A polymer injection pilot started in 2015 in the Rayoso Formation, Desfiladero Bayo field (Neuquen Basin, Argentina) A detailed monitoring of the pilot was planned and the amount of data anticipated led to the construction of a reservoir model with a high level of detail in order to compare the results to prognosis and to reduce the uncertainty to the business case forecast.
A specific complication was that the Rayoso Fm. had been produced to date in a commingled way with no exclusive production data before the pilot. Subsequently other data was needed to constrain the simulation model. Clearly, when the pilot started, the Rayoso Fm. was not in a virgin state (after ten years of waterflooding), so RFT data, saturation logs of infill wells, injection logs, tracer and geochemical data were used instead, adding production and injection exclusively from the Rayoso Fm. in the new wells and workovers subsequent to the pilot starting.
The history match of the water injection stage of the pilot showed things that were not initially obvious; one was that the production of one of the pilot producer wells was not consistent with the log response and core data. It was subsequently established that errors were occurring in the production tests of this well in the field that were then corrected.
Breakthrough times for water were consequently history matched and it was established that even after polymer injection started, water cut was expected to initially rise, this being key for expectation management.
As the pilot has progressed, the tracer dosed with the polymer was seen before polymer, this suggesting either the presence of a thief zone or as a methodology for estimating the polymer retention. Additionally the mismatch of tracers between the simulation and actual data was used to estimate the real injection rates into the Rayoso Fm. of commingled injectors lying near the pilot area.
In this paper we discuss how a detailed pilot simulation model can be constructed and constrained when production data specific to the formation is absent. Subsequently, when field results are different to expectation, simulation can be used as tool to establish the probable cause of the deviation from prognosis and hence identify remedial action if required. Finally, the simulation model was key to manage expectations; unrealistic expectations are one of the leading causes of pilots failing.