Carbon dioxide flooding is a strategic replacement technology for greatly enhancing oil recovery in low-permeability oilfields, which includes social benefits resulting from carbon emission reduction and economic benefits owing to the improvement of oil recovery. Therefore, it is of great significance to develop and apply the technology of CO₂ flooding and storage in the petroleum industry. In reservoir conditions, CO₂ is usually under a supercritical state, presenting both low viscosity and high diffusivity of a gaseous state and high density of a liquid state. The special phase behavior of CO₂ directly affects its extraction capacity, resulting in the change of miscible behavior between CO₂ and crude oil. In this paper, the ultra-high-pressure–high-temperature pressure–volume–temperature (PVT) system was used to evaluate the phase characteristics of CO₂ during the process of reservoir development. The phase behaviors of the CO₂/CH₄/N₂ crude oil system were compared and analyzed. Moreover, the matching mechanism between supercritical CO₂ characteristics and oil–gas system miscibility was investigated and defined. This work deepened the understanding of the phase characteristics of CO₂ in the process of miscible flooding, providing both theoretical guidance for the application of CO₂ injection on oilfields and the essential scientific basis for the implementation of CCUS-EOR technology.

The reservoir proportion with deep and low permeability, where oil and gas exist widely in nanopores, has been discovered increasingly in China. Affected by the nano-confinement effect, the phase behavior of fluid in nanopores varies with nanopore size rather than being constant. In this paper, the density, viscosity, and saturation pressure of pure and mixed fluids in nanopores are studied by molecular dynamics (MD) simulation combined with statistical physics. The feasibility of using the MD method to investigate fluid behavior in nanopores was verified with laboratory data. It was found that the fluids’ phase behavior parameters in nanopores are lower than those in the bulk phase due to the confinement effect. The boundary of confinement effect (BCE) is defined as a size range of nanopores that separates the pore scale into the confinement scale and bulk scale. Each fluid has a specific BCE influenced by the molecular size of fluid. The phase behavior of crude oil in shale and tight oil reservoirs is most affected by the molecular adsorption and interaction energy between the fluid molecule and pore wall. Clarifying a specific BCE in shale reservoirs can significantly enhance the understanding of reservoirs and guide reservoir development strategies.

Songliao Basin hosts abundant shale oil and gas resources, yet the phase behavior of the fluids is still unclear since the confinement effect of the shale reservoir alters the adsorption effect on the pore walls. In this study, molecular dynamics (MD) simulations were employed to clarify this issue. A binary mixture was built to represent Gulong shale oil and the phase state properties under the confinement effect were evaluated. In addition, a series of pure alkane models were constructed to analyze the influence of hydrocarbon type, temperature, and mineral composition on phase behavior. The simulation results showed that the typical Gulong shale fluid remains liquid state in the nanopores. Owing to the confinement effect, both viscosity and density under nano space decrease sharply compared to those at bulk space. Therefore, field development should somewhat rely on high-pressure stimulation to create flow paths, followed by CO₂ huff-and-puff technology to maintain production pressure. In addition, fluid composition, temperature, and mineral type are the primary factors governing the magnitude of the confinement effect.

The development of oil and gas resources in global petroliferous basins has extended from shallow to deep reservoirs. Dew point pressure (Pd) is a vital parameter for fluid characterization and field development. Accurately and quickly obtaining Pd is crucial for the development of ultra-deep condensate gas reservoirs. The objective of this work is to predict the Pd of condensate gas by an artificial neural network (ANN) model. Ten ultra-deep condensate gas samples were analyzed using an experimental method and the Pd at reservoir temperature was obtained. A total of 113 datasets including 103 collected datasets and 10 measured datasets were adopted for ANN model training and testing. The results show that the average absolute percent relative error (AAPRE) of the developed ANN model between the measured and predicted values on the test set was 4.9589%. The predicted accuracy between the ANN model and widely used equations of state was compared. The results of statistical and graphical analysis show that the ANN model achieves the minimum prediction error. This ANN model can provide the necessary guidance for predicting the Pd for the development of different kinds of reservoirs.