The paper describes results of a petrophysical study aimed to improve reservoir description of a complex oil reservoir located in Western-Siberia, Russia. The K-field was put on production in 1998. The reservoir rocks were deposited in a sand-rich deltaic environment, that implies reasonably good lateral sand bodies continuity. Additionally, no evident continuous faults were seen by 3D seismic survey. However, recent field development has revealed far more complex reservoir compartmentalization and heterogeneity, than it was supposed at the exploration stage. A considerable variation of oil-water contact was encountered and large discrepancy was observed between log predicted and observed well productivity. Additionally core experiments demonstrated poor correlation between porosity and permeability with two orders of magnitude permeability variation at similar porosity values. The hydraulic flow unit (HFU) approach is used in the present study as an integrating tool for petrophysical description of the reservoir. First, we start from a rock type classification based on core data to pick out major HFU constituting the reservoir. We employ combination of routine and special core analysis data to work out a resulting rock type classification for the studied field. Along with description of the HFU classification, we present the underlying geological reasons which control FZI variation for K-field reservoir. Based on geological and physical background we select relevant logs types related to FZI and propose simple regression approach of for prediction of FZI using gamma-ray and effective porosity log readings. Predictive capabilities of proposed regression approach is compared with more complex statistical techniques and we show that regression approach have reasonable accuracy of predicted FZI comparable to Bayesian inference and artificial neural network methods. Then we use HFU distributions obtained for each logged well along with classified capillary pressure data for calculation of synthetic water saturation logs and estimation of the free water level (FWL) depth. The estimated FWL data supplemented with seismic information about possible discontinuities and faulting zones are used to update reservoir compartmentalization scheme and to delineate pay zones. The 3D spatial distribution of the HFU is obtained using geostatistical modeling techniques and integrated into the geological reservoir model to define the porosity-permeability relationships. Additionally, HFU are used in the dynamic reservoir model to assign regions of relative permeability and capillary pressure functions. The above described simple engineering application of the hydraulic flow units approach allowed us to construct an improved reservoir model and delineate a new probable pay zones, what lead to an increase of estimated oil in place by 70 %. Introduction The HFU approach is a methodology for classification of rock types and prediction of flow properties, based on sensible geological parameters and the physics of flow at pore scale. The theory of the method was originally suggested by Amaefule et al [1] and further developed by other researchers [2]. Development and application of the HFU approach is stimulated by the common problem of permeability prediction in uncored but logged wells. Classical approaches for estimation of permeability are based either on simple logarithmic regressions evaluating permeability from log-derived porosity (Eq. 1) or on empirical correlations which relate permeability to various log responses.Equation 1 Both traditional approaches are empirical and have no or little physical and geological background. The regression methods deliberately ignore experimental data scatter and predict smoothed permeability distributions, which usually do not reproduce observed variability of permeability. The other empirical correlations based on various log responses usually have limited local applicability since, they were derived for particular geological settings.

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