  We propose a new method for construction of the absolute permeability map
consistent with the interpreted results of well logging and well test
measurements in oil reservoirs. Nadaraya-Watson kernel regression is used to
approximate two-dimensional spatial distribution of the rock permeability.
Parameters of the kernel regression are tuned by solving the optimization
problem in which, for each well placed in an oil reservoir, we minimize the
difference between the actual and predicted values of (i) absolute permeability
at the well location (from well logging); (ii) absolute integral permeability
of the domain around the well and (iii) skin factor (from well tests). Inverse
problem is solved via multiple solutions to forward problems, in which we
estimate the integral permeability of reservoir surrounding a well and the skin
factor by the surrogate model. The last one is developed using an artificial
neural network trained on the physics-based synthetic dataset generated using
the procedure comprising the numerical simulation of bottomhole pressure
decline curve in reservoir simulator followed by its interpretation using a
semi-analytical reservoir model. The developed method for reservoir
permeability map construction is applied to the available reservoir model (Egg
Model) with highly heterogeneous permeability distribution due to the presence
of highly-permeable channels. We showed that the constructed permeability map
is hydrodynamically similar to the original one. Numerical simulations of
production in the reservoir with constructed and original permeability maps are
quantitatively similar in terms of the pore pressure and fluid saturations
distribution at the end of the simulation period. Moreover, we obtained an good
match between the obtained results of numerical simulations in terms of the
flow rates and total volumes of produced oil, water and injected water.
