Copyright 2012, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Russian Oil & Gas Exploration & Production Technical Conference and Exhibition held in Moscow, Russia, 16-18 October 2012. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract In petroleum exploration, reservoir navigation is used for reaching a productive reservoir and placing the borehole optimally inside the reservoir to maximize production. For proper well placement, it is necessary to calculate in real-time parameters of the formation we are drilling in, and the parameters of formations we are approaching. Based on these results, a decision to change the direction of drilling could be made. Modern logging while drilling (LWD) extra-deep and azimuthal resistivity tools acquire multi-component, multi-spacing, and multi-frequency data that provide sufficient information for resolving the surrounding formation parameters. These tools are generally used for reservoir navigation and real-time formation evaluation. However, real-time interpretation software very often is based on simplified resistivity models that can be inadequate and lead to incorrect geosteering decisions. The core of the newly developed software is an inversion algorithm based on a model of transversely-isotropic layered earth with an arbitrary number of layers. The following model parameters are determined in real time: horizontal and vertical resistivities and thickness of each layer, formation dip, and azimuth. The inversion algorithm is based on the method of the most-probable parameter combination. The algorithm has good performance and excellent convergence due to its enhanced capability of avoiding local minima.