Production of high viscous oil from reservoirs is associated with high level of geological and technological risks. In order to make heavy oil projects economically valuable, it is important to reach sufficient levels of oil production to cover the high costs of used technologies. It is very well known that thermal methods are the ones having the highest positive impact on the heavy oil recovery. The complexity of the physical processes while implementation of such methods represents a significant engineering challenge in terms of planning and finding optimal field development scenario. Modern modeling tool such as detailed integrated model combining the reservoir and the surface network simulators allow the engineers to model the complex physical phenomena and investigate in details different "what-if" scenarios, thus allowing them to find and implement optimal solutions, minimize risks and achieve economical profitability of complex heavy oil projects.

However, prior to application of such complex models for making engineering decisions it is crucial to ensure, that they are able to deliver stable and valid results in the entire range of the possible input parameters changes. These changes of parameters can be dictated by either uncertainty of the initial data due to limited availability and quality or by actual variation as the result of complex physical phenomena. This paper describes a challenge of modeling of the shallow heavy oil field Katangli, where for 40 years the cyclic steam injection heavy oil recovery technology has been implemented. In this work, the comprehensive thermodynamic models of the reservoir and the surface infrastructure were built in order to take into account for all necessary physical effects. These models were then combined into the single "reservoir-to-surface" integrated model in order to consider the behavior of the entire system and counter influence of the both parts on each other. While building and calibration those models several important studies were performed with each part of the integrated model, which helped to better understand the process of cycling steam injection, specificities of its application and modeling, and to address issues related to initial data availability, quality and variability and their potential impact on engineering decisions in future.