Quantification and spatial distribution of pore-filling materials through constrained rock physics template and fluid response modelling in Paleogene clastic reservoir from Cauvery basin,India

Sands belonging to Kamalapuram Formation of Paleocene-Eocene age are deposited in Cauvery basin as incised valley fill during a regressive cycle. Here we attempt to quantify the influence of diagenesis on pore-filling materials using rock physics template constrained by geohistory modelling. Primarily, porosity–velocity and acoustic impedance – the ratio of P-wave and S-wave velocity (V_P/V_s) cross-plots are used as rock physics templates. Rock physics template has efficiently quantified pore-filling materials namely; contact cement and non-contact cement. The estimated contact cement and non-contact cement are correlated with conventional petrophysical logs within the selected depth interval. Further, this correlation is used to interpret the composition of pore-filling materials. Shallower depth intervals (I and II) exhibit moderate non-contact cement (4–5%) and insignificant contact cement (1–2% approx.) depositions. However, deeper interval (III) records a significant amount of pore-filling materials amounting average of 12% non-contact cement and 4% contact cement. Pore-filling materials demonstrate a positive correlation with the depth of burial. The fluid response is substantially affected by the degree of diagenesis, composition and spatial distribution of pore-filling materials. Shallower depth intervals (1770–1786 m and 1858–1878 m) are relatively more sensitive to fluid changes as it is affected by insignificant contact cement. The depth interval 1770–1786 m shows class II (oil) and class III (gas) amplitude variation with offset anomalies. The sand occurring in depth interval 1858–1878 m demonstrates class IIP (oil) and II (gas) anomaly. The deeper interval (2118–2170 m) is comparatively stiffer and demonstrates class I amplitude variation with offset (oil and gas sand) anomaly.     

Investigation of Environmental-Concern Trace Elements in Coal and Their Combustion Residues from Thermal Power Plants in Eastern India

Natural Resources Research - The bituminous-to-sub-bituminous quality feed coals of thermal power plants contain several environmental-concern trace elements (As, Se, Ni, Cr, Zn, Pb, Cu, Cd, Co and...     

Time-dependent diffusive interactions between dark matter and dark energy in the context of k-essence cosmology

We investigated the scenario of time-dependent diffusive interaction between dark matter and dark energy and showed that such a model can be accommodated within the observations of luminosity distanceredshift data in Supernova la(SN la)observations.We obtain constraints on different relevant parameters of this model from the observational data.We consider a homogeneous scalar field(t)driven by a k-essence Lagrangian of the form L=V(φ)F(X)with constant potential V(φ)=V,to describe the dynamics of dark energy in this model.Using the temporal behaviour of the FRW scale factor,the equation of state and total energy density of the dark fluid,extracted from the analysis of SN la(JLA)data,we have obtained the time-dependence of the k-essence scalar field and also reconstructed the form of the function F(X)in the k-essence Lagrangian.     

Crossover of cation partitioning in olivines: a combination of ab initio and Monte Carlo study

We report studies based on a combination of ab initio electronic structure and Monte Carlo (MC) technique on the problem of cation partitioning among inequivalent octahedral sites, M1 and M2 in mixed olivines containing Mg²⁺ and Fe²⁺ ions. Our MC scheme uses interactions derived out of ab initio, density functional calculations carried out on measured crystal structure data. Our results show that there is no reversal of the preference of Fe for M1 over M2 as a function of temperature. Our findings do not agree with the experimental findings of Redfern et al. (Phys Chem Miner 27:630–637, 2000), but are in agreement with those of Heinemann et al. (Eur J Mineral 18:673–689, 2006) and Morozov et al. (Eur J Mineral 17:495–500, 2005).     

Ecological consideration of trace element contamination in sediment cores from Sundarban wetland,India

This article reports on the concentration of selected trace elements (Mn, Zn, Cr, Cu, Ba, As, B, V, and Hg) and major elements (Fe and Al) from the intertidal sediment cores from Sundarban wetland, India. This is a typical meso-macrotidal estuarine area affected by domestic and industrial activities located upstream. The overall concentrations range is low to moderate, indicating the environmental conditions in the outfall zone (grain size, hydrodynamic regime, and confinement), which favors the in situ accumulation of pollutants. The extent of contamination from trace elements in Sundarban core sediments is evaluated through a two-pronged approach: (i) by determining the metal enrichment in the sediments through the calculation of Pollution Load Index (PLI), Enrichment Factor (EF) and Index of Geoaccumulation (I _geo), and (ii) by defining a potential level of biological risk by the use of quality criteria such as Threshold Effect Level (TEL) and Effects Range-Low (ERL) benchmarks. On the basis of the calculated indices, sediments are particularly enriched with Cr, Cu, B, V, and As. Those enrichments seem to be due to the fine granulometry of the regions with Fe and Mn oxi-hydroxides being the main metal carriers. Trace Elements input to the Sundarban wetland need to be kept under strict control in future specially with reference to As since, according to TEL and ERL benchmarks, it already appears to be associated with a potential biological risk.     

Goodnews Bay Platinum Resource Estimation Using Least Squares Support Vector Regression with Selection of Input Space Dimension and Hyperparameters

Resource estimation of a placer deposit is always a difficult and challenging job because of high variability in the deposit. The complexity of resource estimation increases when drill-hole data are sparse. Since sparsely sampled placer deposits produce high-nugget variograms, a traditional geostatistical technique like ordinary kriging sometimes fails to produce satisfactory results. In this article, a machine learning algorithm—the support vector machine (SVM)—is applied to the estimation of a platinum placer deposit. A combination of different neighborhood samples is selected for the input space of the SVM model. The trade-off parameter of the SVM and the bandwidth of the kernel function are selected by genetic algorithm learning, and the algorithm is tested on a testing data set. Results show that if eight neighborhood samples and their distances and angles from the estimated point are considered as the input space for the SVM model, the developed model performs better than other configurations. The proposed input space-configured SVM model is compared with ordinary kriging and the traditional SVM model (location as input) for resource estimation. Comparative results reveal that the proposed input space-configured SVM model outperforms the other two models.     

Suspended sediment yield modeling in Mahanadi River,India by multi-objective optimization hybridizing artificial intelligence algorithms

River sediment produced through weathering is one of the principal landscape modification processes on earth.Rivers are an integral part of the hydrologic cycle and are the major geologic agents that erode the continents and transport water and sediments to the oceans.Estimation of suspended sediment yield is always a key parameter for planning and management of any river system.It is always challenging to model sediment yield using traditional mathematical models because they are incapable of handling the complex non-linearity and non-stationarity.The suspended sediment modeling of the river depends on the number of factors such as rock type,relief,rainfall,temperature,water discharge and catchment area.In this study,we proposed a hybrid genetic algorithm-based multi-objective optimization with artificial neural network(GA-MOO-ANN)with automated parameter tuning model using these factors to estimate the suspended sediment yield in the entire Mahanadi River basin.The model was validated by comparing statistically with other models,and it appeared that the GA-MOO-ANN model has the lowest root mean squared error(0.009)and highest coefficient of correlation(0.885)values among all comparative models(traditional neural network,multiple linear regression,and sediment rating curve)for all stations.It was also observed that the proposed model is the least biased(0.001)model.Thus,the proposed GA-MOOANN is the most capable model,compared to other studied models,for estimating the suspended sediment yield in the entire Mahanadi river basin,India.The results also suggested that the proposed GA-MOO-ANN model is unable to estimate suspended sediment yield satisfactorily at gauge stations having very small catchment areas whereas performing satisfactorily on locations having moderate to the large catchment area.The models provide the best result at Tikarapara,the gauge station location in the extreme downstream,having the largest catchment area.