Managing Liquid Loading Using A Physics Inspired Data-Driven Method
Authors: Chauhan Prithvi Singh , Li Zhuoran , Sun Wenyue , Sinha Utkarsh , Surendra Meher , Sabharwal Varad , Sankaran Sathish 
Published: February 2025

Liquid accumulation in gas wells often occurs when gas velocities are too low, causing liquids (condensates and water) to fall back and gather within the wellbore. This can result in frequent well shut-ins and reduced productivity, necessitating regular clean-ups or lift interventions to minimize disruptions. Timely detection, optimization and proactive management of this issue are essential for effective well recovery. Traditional industry methods for detecting liquid loading, which rely on critical rate correlations, suffer from inaccuracies due to several assumptions: (1) consistent shape of liquid droplet and film reversal mechanisms throughout the wellbore, (2) unchanged properties like the drag coefficient during shifts in flow regimes, and (3) uncertainties in fluid parameters and downhole conditions. Our method leverages real-time, high-frequency production data to detect the onset of liquid loading using a data-driven approach and find optimal parameters of a cycling well.

Diagnostic statistical proxy features that signal instability in well flow are derived from high-frequency data on metered gas rates and wellhead pressure. When these features exceed specific thresholds, a signal is generated to indicate potential liquid loading. A simple adjustment scheme then allows for adjustments to accurately determine the corrected critical rate. Recognizing these local patterns also aids in the customized modification and fine-tuning of critical rates that are typically derived from standard empirical correlations, such as those by Turner or Coleman. Moreover, if the well employs continuous artificial lift techniques like gas lift, these are also optimized to improve the efficiency of the artificial lift and enhance overall well performance. The forecasted production rate, computed through nodal analysis and material balance, can be compared against the corrected critical rate curve to predict onset of liquid loading. Finally, for the liquid loaded wells on well cycling operation (intermittent production), a machine learning model is developed to optimize cycling parameters based on features extracted from a sequence of past cycles. The trained models predict the optimum shut-in and production time to maximize the production.

The data-driven approach was applied to numerous wells across multiple onshore gas fields, where algorithmically detected instances of liquid loading correlated well with manually reported field events, leading to successful results. This method enhanced the accuracy of event detection by providing consistent and reliable automation, free from manual subjectivity or interpretation. In one of the tight gas fields, the adjusted critical rates were used to optimize gas lift rates, successfully mitigating liquid loading and minimizing amount of buy-back gas costs. In another conventional gas field, the method was used to detect, predict and optimize time to liquid loading to effectively manage well cycling and increase production of already cycling wells.

This work introduces a fast and methodical approach to detect liquid loading using easily available field data, avoiding traditional assumptions. It also determines critical gas rates directly from observed field data and provides a mechanism to recommend and confirm the best artificial lift settings to address liquid loading problems.