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Surfactants have the potential to reduce the interfacial tension between oil and water and mobilize the residual oil. An important process which makes the surfactant injection to be less effective is loss of surfactant to porous medium during surfactant flooding. This study highlights the results of a laboratory study on dynamic adsorption and desorption of Trigoonella foenum-graceum (TFG) as a new nonionic surfactant. The experiments were carried out at confining pressure of 3000 psi and temperature of 50 °C. Surfactant solutions were continuously injected into the core plug at an injection rate of 0.5 mL/min until the effluent concentration was the same as initial surfactant concentration. The surfactant injection was followed by distilled water injection until the effluent surfactant concentration was reduced to zero. The effluent concentrations of surfactant were measured by conductivity technique. Results showed that the adsorption of surfactant is characterized by a short period of rapid adsorption, followed by a long period of slower adsorption, and also, desorption process is characterized by a short, rapid desorption period followed by a longer, slow desorption period. The experimental adsorption and desorption data were modeled by four well-known models (pseudo-first-order, pseudo-second-order, intraparticle diffusion, and Elovich models). The correlation coefficient of models revealed that the pseudo-second-order model predicted the experimental data with an acceptable accuracy.

Asphaltene precipitation is a major problem in the oil production and transportation of oil. Changes in pressure, temperature, and composition of oil can lead to asphaltene precipitation. In the case of gas injection into oil reservoirs, the injected gas causes a change in oil composition and may lead to asphaltene precipitation. Accurate determination and prediction of the precipitated amount are vital, for this purpose there are several approaches such as experimental method, scaling equation, thermodynamics models, and neural network as the most recent ones. In this paper, we propose a new artificial neural network (ANN) optimized by particle swarm optimization (PSO) to predict the amount of asphaltene precipitation. This is conducted during the process of gas injection into oil reservoirs for enhanced oil recovery purposes. In the developed models, (1) oil composition, (2) temperature, (3) pressure, (4) oil specific gravity, (5) solvent mole percent, (6) solvent molecular weight, and (7) asphaltene content are considered as input parameters to the neural network. The weight of asphaltene and asphaltene content are considered as input parameters to the neural network and the weight of asphaltene precipitation as an output parameter. A comparison between the results of the proposed new model with Gaussian Process algorithm and previous research shows that the predictive model is more accurate.

For gas condensate reservoirs, as the reservoir pressure drops below the dew point pressure (DPP), a large amount of valuable condensate drops out and remains in the reservoir. Thus, prediction of accurate values for DPP is important and leads to successful development of gas condensate reservoirs. There are some experimental methods such as constant composition expansion (CCE) and constant volume depletion (CVD) for DPP measurement but difficulties in experimental measurement especially for lean retrograde gas condensate causes to develop of different empirical correlations and equations of state for DPP calculation. Equations of state and empirical correlations are developed for special and limited data sets and for unseen data sets they are not generalizable. To mitigate this problem, in this paper we developed new artificial neural network optimized by particle swarm optimization (ANN-PSO) for DPP prediction. Reservoir fluid composition, temperature and characteristics of the C7+ considered as input parameters to neural network and DPP as target parameter. Comparing results of the developed model in this research with Gaussian processes regression by particle swarm optimization (GPR-PSO), previous models and correlations shows that the predictive model is accurate and is generalizable to new unseen data sets.