The Vankor field development in Russia's Eastern Siberia involves the exploitation of moderately viscous oil from medium to low permeability reservoirs. The field is on production from two different layers, both of which have active aquifers and are supported by water injection. Conventional petrophysics in the field can be difficult and fluid typing is often ambiguous. Downhole fluid analysis with wireline formation testers (WFT) is frequently used to assist in the clarification of petrophysical challenges.However, formation tester operations are not straightforward. Lower permeability and higher oil viscosity means that the WFT probe does not allow sufficient area for fluid to flow. Inflatable dual packer devices are therefore required. The inflatable packer, however, brings its own challenges. With a larger volume of reservoir being investigated longer pump times are required to evacuate invaded filtrate. Additionally, issues associated with the relative permeability of water and heavier oil frequently mean that hundreds of litres of fluid often must be pumped before the fluid typing questions are resolved. In this paper we discuss the implementation of a modified formation tester tool that uses an inflatable packer arrangement configured with dual intake ports. Each of the intake ports is supported by an independent pump and fluid analysis section. Such a configuration offers two benefits. Firstly, the simultaneous operation of two pumps allows the evacuation of larger amounts of fluid in a given time. Secondly, and more importantly, the positioning of the intake ports within the dual packer module allows the tool to take advantage of the density segregation occurring in the packed off interval and to arrive at a clean oil flow much more quickly than a standard configuration. Field examples and lessons learned are provided.

One of the main issues while reservoir geological model creation is to establish the relationship between measured rock properties (e.g. porosity, permeability) and well logs values. Standard approaches consist in using deductive mathematical modeling algorithms to solve this problem. The primary objective of this study was to develop the best mathematical model for Dolgan reservoir rock characteristics estimation using all available well logs information. It is obvious that simple polynomial models for this area were not applicable due to special properties of water saturated. Matched rock properties lab measurements in reference wells and well logs data were available.In the frameworks of this article several statistical methods including various regression types and neural networks were analyzed. The best relation in terms of statistical criteria was obtained by the group method of data handling (GMDH) method. GMDH is an inductive modeling algorithm using neural network with active neurons. This approach optimizes not only model coefficients for predetermine mathematical equation but also select optimal model complexity.Multilayered algorithm of GMDH, based on polynomial reference function allowed maximizing amount of information being used from different types of well logs in reference wells for target relation. Data from neutron, density, resistivity and PS logs were the most significant for the final model of Dolgan resorvoir.So using of GMDH cybernetics algorithms may significantly increase precision of rock properties forecast for further geomodeling purposes.

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The Vankor oilfield in Eastern Siberia is characterized by multiple layers of varying types of hydrocarbons, including oils ranging in viscosity from less than 1 cp to over 20 cp, the Russian regulatory cut-off for heavy oil. It is important for every reservoir penetration to determine the type of oil encountered and also to make any possible inferences about reservoir connectivity. Wireline formation testers equipped with downhole fluid analysis (DFA) sensors acquiring color and gas-oil ratio data (GOR) are used to determine the hydrocarbon type and fluid property gradients. Then, using the emerging technology of asphaltene gradient modeling and prediction for heavier oils, we are able to help support conclusions of reservoir connectivity.In this paper we present datasets from wells where we determine the fluid type from DFA data. Additionally, we incorporate color with pressure gradient data to help build reservoir models that predict reservoir connectivity and compartmentalization.