To predict the natural gas hydrate formation conditions quickly and accurately, a novel hybrid genetic algorithm–support vector machine (GA-SVM) model was developed. The input variables of the model are the relative molecular weight of the natural gas (M) and the hydrate formation pressure (P). The output variable is the hydrate formation temperature (T). Among 10 gas samples, 457 of 688 data points were used for training to identify the optimal support vector machine (SVM) model structure. The remaining 231 data points were used to evaluate the generalisation capability of the best trained SVM model. Comparisons with nine other models and analysis of the outlier detection revealed that the GA-SVM model had the smallest average absolute relative deviation (0.04%). Additionally, the proposed GA-SVM model had the smallest amount of outlier data and the best stability in predicting the gas hydrate formation conditions in the gas relative molecular weight range of 15.64–28.97 g/mol and the natural gas pressure range of 367.65–33,948.90 kPa. The present study provides a new approach for accurately predicting the gas hydrate formation conditions.

The viscosity of polymer solutions often decreases significantly due to shear degradation in near-wellbore zones and chemical/aging degradation during long-term transport, which seriously compromises the efficiency of polymer flooding. In this study, a hydrophobic monomer, hexadecyl dimethyl-5-enhexyl ammonium bromide (HAB), was synthesized. Subsequently, a novel viscosity stabilizer (LC12) was prepared via micellar polymerization of acrylamide and HAB using sodium dodecyl sulfate (SDS) as a solubilizer. The effect of this stabilizer on a hydrophobic associating polymer (AP-P4) was evaluated. Experimental results revealed that the addition of LC12 accelerated the dissolution rate of the polymer concentrate and significantly improved the viscoelasticity of the solution before and after degradation. Mechanistically, LC12 facilitates the formation of robust hydrophobic aggregates, shifting the retention mechanism from adsorption to mechanical trapping. Consequently, the shear viscosity retention of the AP-P4 solution increased by 28.4%, and the incremental oil recovery margin was improved by 4.49 percentage points after aging.