the impact of Hurricane Hermine on the City of Tallahassee, the capital of Florida. The regional and socioeconomic variations in the Hermine’s impact were studied via spatially and statistically analyzing power outages. First step includes a spatial analysis to illustrate the magnitude of customers affected by power outages together with a clustering analysis. This step aims to determine whether the customers affected from outages are clustered or not. Second step involves a Bayesian spatial autoregressive model in order to identify the effects of several demographic-, socioeconomic-, and transportation-related variables on the magnitude of customers affected by power outages. Results showed that customers affected by outages are spatially clustered at particular regions rather than being dispersed. This indicates the need to pinpoint such vulnerable locations and develop strategies to reduce hurricane-induced disruptions. Furthermore, the increase in the magnitude of affected customers was found to be associated with several variables such as the power network and total generated trips as well as the demographic factors. The information gained from the findings of this study can assist emergency officials in identifying critical and/or less resilient regions, and determining those demographic and socioeconomic groups which were relatively more affected by the consequences of hurricanes than others.     

Bivariate frequency analysis of low flow using copula functions (case study: Dez River Basin,Iran)

Accurate estimation of low flow as a criterion for different objectives in water resource management, including drought is of crucial importance. Despite the complex nature of water deficits, univariate methods have often been used to analyze the frequency of low flows. In this study, low flows of Dez River basin were examined during period of 1956–2012 using copula functions at the upstream of headbranches’ junction. For this purpose, at first 7-day series of low flow was extracted at the studied stations, then their homogeneity was examined by Mann–Kendall test. The results indicated that 7-day low flow series of Dez basin were homogenous. In the next stage, 12 different distribution functions were fitted onto the low flow data. Finally, for Sepid Dasht Sezar (SDS), Sepid Dasht Zaz (SDZ), and Tang Panj Bakhtiyari (TPB) stations, logistic distribution had the best fit, while for Tang Panj Sezar (TPS) station, GEV distribution enjoyed the best fit. After specifying the best fitted marginal distributions, seven different copula functions including Ali–Mikhail–Haq (AMH), Frank, Clayton, Galambos, Farlie–Gumbel–Morgenstern (FGM), Gumbel–Hougaard (GH), and Plackett were used for bivariate frequency analysis of the 7-day low flow series. The results revealed that the GH copula had the best fitness on paired data of SDS and SDZ stations. For TPS and TPB stations, Frank copula has had the best correspondence with empirical copula values. Next, joint and conditional return periods were calculated for the low flow series at the upstream of branches’ junction. The results of this study indicated that the risk of incidence of severe drought is higher in upstream stations (SDZ and SDS) when compared with downstream stations (TPB and TPS) in Dez basin. Generally, application of multivariate analysis allows researchers to investigate hydrological events with a more comprehensive view by considering the simultaneous effect of the influencing factors on the phenomenon of interest. It also enables them to evaluate different combinations of required scenarios for integrated management of basin and planning to cope with the damages caused by natural phenomena.     

Assessment of hydrochemistry and heavy metals pollution in the groundwater of Ardestan mineral exploration area,Iran

The aim of this research is to determine the effective factors on the hydrogeochemistry and assessment of heavy metals pollution indices in the groundwater of Ardestan copper exploration area, Iran. In this study, in total, 111 groundwater samples from one well and several qanats and springs were collected. Piper and Stiff diagrams and statistical methods and quality indices were applied to hydrochemical data. Afterward, the water samples were classified into four groups, namely CaHCO3, CaCl, NaCl, and NaHCO3. According to evaluation indices, considering the fact that only a few points are in the high risk level, the groundwater of the study area has a low level of pollution. On the other hand, the pH of the groundwater of the region was mostly neutral and acid mine drainage was not found. Since the condition of the area is in pre-mining process, two factors are possibly influential: (1) rocks or minerals having sulfides are not exposed to an atmosphere (normally below groundwater) and H⁺ release does not occur and (2) the existence of minerals containing silicate and carbonate that can rapidly reduce acidification of water. Finally, it seems that during mining and in post-mining conditions, acid rock drainage which results from the oxidation of sulfides will probably influence the quality of water resources in Ardestan city. This is because the groundwater flow direction is from the mine toward Ardestan plain.     

A proposed spatial index to prioritize damaged buildings for allocating USAR operations

A spatial index using fuzzy hierarchical analysis (FAHP) is proposed in this study for prioritizing damaged buildings in the allocation of search and rescue operations after the earthquake disaster. The relevant prioritization criteria have been identified through literature review and interviews with 22 relief managers; the relative importance of these criteria and sub criteria has been computed using the FAHP method. The GIS layers equivalent to the selected criteria were prepared and integrated with one another after normalization in the GIS platform. The proposed method to prioritize the damaged buildings was implemented in the city of Varzeghan in the East Azerbaijan province of Iran. The obtained priority map, with five prioritization classes, is presented. Single-parameter sensitivity analysis method identifies the criteria ‘hazardous facilities’, ‘degree of building blockage’ and ‘chance of survival’ as the most effective criteria for prioritizing damaged buildings.     

Catchment scale geostatistical simulation and uncertainty of soil erodibility using sequential Gaussian simulation

Soil erodibility (K) affects sediment delivery to streams and needs to be appropriately quantified and interpolated as a fundamental geographic variable for implementing suitable catchment management and conservation practices. The spatial distribution of K for erosion modelling at non-sampling grid locations has traditionally been estimated using interpolation algorithms such as kriging which do not adequately represent the uncertainty of estimates. These methods cause smoothing effects through overestimating the low values and underestimating the large values. In this study observed values were used to implement a sequential Gaussian simulation (SGS) procedure to evaluate the certainty of modelled data. Soil erodibility values were computed using 41 soil samples taken from the top 10 cm soil layer regularly distributed across four catchments, 367–770 ha in area, within Kangaroo River State forest, New South Wales (NSW). One hundred realisations were applied in the simulation process to provide spatial uncertainty and error estimates of soil erodibility. The results indicated that values simulated by the SGS algorithm produced similar K values for the neighbouring cells. At the pixel level, the SGS approach generated a reliable estimation of soil erodibility in most areas. Spatial variation of the K factor in this study was strongly related to soil landscape differences across the catchments; within catchments slope gradient did not have a substantial impact on the numerical values of the K factor using pixel-by-pixel comparisons of raster grid maps.     

Correlation of lineaments and groundwater quality in Dasht-e-Arjan Fars,SW of Iran

The NE-oriented Dasht-e-Arjan graben is located 65 km west of Shiraz and has resulted from the active Kare-e-Bas fault segmentations. This extensional graben bounded by two fault system east-Arjan and west-Arjan to the Shahneshin and Salamati anticline. In these study using Landsat 7 ETM images with resolution 2.5 m and directional filtering in the four azimuths and semi-automatic technique for linear structure in the study area. Using the obtained data from extracted lineaments, the rose diagrams of the main strike lineaments are well confirm with field measurements of faults with N56° ± 4°E direction. The structural lineaments of the study area show that the Dasht-e-Arjan area is underlain by the limestone, sandstone, and marl. LANDSAT imagery of the area has been analyzed and interpreted in order to determine the lineament and groundwater quality across the area. The fracture is structurally controlled and mostly influences both the groundwater and surface water pollution and flow directions in the Dasht-e-Arjan. Using visual interpretation, determining the lineaments on the satellite image is very difficult and subjective, and it requires an experienced interpreter. In this study, the lineament analysis is undertaken to examine the orientation of the lineament, the relationship between lineaments and tectonic features and groundwater quality. Lineament density maps show that the lineament density is high around areas. Areas having high lineament density represent areas with relatively high groundwater pollution. Field observations agreed with the results from the analysis of the imagery.     

Using artificial neural network (ANN) in prediction of collapse settlements of sandy gravels

Collapse settlement is one of the main geotechnical hazards, which should be controlled during first impoundment stage in embankment dams. Imposing large deformations and significant damages to dams makes it an important phenomenon, which should be checked during design phases. Also, existence of a variety of contributing parameters in this phenomenon makes it difficult and complicated to well predict the potential of collapse settlement. Thus, artificial neural networks, which are commonly applied by majority of geotechnical engineers in predicting various perplexing problems, can be efficiently used to calculate the value of collapse settlement. In this paper, feedforward backpropagation neural networks are considered. And three-layered FFBPNNs with the architectures of 4–6–2 and 4–9–2 accurately predicted the coefficient of stress release and collapse settlement value, respectively. These networks were trained using 180 datasets gained from large-scale direct shear test, which were carried out on gravel materials. High correlation between measured and predicted values for both collapse settlement and coefficient of stress release can be easily understood from the coefficient of determination and root mean square error. It is shown that sand content and normal stress applied to the specimens, respectively, are most effective parameters on the collapse settlement value and coefficient of stress release.