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The Kuyumba Oil Field, Eastern Siberia: Fracture Reservoir Characterization from a Fully and Multi-Disciplinary Integrated Approach

 

Mattioni, Luca1, Olivier Fonta1, Sylvain Sarda1, Victor Ryabchenko2, Evgueni Sokolov3, Reval Mukhametzyanov4, Sergey Shlionkin5, Vladislav Zereninov5, Irina Bobb1;.(1) Beicip-Franlab, Rueil Malmaison, France (2) Slavneft-Krasnoyarskneftegaz,Krasnoyarsk, Russia (3) Slavneft Moscow, Russia (4) Sibneft E/P, Moscow, Russia;.(5) Previous HitScientificNext Hit-Analytical Center of Slavneft, Tver, Russia

 

Pre-Cambrian reservoirs represent very promising targets for petroleum Previous HitexplorationTop and production in Eastern Siberia. On Kuyumba field, this reservoir is made of an alternation of shales and thick and fractured dolomite sequences. Based on the geological information (BHI, cores, wireline logs) and reservoir engineering data (production data, PLT, welltest), we performed a multi-disciplinary study, to analyse the main types of fractures occurring within the reservoir, to predict their occurrence in the reservoir and to determine the hydraulic properties of the different fractures sets. Two main scales of fractures were first­ly predicted from the BHI images: joints and large-scale fractures (faults and fracture swarms). Vsh content and mechanical beds thickness were found to be the two main geo­logical factors controlling the fracture distribution. BHI acoustic images enabled to measure an S/T ratio (fracture spacing/bed thickness) for each fracture set and for different shaly­ness. A 3D stochastic fracture model was then generated incorporating the two scales of fractures and constrained by the reservoir shalyness and the S/T ratio. The calibration of the hydraulic properties of the fractures was achieved through the second innovative part of our own methodology: the simulation of a synthetic well test using the 3D fracture model and matched with the real data. This resulted in the calibration of the hydraulic fractures conduc­tivity for each fracture type. The values were combined with the 3D stochastic fracture model, to produce 3D fracture properties models (porosity, permeability and block size) for the considered oil field.