No abstract

Solvent-based processes have shown technical advantages over thermal techniques for recovery of heavy oil and bitumen. The success of these processes relies on accurate computation of molecular diffusion coefficient which determines how fast a solvent penetrates into oil. Concentration profile measurements of solvent in oil are used for the determination of the molecular diffusion coefficient. Although numerous experimental techniques have been proposed, the accurate estimation of this parameter is still a topic of debate in the literature. In this work, 1-D nuclear magnetic resonance imaging (MRI) is employed to obtain diffusivity data for a toluene–heavy oil system. Diffusion of toluene in heavy oil was monitored for 20 days at a controlled temperature of 35 °C and ambient pressure. Over time, toluene diffusion into oil leads to changes in spatial distribution of T 1 and T 2 that affect the received signal. This serves as the basis of the solvent and heavy oil concentration estimation. Consequently, concentration profiles were established by converting the MRI signals to concentration values. This conversion was achieved by creating samples with known concentrations of heavy oil–toluene and measuring their response in the same environment and parameter settings. A concentration-dependent diffusion coefficient was obtained from concentration profiles. The results show that relaxation based 1D MRI is an accurate and robust tool to obtain diffusivity data in complex fluids such as heavy oil.

Estimation of the viscosity of naturally occurring petroleum gases is essential to provide more accurate analysis of gas reservoir engineering problems. In this study, a new soft computing approach, namely, least square support vector machine (LSSVM) modeling, optimized with a coupled simulated annealing technique was applied for estimation of the natural gas viscosities at different temperature and pressure conditions. This model was developed based on 2485 viscosity data sets of 22 gas mixtures. The model predictions showed an average absolute relative error of 0.26% and a correlation coefficient of 0.99. The results of the proposed model were also compared with the well-known predictive models/correlations available in the literature. It has been observed that the proposed model correctly captures the physical trend of changing the natural gas viscosity as a function of the temperature and pressure. Finally, sensitivity analysis was performed to assess the effect of the gas viscosity uncertainty on the cumulative gas production for a synthetic natural gas reservoir, using a numerical reservoir simulation. Results revealed that applications of LSSVM modeling can lead to a more accurate and reliable estimation of the gas viscosity over a wide range of reservoir conditions.

The values of the diffusion coefficient, swelling, and solubility are frequently required for designing processes which involve contact of various gases with liquids. Solubility determines the capacity of a liquid phase to dissolve gas, diffusivity controls the rate and extent of mass transfer, and swelling is the amount of volume change due to gas dissolution. Accurate estimation of these parameters by reliable and practical techniques is of continuing interest. In this study, 1D magnetic resonance imaging (MRI) is coupled with a numerical model to evaluate the diffusivity of gaseous solvents in heavy oil with a moving boundary condition. Diffusion tests are conducted for various solvents (dimethyl ether, propane, ethane, and carbon dioxide) under constant pressure and temperature inside a closed pressure volume temperature (PVT) cell. Spatial and temporal signal amplitudes are acquired during the diffusion tests which are further analyzed to obtain the concentration profiles and calculate the concentration-dependent diffusion coefficients using the measured correlation between the signal amplitude and solvent content. Oil swelling is found by tracking the gas−liquid interface and solubility is calculated by PVT analysis. Finally, the diffusivity results are compared with the pressure decay method considering both equilibrium and quasi-equilibrium boundary conditions.

Steam injection is widely used for heavy oil and bitumen recovery. The advantage of this process is its high recovery factor and its high oil production rate. However, the high production rate is associated with excessive energy consumption, carbon dioxide generation, and expensive post-production water treatment. Some of these disadvantages are overcome or reduced by the addition of solvent mixtures to steam. The steam-solvent processes are complex oil displacement methods involving simultaneous heat, mass, and fluid transport. These processes are not clearly understood despite their apparent importance to the oil industry. Systematic studies are essential in the design, analysis, and evaluation of the steam-solvent processes as well as in mathematical simulation. These studies provide valuable insights for petroleum engineers to improve the oil recovery efficiency when applied in a reservoir. Results of these processes are scattered in many publications over more than 40 years and are not readily available for most petroleum engineers. The purpose of the paper is to present a review of current knowledge and available data, and to delineate the steam-solvent processes.