Characteristics of pegmatoidal granite exposed near Bayalan,Ajmer district,Rajasthan

The study involves the characterization of pegmatoidal granite, southeast of Beawar, Ajmer district, Rajasthan. Earlier researchers had described this granite as part of the BGC, basement to the Bhim Group of the Delhi Super Group rocks. However, the present study indicates that it is younger than the rocks of Bhim Group of South Delhi Fold Belt, into which it is intrusive. The intrusion is structurally controlled and the outcrop pattern is phacolithic. The granite had intruded post-D₂ deformation of the Delhi orogeny along the axial planes of D₂ folds. The intrusion has also resulted in the formation of a contact aureole about the calc gneisses.     

ARMA order model detection using minimum of Kurtosis: application on seismic data

The objective of this study is to find the order and coefficients of non-low-phase causal filters for ARMA (auto regressive moving average) filter model, using the Kurtosis minimization criterion. This method is based on the Kurtosis calculation of the treated sample at the input level and its identification at the output of the ARMA model. For this purpose, the order and coefficients of the AR (auto regressive) part are identified using the Yule-Walker algorithm at order two and then extended to order four. To obtain the MA (moving average) part, the AR components are calculated at first from the ARMA filter by deconvolution. Then, spectrally equivalent and minimum phase (SEMP) MA filter is identified using the Durbin algorithm at second and fourth order. Finally, the correct filter is found when the Kurtosis value of the output ARMA filter reconstituted is the closest to the Kurtosis of introduced signal. The proposed method is then tested on simulated processes and applied to real seismic data to perform blind deconvolution and obtain the reflectivity coefficients of subsoil studied.     

A low-dimensional subsurface model for saturated and unsaturated flow processes: ability to address heterogeneity

A low-dimensional model that describes both saturated and unsaturated flow processes in a single equation is presented. Subsurface flow processes in the groundwater, the vadose zone, and the capillary fringe are accounted for through the computation of aggregated hydrodynamic parameters that result from the integration of the governing flow equations from the bedrock to the land surface. The three-dimensional subsurface flow dynamics are thus described by a two-dimensional equation, allowing for a drastic reduction of model unknowns and simplification of the model parameterizations. This approach is compared with a full resolution of the Richards equation in different synthetic test cases. Because the model reduction stems from the vertical integration of the flow equations, the test cases all use different configurations of heterogeneity for vertical cross-sections of a soil-aquifer system. The low-dimensional flow model shows strong consistency with results from a complete resolution of the Richards equation for both the water table and fluxes. The proposed approach is therefore well suited to the accurate reproduction of complex subsurface flow processes.     

Stochastic evaluation of simple pairing approaches to reconstruct incomplete rainfall time series

Two-station pairing approaches are routinely used to infill missing information in incomplete rainfall databases. We evaluated the performance of three simple methodologies to reconstruct incomplete time series in presence of variable nonlinear correlation between data pairs. Nonlinearity stems from the statistics describing the marginal peak-over-threshold (POT) values of rainfall events. A Monte Carlo analysis was developed to quantitatively assess expected errors from the use of chronological pairing (CP) with linear and nonlinear regression and frequency pairing (FP). CP is based on a priori selection of regression functions, while FP is based on matching the probability of non-exceedance of an event from one time series with the probability of non-exceedance of a similar event from another time series. We adopted a generalized Pareto (GP) model to describe POT events, and a t-copula algorithm to generate reference nonlinearly correlated pairs of random temporal distributions distributed according with the GP model. The results suggest that the optimal methodology strongly depends on GP statistics. In general, CP seems to provide the lowest errors when GP statistics were similar and correlation became linear; we found that a power-2 function performs well for the selected statistics when the number of missing points is limited. FP outperforms the other methods when POT statistics are different and variables are markedly nonlinearly correlated. Ensemble-based results seem to be supported by the analysis of observed precipitation at two real-world gauge stations.     

Rock Mass Deformation Characteristics in High-Steep Slopes Influenced by Open-Pit to Underground Mining

In Daye Iron Mine, the open-pit mining has ended and the underground mining started in 2003. The present pit slopes are as high as 430 m and the slope angle is up to 43°. During the process of open-pit to underground mining, the high-steep pit slopes would be affected by both open-pit mining and underground mining, and its deformation characteristics would become more complex. So in this paper, the trinity method of numerical simulation, model experiment and field test was adopted to analyze the displacement and stress fields systematically. The results show that: (1) Prominent rebound deformation occurs near the slope foot, which is induced by the unloading in open-pit mining. When it is backfilled to 0 m level, the rebound deformation decreases, which indicate that backfilling mass can restrict the deformation and improve the slope stability; (2) Subsidence dominates the slope deformation in open-pit to underground mining and it increases with an increasing elevation of monitoring point; the maximum horizontal displacement occurs in the lower part of the slopes, because the backfilled part is squeezed by both the north slope and the south slope, and it has a lower elastic modulus than the previous orebody; (3) The stress and its variability near the slope foot are much larger than other places, indicating that the slope foot is most affected by stress redistribution and stress concentration may occur here; the stress at other stress monitoring points changes little, indicating that the influence of open-pit to underground mining is local; (4) The effect of underground mining on the deformation of the faults is not prominent; (5) Mining operations in near-ground part affect more on the variation of deformation and stress of pit slopes than that in deeper part.     

Haynesville Shale Well Performance and Development Potential

Louisiana’s Haynesville Shale is one of several unconventional gas plays that have been discovered in the U.S. in recent years and promise to dramatically change the course of future domestic energy development. The Haynesville Shale is the deepest, hottest, and highest pressured shale among the big four plays in the U.S. with drilling and completion cost ranging between 7 and7 and 10 million per well. The average Haynesville well has an initial production rate of 10 MMcfd and declines rapidly, producing 80% of its expected recovery during the first 2 years of production. The purpose of this article is to describe the productivity characteristics of Haynesville wells, project future production from the inventory of active wells, and assess production potential based on drilling scenarios. We offer statistical analysis of the wells drilled to date and construct type profiles to characterize the play. We estimate that the current inventory of Haynesville wells will produce 3 Tcf over their lifecycles, and within the next 3 years, cumulative build-out in the region will range between 3 and 9 Tcf. To maintain current gas production levels in the state, we estimate that about 550 shale gas wells per year will need to be brought online over the next 3 years.