Investigation of Hydrous Pyrolysis on Type-II Kerogen-bearing Source Rocks from Iran and its Application in Geochemical and Kinetic Analyses of Hydrocarbon Products

Hydrous pyrolysis (HP) practiced on type-II kerogen-bearing oil shale samples from the Sargelu Formation in the Ghali-Kuh Area, western Iran, using a specially designed apparatus was performed at different temperatures (250–350°C), with hydrocarbon generation evaluated at each temperature. For comparison, the samples subjected to Rock-Eval pyrolysis before proceeding to HP resulted in Tmax = 418°C, HI = 102, and TOC = 4.33%, indicating immaturity and hence remarkable hydrocarbon (especially oil) generation potential, making them appropriate for HP. Moreover, the samples were deposited in a low-energy reductive marine environment, with maximum oil and gas generation (739 mg and 348 mg out of 50 g of rock sample, respectively) observed at 330°C and 350°C, respectively. The oil generated at 330°C was subjected to gas chromatography (GC) and isotopic analyses to assess hydrocarbon quality and composition. The hydrocarbon generation data was devised to estimate kinetic indices of the Arrhenius equation and to investigate the gas–oil ratio (GOR) and overall conversion yield. Based on the producible hydrocarbon quantity and quality, the findings contribute to the economic assessment of oil shales across the study area. The developed kinetic model indicates the history of hydrocarbon generation and organic matter (OM) maturity.     

Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry

A genetic algorithm (GA)-based neuro-fuzzy approach is used for identification of geochemical anomalies by implementing a Takagi, Sugeno and Kang (TSK) type fuzzy inference system in a 5-layered feed-forward adaptive artificial neural network. This paper investigates the effectiveness of GA-based neuro-fuzzy for separating zone dispersed mineralization (ZDM) from blind mineralization, and its application for identification of geochemical anomalies in the arid landscape of the Lut metallogenic province in eastern Iran. Other classification algorithms such as metallometry, zonality, criteria, and back-propagation artificial neural network classifiers are also used for comparison. The genetic operators are carefully designed to optimize the artificial neural network, avoiding premature convergence and permutation problems. The results show that the GA-based hybrid neuro-fuzzy model can provide accurate results in comparison with those results obtained by other techniques. Neuro-fuzzy and GA-based neuro-fuzzy techniques appear to be well-suited for routine exploration geochemistry applications. In conjunction with statistics and conventional mathematical methods, hybrid approaches can be developed and may prove a step forward in the practice of applied geochemistry.     

Collapse capacity of soil‐structure systems under pulse‐like earthquakes

In this paper, a comprehensive study is carried out to examine the possibility of dynamic instability produced in soil‐structure systems using an ensemble of 50 pulse‐like records. A number of structural models with various vibration periods varying from 0.1 to 2 s are used in this study. The superstructure is simulated as a non‐linear SDOF oscillator with a two‐segment backbone curve having negative post‐yield stiffness. The soil is idealized based on the cone model concept widely used for practical purposes. The results of this investigation demonstrate that as the pulse period increases, the collapse relative lateral strength ratio decreases and probability of dynamic instability enhances. Moreover, soil flexibility makes the system dynamically more unstable, and as the non‐dimensional frequency increases, the collapse relative lateral strength ratio highly reduces. Additionally, the aspect ratio has insignificant effects on the collapse relative lateral strength ratio. Furthermore, comparison of the collapse relative lateral strength ratios resulting from pulse‐like motions with those obtained from studies under non‐pulse‐like motions (Miranda and Akkar; FEMA 440) for fixed‐base conditions shows that high‐velocity pulses exacerbate the dynamic instability problem and decrease the collapse relative lateral strength ratio. Copyright © 2014 John Wiley & Sons, Ltd.     

Geostatistical seismic inversion for non‐stationary patterns using direct sequential simulation and co‐simulation

Geostatistical seismic inversion methods are routinely used in reservoir characterisation studies because of their potential to infer the spatial distribution of the petro‐elastic properties of interest (e.g., density, elastic, and acoustic impedance) along with the associated spatial uncertainty. Within the geostatistical seismic inversion framework, the retrieved inverse elastic models are conditioned by a global probability distribution function and a global spatial continuity model as estimated from the available well‐log data for the entire inversion grid. However, the spatial distribution of the real subsurface elastic properties is complex, heterogeneous, and, in many cases, non‐stationary since they directly depend on the subsurface geology, i.e., the spatial distribution of the facies of interest. In these complex geological settings, the application of a single distribution function and a spatial continuity model is not enough to properly model the natural variability of the elastic properties of interest. In this study, we propose a three‐dimensional geostatistical inversion technique that is able to incorporate the reservoir's heterogeneities. This method uses a traditional geostatistical seismic inversion conditioned by local multi‐distribution functions and spatial continuity models under non‐stationary conditions. The procedure of the proposed methodology is based on a zonation criterion along the vertical direction of the reservoir grid. Each zone can be defined by conventional seismic interpretation, with the identification of the main seismic units and significant variations of seismic amplitudes. The proposed method was applied to a highly non‐stationary synthetic seismic dataset with different levels of noise. The results of this work clearly show the advantages of the proposed method against conventional geostatistical seismic inversion procedures. It is important to highlight the impact of this technique in terms of higher convergence between real and inverted reflection seismic data and the more realistic approximation towards the real subsurface geology comparing with traditional techniques.     

Spectroscopic-based assessment of the content and geochemical behaviour of arsenic in a highly heterogeneous sulphide-rich mine waste dump

Soil pollution by arsenic is a serious environmental problem in many mining areas. Quick identification of the amount and extent of the pollution is an important basis for developing appropriate remediation strategies. In a case study, 55 soil samples were collected from a highly heterogeneous waste dump around the Sarcheshmeh copper mine, south east Iran. Samples’ visible and near-infrared (VNIR) reflectance spectra were measured, transformed to absorbance and then pre-processed using Savitzky–Golay first-derivative (FD) and Savitzky–Golay second-derivative (SD) transformation methods. The obtained spectra were then subjected to three regression models including principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) for predicting arsenic concentration. The best prediction accuracies were obtained by SVR and PLSR methods applied on first-derivative pre-processed spectra with R ² values of 0.81 and 0.69, respectively. It was found that VNIR spectroscopy is a successful method for predicting As concentration in contaminated soils of the dumpsites. Study of the prediction mechanism showed that the intercorrelation between arsenic and spectral features of soil including iron oxy/hydroxides and clay minerals was the major mechanism enabling the prediction of arsenic concentration. However, higher values of correlation coefficients at ~460, ~560 and ~590 nm suggested the internal association between arsenic and iron minerals as the more important mechanism for prediction. This conclusion supported previous speciation studies conducted in the same waste dump using improved correlation analysis and chemical sequential extraction method.     

Seismicity patterns associated with the September 10th, 2008 Qeshm earthquake, South Iran

The b value of the Gutenberg-Richter relation and the standard deviate, Z, were calculated to investigate the temporal and spatial variations in seismicity patterns associated with the September 10th, 2008 (Mw?=?6.1) Qeshm earthquake. The temporal variations of b value illustrate a distinct dramatic drop preceding the Qeshm earthquake, and the spatial changes in b value highlight a zone with an abnormally low b value around the epicenter of this event. The cumulative number and Z value as a function of time show a precursory seismic quiescence preceding the 2008 Qeshm earthquake that observed for 1?year in a circle with R?=?50?km around its epicenter. The spatial distribution map of the standard deviate, Z, also exhibits an obvious precursory seismic quiescence region before the 2008 Qeshm event around the epicenter of this event. Interestingly, the precursory seismic quiescence region is approximately consistent with low b value anomaly region, and both have E–W to NE–SW trend. These two precursory anomalies took place in relatively large regions, which were possibly relevant to the preparation zone of the 2008 Qeshm event.     

Characterisation of the Sarcheshmeh copper mine tailings,Kerman province,southeast of Iran

Mineral processing operation at the Sarcheshmeh porphyry copper mine has produced huge quantities of tailings materials containing sulphide minerals in particular pyrite. These tailings materials were geochemically and mineralogically characterised to assess pyrite and chalcopyrite oxidation, acid mine drainage generation, and trace element mobility to lead development of a proper remediation plan. Five vertical trenches up to 4.2 m deep were excavated from the tailings surface, and 70 solid samples were taken in 0.3 m intervals. The samples were first mineralogically analysed. Pyrite was the main sulphide mineral found in the tailings. The gangue minerals include quartz ± muscovite–illite ± chlorite ± albite ± orthoclase ± halite. The samples were geochemically analysed for total concentrations of 62 elements, paste pH, SO₄ ^2?, CO₃ ^2?, and HCO₃ ^?. The maximum concentrations of SO₄ ^2? (1,300, 1,170, 1,852, 1,960 and 837 mg/L) were observed at a depth of 0.9 m in profiles A, B, C, D and E, respectively. The tailings have a high acid-producing potential and low acid-neutralising potential (pyrite 4–6 wt %, calcite 1 wt %). Fe₂(SO₄)₃, CuSO₄, MgSO₄ and MnSO₄ were the dominant secondary sulphate minerals in the tailings. The lowest pH values (2.9, 3 and 3) were measured at a depth of 0.3 m in the profiles A, B and C, 3.9 at a depth of 0.6 m in the profile D and 3 at a depth of 0.9 m in the profile E. The upper portions of the profiles C (1.8 m) and D (2.1 m) were moderately oxidised, while oxidation in the profiles A, B and E did not extend more than 1.2, 1.2 and 1.5 m beneath the tailings surface. Zn, Pb, Rb, U, Hf, Nd, Zr and Ga show almost a constant trend with depth. Cd, Sr, Th, La and Ce increased with increasing depth of the tailings materials while, Co, V, Ti, Cr, Cu, As, Mn, Ag, Mo and Ni exhibit initially a decreasing trend from tailings surface to the depths that vary between 0.9 and 1.2. They then remained constant with the depth. The results show pyrite and chalcopyrite oxidation at surface layers of the tailings and subsequent leaching of the oxidation products and trace elements by infiltrated atmospheric precipitation.     

A statistical model to relate pyrite oxidation and oxygen transport within a coal waste pile: case study,Alborz Sharghi,northeast of Iran

This paper presents a statistical model for predicting pyrite fraction remaining in a coal waste pile at the Alborz Sharghi coal mine located at northeast of Iran. This model calculates the fraction of pyrite remaining using mole fraction of oxygen diffused into the pore spaces of the pile and the pile depth. Comparison of the statistical outputs revealed that a second-order polynomial expression with respect to oxygen mole fraction and depth provides a better correspondence to the field measurements for the fraction of pyrite remaining with a RMSE of 0.089231. Besides, two statistical relationships have been proposed for the remaining pyrite fraction and the mole fraction of oxygen versus the pile depth. A quadratic polynomial shows the best correlation of the field measured data. The suggested models were successfully validated with the acceptable confidence levels of 92 and 90 % for remaining pyrite and oxygen using a new data set which revealed the fact that they can be applied in similar situations. Both statistical analysis and field data indicate that the pyrite oxidation process is limited to the shallower depths of the waste pile where the mole fraction of oxygen decreased rapidly.