Cystic dilatation and tubular cell proliferation are unique features of HIV-associated nephropathy. We studied the effect of HIV-1 envelope gp160 protein on human renal proximal tubular epithelial cells (RPTEC) and opossum kidney (OK) cells. gp160 protein enhanced (p < 0.01) the proliferation of RPTEC when compared to control cells (control 9.0 ± 1.4 vs. gp160 protein 19.3 ± 2.0 × 104 cells/ml). Similarly, gp160 protein stimulated (p < 0.001) the proliferation of OK cells. This effect of gp160 was dose dependent. Both anti-tumor necrosis factor alpha and anti-transforming growth factor beta antibodies inhibited the gp160-induced OK cell proliferation. However, the inhibitory effect of anti-tumor necrosis factor alpha antibodies was more pronounced on RPTEC when compared to the inhibitory effect of anti-transforming growth factor beta antibodies. Other HIV-1 proteins (TAT and Gag4) did not affect the growth of tubular cells. gp160 protein attenuated (p < 0.001) the synthesis of collagen type I (control 883.2 ± 42.3 vs. gpl60 579.6 ± 57.6 ng/well), but increased the production of proteoglycan (control 5,832.3 ± 426 vs. gp160 9,018.0 ± 471.1 ng/well) by RPTEC. gp160 also attenuated the synthesis of collagen type I by OK cells. We hypothesize that gp160 protein-induced altered basement membrane composition and associated cell proliferation may be contributing to tubulointerstitial lesions of HIV-associated nephropathy.
The Barmer Hill Formation is a Palaeocene lacustrine deposit occurring in the Barmer Basin of northwestern India. The lake developed entirely within the rift basin and its sediments comprise a complex of shallow to deep water deposits. In much of the northern part of the basin, the upper part of the Barmer Hill Formation is characterised by the presence of a finely laminated siliceous unit. Millimeter scale laminations in this unit consist of highly siliceous lithofacies, interbedded with organic material and subordinate clay. This syn-rift unit varies from 50 to 250 meters in thickness and is known to be hydrocarbon-bearing through much of the basin from numerous well penetrations. It is characterised by high porosities (25-35%) but low permeabilities (0.1-4mD). Hydraulic fracturing well operations in this reservoir had produced stabilized oil flows in the range of 30-250 BOPD demonstrating commerciality.Detailed reservoir characterisation of this siliceous Barmer Hill Formation unit has been carried out in advance of planned field development and further hydraulic fracturing activities. Outcrop samples and subsurface core data have been integrated to understand the unusual properties of the reservoir. Scanning Electron Microscope (SEM) examinations of subsurface core samples reveal microcrystalline quartz (chalcedony) as the main mineral present, whereas equivalent outcrop samples are dominated by both Opal-CT and chalcedony and contain opaline diatom tests. This integration of core and outcrop data indicates that the siliceous unit was likely formed through diagenetic alteration of biogenic silica from opal-A through opal-CT to microcrystalline quartz. Early oil migration may have helped to preserve the porosity in the subsurface resulting in high porosity-low permeability reservoir properties unusual for microcrystalline quartz.Geomechanical properties were assessed from bulk rock X-ray diffraction (XRD) information and core data to identify the most suitable intervals for hydraulic fracture initiation. This XRD data has been used semi-quantitatively to predict reservoir brittleness using a ternary plot of quartz-carbonate-clay abundance whereas geomechanical properties viz. Poisson's Ratio (PR), Young's Modulus (YM) and Unconfined Compressive Strength (UCS) of each units are estimated from wireline densitysonic log data and calibrated with core plugs to quantify the fracability of the units. The in-situ stress orientation is estimated from image log interpretation to predict fracture propagation orientation and aid optimal well placement.This integrated workflow combining geological, petrophysical and geomechanical properties for reservoir characterisation is being applied to support commercial development of the hydrocarbon resources of this low permeability siliceous unit.
Probabilistic modelling is one of the most frequently used methods in reservoir simulation to manage uncertainties and assess their impact on reservoir behavior/cumulative production. However, depending on the extent of the uncertainty, 100s of scenarios can be generated leaving engineers unable to meaningfully analyze this data. To remedy this an unsupervised machine learning based workflow was developed to identify unique scenarios which was then paired with an integrated dashboard to enable rapid and deep analysis. A case study was done using data from a Shell operated gas field in the North Sea. Data was first mined from 480 history matched scenarios using python; out of which 20 unique clusters were identified through K-Means clustering of pressure and saturation changes with time in each gridblock. This meant that the team had to look only at 20 scenarios instead of 480 to understand the effect of different inputs on pressure and saturation response. For enhanced analysis, an integrated visualisation dashboard was created to visualize pressure and saturation changes, production profiles and connect them back to input parameters The new methodology enabled the team to integrate different aspects of reservoir modelling from static to dynamic to surface constraints on a single dashboard, making it possible to find patterns in large volumes of data which was previously not possible. For example, a cluster was identified which had high water movement; upon inspection of input parameters it was seen that late life recovery was significantly different in this cluster as compared to others. Being able to visualize different properties of multiple scenarios simultaneously at both group and grid level is a very powerful tool that not only generates insights but significantly reduces analysis time and helps in quality checking property modelling and grid behavior. The developed workflow is quite generic in nature, capable of working with various simulators and can be extended to assessing history match quality in Assisted History Matching (AHM) and multi-scenario modelling. Key parameters impacting different scenarios were identified and the team observed 10x reduction in time and significant reduction in manpower requirements through the new approach
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