| Summary: | Oil companies propose polymer flooding techniques, but oftentimes find it difficult to convince asset teams to implement these. This is because it is much easier to estimate the return on investment from an infill well drilling programme, and the return is much quicker. On the other hand, there may be a delay of years before increased oil recovery is observed following implementation of polymer flooding process, and indeed, it may be difficult to ascertain just how much incremental oil has been recovered. The work developed in this thesis involved setting up a range of polymer flooding scenarios, performing analysis using both very detailed reservoir simulation calculations with a range of sensitivities, and also economic calculations, again testing a range of parameters, to ensure that a full range of possible outcomes is evaluated, and then making a comparison with infill drilling to maximise the value of mature assets. The method was first applied to a synthetic scenario with constant economic parameters, and was then applied and tested with varied operational and economic parameters. These sensitivity calculations have been performed by developing a computer program, coded in Java. Monte Carlo Simulation (MCS) is then performed to generate statistics from this method, and test economic uncertainties and the risks associated with implementation of polymer flooding. The method was then applied to a real field system where the choice of infill well drilling had previously been made by the operating company, to test the robustness of the analysis using polymer flooding against a conventional decision making process for which there is historical data. Finally, the approach was then used in an offshore field which has been undergoing waterflooding, but where the choice for further field development has yet to be made, with the operator considering polymer flooding as an alternative (or in addition) to infill well drilling. The thesis discusses the implications of using this newly developed methodology in identifying the risk of failure and in assisting in making an optimal choice based on technical and economic considerations in a fully integrated manner.
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