Carbon dioxide miscible flooding has become a popular method for Enhanced Oil Recovery (EOR) because it not only efficiently enhances oil recovery but also considerably reduces green house gas emissions. However, it can significantly cause asphaltene deposition, which leads to serious production problems such as wettability alteration, plugging of the reservoir formation, blocking the transportation pipelines, etc. It is crucial to investigate the effects of different factors on asphaltene deposition. A novel experimental setup was prepared to employ a high-pressure visual cell for investigation of asphaltene deposition on a model rock under typical reservoir conditions. The evolution of asphaltene deposition was monitored via a highresolution microscope. Image processing software was utilized to check the amount of deposited asphaltene and its size distribution under different conditions. Crudes from two Iranian oil fields were used in the experiments. The amount of asphaltene deposition was measured during pressure depletion under two operating conditions: with/without CO 2 -injection. It was observed that the amount of deposited asphaltene decreases with pressure depletion. For instance, asphaltene deposition at 140 bar and 90 °C is 5.7 times greater compared to 30 bar and 90 °C condition. The results of CO 2 gas injection confirm that the deposited asphaltene increases with the concentration of injected CO 2 . According to the results, a temperature increase from 35 to 90 °C contributes to growth and aggregation of asphaltene particles. A comparison of two different asphaltene sources in terms of aggregation and flocculation behavior revealed that the asphaltene molecular structure could have a noticeable influence on asphaltene deposition. A new parameter was defined as the potential of deposition to describe, quantify, and compare the tendency of different asphaltene samples for flocculation and deposition.

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The enlargement of asphaltene particles precipitated in a heptane−toluene mixture (Heptol) was mechanistically modeled using mass and population balance equations. The kinetic parameters used in the model equations were optimized through fitting of the model predictions to the experimental data. The significance of this study is the investigation of growth mechanism of asphaltene particles based on supersaturation not reported in the literature. Agglomeration and growth mechanisms of asphaltene precipitation were quantitatively related to the supersaturation as a driving force in the form of rate equations. Using a fractal dimension for asphaltene particles found in the literature resulted in a better consistency between the model predictions and experimental data. Size analysis was carried out using a Horiba LB-550 nanoparticle size analyzer, which uses a dynamic laser scattering technique to measure the size of the asphaltene particles in the range of 1 nm to 6 μm. The concentration of asphaltene in the liquid mixture during the development of flocs was measured using the spectrophotometry technique. Particle enlargement from about 8 nm to 2 μm lasted about 2 h in a mixture of asphaltene−toluene containing 115.3 mg of asphaltene/kg of toluene when 0.79 kg of n-heptane/kg of toluene was added as an antisolvent.

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Asphaltene precipitation affects enhanced oil recovery processes through the mechanism of wettability alteration and blockage. Asphaltene precipitation is very sensitive to the reservoir conditions and fluid properties, such as pressure, temperature, dilution ratio, and injected fluid molecular weight. A Bayesian belief network (BBN) was used in this study as an artificial intelligence modeling tool to investigate the effect of different variables/parameters on asphaltene precipitation. The predicted results from the BBN model were compared to the experimental precipitation data obtained using high-resolution images captured in a high-pressure cell and processed by image analysis software. The cell accessories facilitate in situ visual monitoring of nuclei growth of asphaltene at high pressures and specified temperatures. The average relative absolute deviation between the model predictions and the experimental data was found to be less than 4.6%. Burst of nucleation or the onset of asphaltene precipitation was also determined at different conditions directly by the developed BBN model. A comparison between the prediction of this model and the alternatives showed that the BBN model predicts asphaltene precipitation more accurately and covers a wider range of affected variables/parameters.