As a tight gas reservoir with low permeability and production well head pressure, DS gas field faces the dilemma of cost reduction and efficiency due to untimely identify and analysis of liquid loading issue of gas wells. It is mainly because the data processing is completed manually by on-site engineers which is time-consuming and low efficient. The basic threshold-based workflow used by engineers, which only utilize few measuring parameters such as pressure and gas flow rate, has proven to be less accurate with field practices. At the same time, supervisory control, and data acquisition (SCADA) system in DS field which provides high frequency production data is not effectively utilized whereas it is critical for real-time liquid loading diagnosis and prediction. In this study, Sinopec collaborated closely with SLB to harness the power of artificial intelligence (AI) and domain expertise to implement an intelligent gas well lifecycle management solutions for this problem. The Long Short-Term Neural Network (LSTM) algorithm was used to train liquid loading diagnosis model with extracted features from high frequency historical production data and target liquid loading issue using automatic labeling. The self-trained model was then exposed to high frequency real-time production data which is acquired from SCADA system to conduct a timely liquid loading diagnose and prediction. The solutions conclude: exception-based surveillance to customize indicators and dashboards automate production performance monitoring to significantly reduce analysis time for wells from days to minutes, AI-assisted production forecast to auto-batch production history matching and prediction, intelligent liquid loading prediction (LLP) to perform timely prediction of gas wells liquid loading issues, and AI-driven liquid loading diagnose (LLD) to provide real-time quantitative indicators for liquid loading warning. Furthermore, the innovative solutions combine a hybrid AI and physical critical liquid loading gas rate model to perform analysis and intervention recommendations. Having integrates automatic data processing workflow in the backend and user-friendly interface, the system greatly reduced daily processing time in practices.