(225d) Experimental and Modelling Study of the Phase Behaviour of (CO2 + CH4 + Methylbenzene) at High-Pressure and High-Temperature Conditions
Knowledge and understanding of the phase behavior of CO2-hydrocarbon mixtures at reservoir conditions is essential for the design, construction and operation of carbon capture and storage (CCS) and enhanced oil recovery (EOR) processes. For an optimal design and operation of these processes, numerical simulations are developed which play a key role in predicting the flow of CO2 from the injection wells into the storage formation and determining the long-term evolution of the CO2 plume after the termination of injection as well as predicting the required operating conditions for the maximum recovery of heavy oil. These models, however, require detailed and accurate thermophysical-property data for CO2 and its mixtures with the reservoir fluids it encounters, in particular their phase behaviour. Providing such experimental data is expensive and time consuming. Therefore there is a great deal of interest in developing improved models and computational packages to predict the phase behavior and thermophysical properties of such mixtures with the least inputs of experimental data. However, to validate and improve those models, experimental data, in particular data for ternary and multi-component CO2-hydrocarbon mixtures, are still required to tune the interaction parameters of such models, to help in developing predictive approaches, and to assess the predictive capabilities of all models.
2. Experimental Work
Various techniques have been employed to determine the phase behaviour of multicomponent mixtures under high-pressure and high-temperature conditions. In this work, a new experimental apparatus  was used to measure the phase equilibria of systems containing CO2and hydrocarbons at reservoir temperatures and pressures. The apparatus involved a high-pressure high-temperature variable-volume view cell, with wetted parts fabricated from Hastelloy C-276, driven by a computer-controlled servo motor system, and equipped with a sapphire window for visual observation. Two calibrated syringe pumps with a maximum service pressure of 70 MPa were used for quantitative fluid injection. A low-dead-volume pressure transducer was used for the pressure measurements while the cell temperature was controlled by a heating jacket, and the temperature was measured by means of a Pt100 sensor. The phase behavior of the mixture in the cell was observed with the aid of a CCD camera operating with both front and rear illumination. The maximum operating pressure and temperature of the entire setup were 40 MPa and 473.15 K, respectively.
The apparatus was previously validated by comparison with published isothermal vapor-liquid equilibrium data for the binary system (CO2 + heptane). Measurements were made on the ternary mixture (CO2 + CH4 + methylbenzene) over the temperature range (323 to 423) K at pressures up to 35 MPa. In this case, the molar ratio of CO2to methane in the ternary system was fixed at three different values (0.25, 0.5 and 0.75) and the measurements were made on three isotherms (323, 373 and 423) K for each fixed value.
3. Modeling Work
In this work, we explore the predictive capability of SAFT-g-Mie  to model the phase equilibria of this mixture. The Statistical Associating Fluid Theory, stemming from the first order perturbation theory of Wertheim , was implemented in this work with a group contribution approach and the generalized Mie potential to represent segment-segment interactions. In the resulting SAFT-g-Mie, complex molecules are represented by fused segments representing the functional groups from which the molecule may be assembled. All interactions, both like and unlike, as implemented in this work were determined from experimental data of systems comprising the constituent groups. We also explore the predictive capability of, PPR78 , which is based on the Peng-Robinson 78 EoS . In the PPR78 approach, the binary parameters are obtained from a group-contribution scheme.
The current work provides new experimental data, detailed assessment of predictive models as well as tuning the model parameters through regress analysis and fitting procedures.
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We gratefully acknowledge the funding of QCCSRC provided jointly by Qatar Petroleum, Shell, and the Qatar Science and Technology Park, and their permission to publish this research.