Automated Intelligent Alarms to Monitoring and Identify Liquid Loading Challenge for Giant Depletion Gas Condensate Fields

Depleted well monitoring is a crucial task to ensure continuous production without facing substantial issues that withhold the production, such as liquid loading. Utilizing an integrated digital production system and custom intelligence alarms functionality can help identify and analyze this bottleneck using physics-based model estimations that can help users take preventive actions, leading to saving cost, time, and effort. This paper demonstrates the identification of the liquid loading using custom intelligence alarms and an automated framework. Initially, a representative compositional well model is added to the digital twin solution enabling the automated well analysis workflow. Subsequently, custom intelligence alarms guidelines are configured to keep the well's performance and production rates under supervision with a notification capability when parameters violate the guidelines. Along with various well performance parameters being analyzed, two critical parameters for liquid loading debottlenecking, critical unloading velocity and the In-situ velocity, are investigated in the system for each well as the function of depth along well's completion. Moreover, advanced dashboards report the analysis output in an informative manner, guide users’ engineering judgment to take preventive decisions. As a result of the custom intelligence alarm, gas condensate wells suffering from liquid loading were predicted and identified. Based on the production parameter and target monitoring, these wells were unable to produce their expected mandate resulting in violating the set of production parameters guidelines. Identified wells were run through production gas rate sensitivity analysis using the analytical tool, and in conclusion, the optimal production rate was calculated. Producing the well below this critical rate causes the In-situ velocity to drop below critical unloading velocity. Additionally, using the tuned and calibrated network model, the operating choke was identified to maintain the stable flow in the well and avoid further liquid loading. This choke size was provided to field operation for implementation and saved the cost and man-hour spent during the flowing gradient surveys. The case study demonstrates significant production improvements observed for these wells, thereby significantly reducing cost and time. Using the integration of the latest production optimization platforms and custom intelligence alarm provides tools to identify wells that are currently experiencing liquid loading challenges and healthy wells that might come under the liquid loading category in the course of production, thus helping in taking proactive remedial action. Furthermore, the integrated framework provides erosional velocity-related data, which acts as a guideline while optimizing gas production.