An Accurate Model for Prediction of Hydrocarbon Gas MMP Based on aLarge High-Temperature Data Set

摘要

Minimum miscibility pressure(MMP)is an important parameter when designing a miscible gas flood.Traditionally,the MMP is evaluated using slimtube tests,which are time-consuming and expensive.Sinceit is typically not feasible to test more than a few injection gases on a subset of relevant reservoir fluids,reliable methods are required to estimate the MMP.The most popular correlations are often based on datafrom low-temperature reservoirs and are not always very reliable.In this work,we make use of the new,comprehensive ADNOC PVT database,which contains morethan 100 slimtube MMP measurements for a variety of injection gases,including sour gases.We thencomplement this data source by performing equation of state(EOS)based miscibility calculations covering alarge temperature range with appropriately tuned EOS models.The combination of measured and simulateddata is then used as input for development of a general correlation.The new correlation is a modification of the Eakin-Mitch formulation.It has been tuned to a large varietyof injection gases and reservoir fluid compositions,and covers a wider temperature range.The averagedeviation is 5% and the maximum error is less than 20%.Results show that the MMP exhibits a maximumversus temperature,a feature also noticed previously during development of CO2 miscibility(Yuan et al.,2005;Alshuaibi et al.,2019).One of the novelties of this work is the identification of a more complicated temperature-dependency ofthe MMP.Furthermore,the new correlation considers not just lean gases but also rich gases and sour gases,which develop miscibility based on the combined condensing-vaporizing mechanism.We believe that thismodel is more robust because it covers a much larger parameter range compared to existing correlations.