Reliable estimation of liquid accumulation in gas-well tubing is important for characterizing liquid-loading conditions and supporting engineering assessment. Traditional mechanistic approaches commonly depend on an extensive set of wellbore descriptors and empirical parameters, while also requiring complicated solution procedures. This work addresses these constraints through a data-driven predictive framework that couples ensemble feature selection with ant-colony-optimized support vector regression (ACO-SVR). A majority-voting scheme was applied to 107 production-test records collected from a gas field. The scheme combined linear regression, grey relational analysis, random-forest mean decrease in impurity, the Pearson correlation coefficient, and SHAP attribution, and selected seven dominant factors from 11 candidate variables: casing pressure, tubing pressure, tubing depth, reservoir mid-depth, daily gas production, daily water production, and wellhead temperature. Ant colony optimization subsequently determined the SVR hyperparameters. Evaluation with 32 held-out well samples produced a root-mean-square error of 165.73 m, a mean absolute error of 103.26 m, a coefficient of determination of 0.94, and a mean relative error of 2.11%. Repeated five-fold cross-validation further yielded an average $R^2$ of $0.91\pm 0.04$ and an RMSE of $181.6\pm 24.8$ m, indicating moderate variability across alternative data partitions. Relative to the untuned SVR, ACO-SVR lowered the root-mean-square error and mean absolute error by approximately 27.0% and 31.1%, respectively. Its mean relative error was also 3.66 percentage points below that of the PLATA model. The resulting framework provides accurate prediction of tubing liquid accumulation height from a small sample and offers quantitative information for liquid-loading assessment under the investigated operating conditions.

Low-pressure gas wells treated by foam-assisted deliquification often suffer from poor temporal coordination between foamer and defoamer injection, excessive defoamer consumption under continuous dosing, and foam carryover into surface facilities. This study proposes a coordinated intermittent strategy based on the complete foam-transport timeline. A visual vertical-pipe facility quantified the effects of chemical concentration on foam generation, liquid holdup, frictional pressure loss, and foam collapse. A gravity-driven annular falling-film model predicted the time required for the foamer to reach the bottomhole. The wellbore was then discretized, and local liquid holdup was coupled with actual liquid velocity to predict foam return from the bottomhole to the wellhead. Together, the models determine the defoamer start time, injection duration, and dose. Increasing foamer concentration reduced liquid holdup but increased flow resistance above an effective range; excess defoamer likewise produced little additional collapse. In a tight-gas well, the optimized schedule maintained liquid unloading and stable surface operation while reducing daily defoamer consumption from 36 to 6 L and chemical cost by more than 80%. The method provides a quantitative basis for digitally controlled chemical dosing in foam-assisted gas-well deliquification.