Minimum miscibility pressure (MMP) is a key parameter in the design of gasfloods. Injection-gas compositions often vary during the life of a gasflood owing to reinjection and mixing of fluids in situ. Understanding the impact of the gas compositional changes on the MMP is essential to optimal design of fieldwide pressure management and carbon dioxide (CO2) use. Determining the MMP by slimtube or other methods for each possible variation in the gasmixture composition is impractical. This paper gives an easy and accurate way to determine impure CO2 MMPs for variable field solvent compositions on the basis of just a few MMPs. Alternatively, the approach could be used to estimate the enrichment level required to lower the MMP to a desired pressure. The MMP-estimation method relies on determining the MMP for pure CO2 injection, and also for a few impure binary MMPs at small CO 2-contaminant levels. The number of MMPs needed for the method is equal to the number of components in the injection gas. We use the method of characteristics (MOC) and our newly developed mixing-cell method to estimate the required MMPs, although any reliable MMP analytical or experimental method can be used. We demonstrate how to calculate MMPs for several multicomponent oils displaced by CO2 contaminated by mixtures of N2, CH 4, C2, C3, and H2S. The results show that the predicted MMPs for a west Texas crude displaced by contaminated-CO2 injection streams are nearly linear over the range from pure-CO2 injection to any mole fraction combination of the five contaminants. The accuracy of the predicted MMPs is within ±15 psia of that from calculations using mixing-cell simulations, slimtube simulations, and slimtube experiments where available. For another example oil displacement by impure CO2, however, the linear trend in MMPs with contamination mole fractions is accurate only for total contamination levels less than approximately 20% mole fraction, but this is still within a useful range for CO2-gasflood design and optimization. We also examine the sensitivity of local displacement efficiency to dispersion for binary gas mixtures using 1D simulation.
All Science Journal Classification (ASJC) codes
- Fuel Technology
- Energy Engineering and Power Technology