Research progress on supercritical CO2 thickeners

A comprehensive review direct methods to overcome the limitations of gas injection during the EOR process

Among the Enhanced Oil Recovery (EOR) methods, gas-based EOR methods are very popular all over the world. The gas injection has a high ability to increase microscopic sweep efficiency and can increase production efficiency well. However, it should be noted that in addition to all the advantages of these methods, they have disadvantages such as damage due to asphaltene deposition, unfavorable mobility ratio, and reduced efficiency of macroscopic displacement. In this paper, the gas injection process and its challenges were investigated. Then the overcoming methods of these challenges were investigated. To inhibit asphaltene deposition during gas injection, the use of nanoparticles was proposed, which were examined in two categories: liquid-soluble and gas-soluble, and the limitations of each were examined. Various methods were used to overcome the problem of unfavorable mobility ratio and their advantages and disadvantages were discussed. Gas-phase modification has the potential to reduce the challenges and limitations of direct gas injection and significantly increase recovery efficiency. In the first part, the introduction of gas injection and the enhanced oil recovery mechanisms during gas injection were mentioned. In the next part, the challenges of gas injection, which included unfavorable mobility ratio and asphaltene deposition, were investigated. In the third step, gas-phase mobility control methods investigate, emphasizing thickeners, thickening mechanisms, and field applications of mobility control methods. In the last part, to investigate the effect of nanoparticles on asphaltene deposition and reducing the minimum miscible pressure in two main subsets: 1- use of nanoparticles indirectly to prevent asphaltene deposition and reduce surface tension and 2- use of nanoparticles as a direct asphaltene inhibitor and Reduce MMP of the gas phase in crude oil was investigated.

Research of CO2-Soluble Surfactants for Enhanced Oil Recovery: Review and Outlook

CO2 foam injection has been shown to be effective under reservoir conditions for enhanced oil recovery. However, its application requires a certain stability and surfactant absorbability on rock surface, and it is also associated with borehole corrosion in the presence of water. Adding surfactants to CO2 can enhance the interaction between CO2 and crude oil and control the CO2 mobility, thereby improving the performance of CO2 flooding. This paper presents a review of the research of CO2-soluble surfactants and their applications. Molecular dynamics simulation is introduced as a tool for analyzing the behavior of the surfactants in supercritical CO2 (scCO2). The applications of CO2-soluble surfactants, including CO2 thickening, reducing miscibility pressure, and generating supercritical CO2 foam, are discussed in detail. Moreover, some opportunities for the research and development of CO2-soluble surfactants are proposed.

Evaluation of the Driving Effect of the CO2 Viscosity Enhancer Composite System in Extra-Low Permeability Sandstone Reservoirs

CO₂ flooding is an important technology to enhance oil recovery and realize effective storage of CO₂ in ultra-low permeability reservoir. However, due to poor reservoir properties, strong interlayer heterogeneity, and unfavorable mobility ratio of CO₂, gas channeling easily occurs, resulting in low recovery. Chemically assisted CO₂ thickening technology has been developed to control the gas flow rate and improve the CO₂ repulsion effect. Through solubility and viscosity enhancement tests, the CO₂ viscosity enhancer composite system is preferably constructed and then combined with the core drive experiments, the effect of CO₂ viscosity enhancer composite system on oil drive in homogeneous and non-homogeneous cores is evaluated, the correlation between the drive efficiency and viscosity enhancement and solubility of the system is analyzed, and the mechanism of recovery enhancement is explored. The results show that the preferably constructed CO₂-ASA-LAP composite system has a good effect for improving the gas drive effect under simulated formation conditions, and its improvement effect is positively related to the solubility and viscosity increase of the system. Combining oil repelling efficiency and economic considerations, ASA:LAP = 1:1 is selected as the optimal CO₂ viscosity enhancer composite oil repelling system. For homogeneous cores, the CO₂-ASA-LAP combined system drive can increase the recovery rate by 6.65% as compared with CO₂ flooding. For heterogeneous cores, when the permeability difference is 5, the comprehensive recovery factor of the CO₂-ASA-LAP system flooding is 8.14% higher than that of CO₂ flooding. When the permeability difference increases from 5 to 10, the comprehensive recovery factor of the CO₂-ASA-LAP system flooding increases by 1.85%.The injection of the CO₂-ASA-LAP system has some injurious effect on the permeability of the reservoir core, and the smaller the permeability, the greater the degree of injury. The mechanism of the CO₂-ASA-LAP system to improve recovery includes increasing CO₂ viscosity, improving the oil repelling flow ratio, blocking high seepage channels, initiating low seepage residual oil, enhancing CO₂ dissolution, and expanding the oil repelling effect.

Anti-Cancer Drug Solubility Development within a Green Solvent: Design of Novel and Robust Mathematical Models Based on Artificial Intelligence

Nowadays, supercritical CO₂(SC-CO₂) is known as a promising alternative for challengeable organic solvents in the pharmaceutical industry. The mathematical prediction and validation of drug solubility through SC-CO₂ system using novel artificial intelligence (AI) approach has been considered as an interesting method. This work aims to evaluate the solubility of tamoxifen as a chemotherapeutic drug inside the SC-CO₂ via the machine learning (ML) technique. This research employs and boosts three distinct models utilizing Adaboost methods. These models include K-nearest Neighbor (KNN), Theil-Sen Regression (TSR), and Gaussian Process (GPR). Two inputs, pressure and temperature, are considered to analyze the available data. Furthermore, the output is Y, which is solubility. As a result, ADA-KNN, ADA-GPR, and ADA-TSR show an R² of 0.996, 0.967, 0.883, respectively, based on the analysis results. Additionally, with MAE metric, they had error rates of 1.98 × 10⁻⁶, 1.33 × 10⁻⁶, and 2.33 × 10⁻⁶, respectively. A model called ADA-KNN was selected as the best model and employed to obtain the optimum values, which can be represented as a vector: (X1 = 329, X2 = 318.0, Y = 6.004 × 10⁻⁵) according to the mentioned metrics and other visual analysis.