Limitations in the availability of methods to estimate recovery factor at the initial stage of petroleum exploration pushes for investigation new ways of analysing available datasets. This work investigates empirical and volumetric methods to estimate recovery factors in viscous and heavy oil reservoirs. It also investigates newly available advance screening methodology to determine tertiary/ultimate recovery factor in these reservoirs. Initially, primary recovery factor was estimated based on field analogy. Secondary recovery factor was calculated using empirical equations. In the second, main part of this work, ultimate recovery factor was approximated using data mining/machine learning approach to reveal eventual trends in viscous or heavy oil databases. Information contained within this project was used to estimate recovery factors in certain viscous oil reservoirs at the initial stage of exploration. However, after reformatting original data base, advance screening methodology would be potentially applicable to any viscous or heavy oil reservoir around the world. It was found during the project that the primary recovery factor can be successfully estimated based on the field analogy. Empirical methods can be applied in some cases. The quality of the obtained results depends on whether they were derived for conventional or heavy oil reservoirs. Based on advance screening, it was shown that tertiary/ultimate recovery factor can be successfully estimated. Final product includes development of a methodology on how to approach the recovery factor approximation in viscous or heavy oil reservoirs without production history or at early stage of the field life.


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