Prediction of Minimum Miscibility Pressure for CO2 Flooding Based on Microscopic Pore-Throat Structure

Abstract

CO2 flooding can effectively enhance the recovery of low-permeability reservoirs and realize CO2 geological storage. During the displacement process, the minimum miscible pressure (MMP) of CO2 and oil is an important parameter that affects the displacement effect and storage efficiency. However, the microscopic pore-throat structure of low-permeability reservoirs has significant influences on the fluids and phase behaviors. This paper presented a method to determine the miscible state of CO2 flooding based on the microscopic pore-throat structure. Firstly, a physic model was established to quantitatively characterize the microscopic pore-throat structure. Secondly, taking into consideration the P-R equation of state, the gas-liquid equilibrium in the narrow pore-throat was calculated. On this basis, a MMP prediction model was established correspongdingly by considering the multi-stage contact and mass transfer of CO2-oil. Finally, the results obtained by the proposed model were compared with the experimental results of CO2 flooding, and then the model was applied to the actual reservoir to predict plane distribution of MMP. The curves of MMP distribution and pressure drawdown between wells were combined to determine the position of miscible front and non-miscible area at different production stages. The results have shown that the MMP of core sample calculated by the model was 20.3 MPa, which was comparable to that of CO2 flooding experiment, e.g., 20 MPa, and thus indicatesd a high accuracy of the model. The MMP in the well control area of the Y29-101 well group was 19.8 MPa. During the unsteady flow stage, the miscible-phase front was 430 m from the injection well, while it was 310 m from the injection well during the stable flow stage. This method can accurately determine the specific phase distribution of CO2-oil in the formation, which is of great significance to promote the development of CO2 flooding and storage technology, improve the recovery of low permeability reservoirs, ensure energy supply and reduce carbon emission.

Dynamic Prediction Model for a Lifecycle Process of Multi-Layer Oil Reservoir in porous media

“…In this part, HPMI was utilized to analyze the micron pores in P13-1 and P13-2. Because HPMI gathers data on the pores of the throat, as well as mercury intake and pressure, it is able to precisely portray the distribution of the throat [16]. According to Fig.…”