Artificial Intelligence Enhanced Prediction of Water Loading in Gas Wells: A Data-Driven Alternative to Turner's Model

This study aims to develop an AI-enhanced, optimized empirical model for predicting critical gas rates to prevent water loading. By integrating real–field data and advanced machine learning techniques, a new Turner–style equation will be formulated. The model will dynamically adapt to varying well conditions, outperform traditional methods, and enable proactive, real–time management of deliquification strategies in complex environments. High- resolution well datasets, including bottomhole pressure, wellhead pressure, gas properties, and production rates, were extracted of a well X were taken and rigorously preprocessed. Variance analysis and permutation entropy were applied to detect early–stage flow regime transitions associated with liquid loading onset. Polynomial feature transformations captured nonlinear and cross–variable interactions among key operational parameters. A supervised learning framework with polynomial regression achieved 92% accuracy in predicting critical gas rates, representing a 28% improvement over conventional Turner–based methods. The final Turner–style AI–enhanced model dynamically adapts to changing multiphase flow regimes , enabling proactive, real–time optimization of deliquification strategies in Production systems.The polynomial regression approach , incorporating second–order and interaction terms between flowing bottomhole pressure (Pwf) and gas density (ρg),enabled the model to accurately capture complex nonlinear behaviors in multiphase offshore environments. Minimal deviation between predicted and actual critical gas rates was observed, confirming the model's robustness across varying well conditions. The flexible structure of the AI–based model allows seamless integration into digital twin platforms and real–time surveillance systems, enabling dynamic monitoring and proactive management of water loading risks. By adapting to fluctuating operational parameters, the model supports optimization of deliquification strategies, artificial lift planning, and well intervention scheduling. Overall, the AI–driven predictive framework presents a major advancement over conventional methods, offering a reliable, data– driven tool for enhancing gas production stability and extending well life in challenging production environments. Unlike traditional models that primarily focus on droplet or film reversal theories, this study introduces a hybrid AI– based approach that dynamically learns from multiphase behaviors. By integrating entropy-based instability detection and real–time data patterns, the developed model not only predicts the critical rate but also evolves adaptively to changing well conditions, ensuring sustained production optimization.