Abstract

Russkoye oil field with gas cap, discovered in 1968, is one of the biggest high-viscous oil fields in Russia. It is a challenging field to develop commercially because of its geological complexity, unconsolidated formations, permafrost zone, and high heterogeneous compartmentalized reservoir with a large gas cap, active bottom aquifer, and remote location from main infrastructure. Consequently, a "generic modeling" approach was developed to evaluate the feasibility application of the following Enhanced Oil Recovery (EOR) techniques for this field: cold waterflood, hot waterflood, Water Alternating Gas (WAG), polymers and Alkaline-Surfactant-Polymer (ASP) injection.

The generic model consists of two main parts: a simulation model and a response surface model (RSM) equation.

The simulation model was constructed with uncertainty parameters to represent any combination of reservoir properties and any reservoir situation: large and small gas caps and just oil rim with bottom aquifer. Sensitivity analysis performed for selecting the most influencing parameters. Then a reasonably broad variation of the key parameters considered to successfully constructing the RSM equation. The Monte-Carlo technique was applied to get stochastic production profiles, which enable a rapid and reliable forecast of cumulative production, injection and recovery factor ranges.

The five field development technologies selected were analyzed using this generic modeling approach, prior to the commercial field development to maximize the recoverable reserves. Probabilistic profiles were estimated, taking into account the risk associated with the uncertainty in order to compare and rank selected EOR methods.

The generic modeling approach enables to make recommendations for data acquisition strategy in order to mitigate the influence of the key uncertainties on the commercial development. This approach gives to an engineer fast forecasting tool for estimation of production profiles by changing of boundaries of uncertainty parameters which reflects the change of circumstances after new data acquisition process conducted.

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