Complex petrophysical correction in the adaptation of geological hydrodynamic models (on the example of Visean pool of Gondyrev oil field)

The authors review a method of combined porosity and volume density correction in the process of modeling the distribution of reservoir permeability. Basing on petrophysical investigations of core samples from Bashkir fold deposits, an association between rock porosity, density and permeability has been analyzed. Significant correlation has been observed for the above mentioned parameters in porous collectors in contrast to reduced correlation for dense rocks and intervals of anomalously high poroperm characteristics. For terrigene porous collectors the authors propose a model of permeability assessment based on combined porosity and density correction. A modified model was developed for Visean pool of Gondyrev oil field, where collector permeability had been calculated as a function of rock porosity and density. The modified model has been compared to the conventional one; significant differences have been detected. In the modified version maximum permeability is associated with the southern part of the pool, whereas the conventional method points out the central part and predicts lowering permeability closer to the periphery. Geological model in the modified version is more homogenous than the conventional one and has no sharp peaks and valleys. The calculations have been made that reproduce the history of field development for both permeability volumes. Authors demonstrate that total oil production obtained using the modified model has a much better correlation with the actual data. The best results from using suggested method apply to the initial stage of development due to better convergence of high-rate wells. On the whole, comparison of two methods shows that for the purposes of production history adaptation the modified model is significantly better than the conventional one. Hence, the method of density correction allows for better justification of differences in the lithology of Visean collectors, which ultimately results in higher accuracy of data on residual oil reserves in the deposit

List of macrobenthic species: Data from the siberian seas and the adjacent area of the deep-sea central arctic

An annotated species list of all macrobenthic invertebrates inhabiting the Siberian sector of the Arctic Ocean is presented. The area considered includes the Kara, Laptev and East Siberian seas and the adjacent region of the deep-sea Central Arctic. Entries on species occurrences in the database are supported by corresponding references. Species of Polychaeta, Crustacea and Echinodermata in addition contain information on bathymetric distribution. Apart from published data, 12 taxa were identified in the area for the first time. In total 1574 macrobenthic species were recorded within the considered area. The most species rich was the Kara Sea with 1184 species. The Laptev and East Siberian seas and the Central Arctic showed lower species richness with correspondingly 1105, 780 and 268 species. The much smaller numbers of species in the East Siberian Sea and in the deep-sea Central Arctic can be related to taxonomic impoverishment or/and much smaller study effort in those regions

Enhancements in Functionality of the Interactive Visual Explorer for ATLAS Computing Metadata

The development of the Interactive Visual Explorer (InVEx), a visual analytics tool for the computing metadata of the ATLAS experiment at LHC, includes research of various approaches for data handling both on server and client sides. InVEx is implemented as a web-based application which aims at the enhancing of analytical and visualization capabilities of the existing monitoring tools and facilitates the process of data analysis with the interactivity and human supervision. The current work is focused on the architecture enhancements of the InVEx application. First, we will describe the user-manageable data preparation stage for cluster analysis. Then, the Level-of-Detail approach for the interactive visual analysis will be presented. It starts with the low detailing, when all data records are grouped (by clustering algorithms or by categories) and aggregated. We provide users with means to look deeply into this data, incrementally increasing the level of detail. Finally, we demonstrate the development of data storage backend for InVEx, which is adapted for the Level-of-Detail method to keep all stages of data derivation sequence

Enhancements in Functionality of the Interactive Visual Explorer for ATLAS Computing Metadata

The development of the Interactive Visual Explorer (InVEx), a visual analytics tool for ATLAS computing metadata, includes research of various approaches for data handling both on server and client sides. InVEx is implemented as a web-based application which aims at the enhancing of analytical and visualization capabilities of the existing monitoring tools and facilitates the process of data analysis with the interactivity and human supervision. The current work is focused on the architecture enhancements of the InVEx application. First, we will describe the user-manageable data preparation stage for cluster analysis. Then, we will present the Level-of-Detail approach for the interactive visual analysis. It starts with the low detailing, when all data records are grouped (by clustering algorithms or by categories) and aggregated, we provide users with means to look deeply into this data, incrementally increasing the level of detail. And finally, the development of data storage backend for InVEx, which is adapted for the Level-of-Detail method to keep all stages of data derivation sequence

Enhancements in Functionality of the Interactive Visual Explorer for ATLAS Computing Metadata

The development of the Interactive Visual Explorer (InVEx), a visual analytics tool for the computing metadata of the ATLAS experiment at LHC, includes research of various approaches for data handling both on server and client sides. InVEx is implemented as a web-based application which aims at the enhancing of analytical and visualization capabilities of the existing monitoring tools and facilitates the process of data analysis with the interactivity and human supervision. The current work is focused on the architecture enhancements of the InVEx application. First, we will describe the user-manageable data preparation stage for cluster analysis. Then, the Level-of-Detail approach for the interactive visual analysis will be presented. It starts with the low detailing, when all data records are grouped (by clustering algorithms or by categories) and aggregated. We provide users with means to look deeply into this data, incrementally increasing the level of detail. Finally, we demonstrate the development of data storage backend for InVEx, which is adapted for the Level-of-Detail method to keep all stages of data derivation sequence