Around 40% of the current world conventional oil production comes from carbonate reservoirs, dominantly mature and declining giant oilfields. Tertiary oil production methods as part of an Enhanced Oil Recovery (EOR) scheme are inevitable after primary and secondary oil production. The goal of surfactant flooding is to reduce the mobility ratio by lowering the interfacial tension between oil and water and mobilizing the residual oil. This paper highlights adsorption kinetics and equilibrium of Glycyrrhiza Glabra, a novel surfactant, in aqueous solutions for EOR and reservoir stimulation purposes. A conductivity technique was used to assess adsorption of the surfactant in the aqueous phase. Batch experimental runs were also performed at various temperatures to understand the effect of adsorbate dose on the sorption efficiency. The adsorption kinetics was experimentally investigated at room temperature (27 °C) by monitoring the uptake of the Glycyrrhiza Glabra as a function of time. The adsorption data were examined using different adsorption equilibrium and kinetic models. The Langmuir isotherm suits the equilibrium data very well. A pseudo-second order kinetic model can satisfactorily estimate the kinetics of the surfactant adsorption on carbonates. Results obtained from this research can help in selecting appropriate surfactants for design of EOR schemes and reservoir stimulation plans for carbonate reservoirs.

Over 40% of the current world conventional oil production comes from carbonate reservoirs, dominantly mature and declining giant oilfields. After primary and secondary oil production stages using tertiary oil production methods as part of an enhanced oil recovery (EOR) scheme is inevitable. Surfactant flooding aims at reducing the mobility ratio through lowering the interfacial tension between oil and water and mobilizing the residual oil. This article highlights the adsorption equilibrium of the combination of different types of nanosilica and Zyziphus Spina Christi, a novel surfactant, in aqueous solutions for EOR and reservoir stimulation purposes. A conductivity technique was used to assess the adsorption of the surfactant and nanosilica in the aqueous phase. Batch experiments were used to understand the effect of adsorbent dose on sorption efficiency as well. The adsorption data were examined using four different adsorption isotherm models (Langmuir, Freundlich, Temkin, and Linear), and the adsorption parameters were determined for each model. This study suggests that a Freundlich isotherm model can satisfactorily estimate the adsorption behavior of combination nanosilica and surfactant adsorption on carbonates. Results from this study can help in appropriate selection of surfactants in the design of EOR schemes and reservoir stimulation plans in carbonate reservoirs.

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Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed‐forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network‐based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method‐based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability.

Condensate-to-gas ratio (CGR) plays an important role in sales potential assessment of both gas and liquid, design of required surface processing facilities, reservoir characterization, and modeling of gas condensate reservoirs. Field work and laboratory determination of CGR is both time consuming and resource intensive. Developing a rapid and inexpensive technique to accurately estimate CGR is of great interest. An intelligent model is proposed in this paper based on a feed-forward artificial neural network (ANN) optimized by particle swarm optimization (PSO) technique. The PSO-ANN model was evaluated using experimental data and some PVT data available in the literature. The model predictions were compared with field data, experimental data, and the CGR obtained from an empirical correlation. A good agreement was observed between the predicted CGR values and the experimental and field data. Results of this study indicate that mixture molecular weight among input parameters selected for PSO-ANN has the greatest impact on CGR value, and the PSO-ANN is superior over conventional neural networks and empirical correlations. The developed model has the ability to predict the CGR with high precision in a wide range of thermodynamic conditions. The proposed model can serve as a reliable tool for quick and inexpensive but effective assessment of CGR in the absence of adequate experimental or field data.