Effect of silicon-based nanoparticles on enhanced oil recovery: Review

A review on application of nanoparticles for EOR purposes: history and current challenges

Applications of nanotechnology in several fields of petroleum industry, e.g., refinery, drilling and enhanced oil recovery (EOR), have attracted a lot of attention, recently. This research investigates the applications of nanoparticles in EOR process. The potential of various nanoparticles, in hybrid and bare forms for altering the state of wettability, reducing the interfacial tension (IFT), changing the viscosity and activation of other EOR mechanisms are studied based on recent findings. Focusing on EOR, hybrid applications of nanoparticles with surfactants, polymers, low-salinity phases and foams are discussed and their synergistic effects are evaluated. Also, activated EOR mechanisms are defined and specified. Since the stabilization of nanofluids in harsh conditions of reservoir is vital for EOR applications, different methods for stabilizing nanofluids through EOR procedures are reviewed. Besides, a discussion on different functional groups of NPs is represented. Later, an economic model for evaluation of EOR process is examined and "Hotelling" method as an appropriate model for investigation of economic aspects of EOR process is introduced in detail. The findings of this study can lead to better understanding of fundamental basis about efficiency of nanoparticles in EOR process, activated EOR mechanisms during application of nanoparticles, selection of appropriate nanoparticles, the methods of stabilizing and economic evaluation for EOR process with respect to costs and outcomes.

An overview on the enhanced gas condensate recovery with novel and green methods

A consideration of the negative environmental aspects of fossil fuels has made natural gas the best choice to meet the human demand for energy. The condensate gas reservoir is one source of gases that tolerates skin problems (liquid blockage). Conventional methods for inhibiting or removing liquid blockages are momentary treatments, non-eco-friendly, and expensive. Therefore, new methods have been introduced, such as wettability alteration toward liquid repellency, renewable energies, thermochemical reactions and waves for heating reservoirs, and CO₂ injection. This paper reviews the methods for altering the wettability of porous media by fluorochemicals, fluorinated nanoparticles (NPs), and free fluorocarbon materials from natural substances. NPs, particularly silicon-based types, as a green, clean, and emerging technology that are more compatible with the environment, were investigated for their ability to alter the wettability and upgrade conventional materials, such as polymers and surfactants. The feasibility of using renewable energies, thermochemical reactions, and waves for heating the gas condensate reservoir to overcome the skin problem and return the reservoir to the reasonable and economical gas production is reviewed. Finally, CO₂ injection is introduced as a multi-purpose green method to enhance gas condensate recovery and allow CO₂ sequestration.

Artificial Neural Network to Forecast Enhanced Oil Recovery Using Hydrolyzed Polyacrylamide in Sandstone and Carbonate Reservoirs

Polymer flooding is an important enhanced oil recovery (EOR) method with high performance which is acceptable and applicable on a field scale but should first be evaluated through lab-scale experiments or simulation tools. Artificial intelligence techniques are strong simulation tools which can be used to evaluate the performance of polymer flooding operation. In this study, the main parameters of polymer flooding were selected as input parameters of models and collected from the literature, including: polymer concentration, salt concentration, rock type, initial oil saturation, porosity, permeability, pore volume flooding, temperature, API gravity, molecular weight of the polymer, and salinity. After that, multilayer perceptron (MLP), radial basis function, and fuzzy neural networks such as the adaptive neuro-fuzzy inference system were adopted to estimate the output EOR performance. The MLP neural network had a very high ability for prediction, with statistical parameters of R² = 0.9990 and RMSE = 0.0002. Therefore, the proposed model can significantly help engineers to select the proper EOR methods and API gravity, salinity, permeability, porosity, and salt concentration have the greatest impact on the polymer flooding performance.