Transforming Operations Through Automated Network Validation, Calibration and Pipeline Performance Monitoring for Underground Gas Storage Systems

This study introduces an optimized process for automating calibration and monitoring pipeline performance in gas production and injection networks within an underground gas storage (UGS) facility. The surface network model leverages the latest operational data for all network entities, ensuring that the system accurately reflects current field conditions and provides precise estimates of pipeline performance factors. These models integrate seamlessly with database systems, triggering automated workflows that facilitate model updates, calibration, and improved pipeline management and utilization. To ensure precise network performance assessment, gas production and injection networks are prepared using a steady-state network simulator. Data from digital field instrumentation is integrated into an SQL-based production data management system (PDMS), initiating automated workflows for network management. A valid surface network model that accurately mimics field conditions serves as the backbone for operational planning. When a new network model is uploaded into the system, it is first automatically validated against the measured data. Next, an automated workflow analyses if pressure and temperature, for any network branch is breached. Calibration is triggered for eligible branches using the latest measurement data and adjusting pre-defined parameters like friction factor and U-multiplier to meet tolerance thresholds. This ensures realistic network performance analysis and bottleneck identification. A third automated workflow estimates pipeline transportation efficiency and load rate, vital for operational reliability and cost management, supporting sustainable and safe UGS operations. The system filters ineligible pipelines out of the calibration loop, avoiding unnecessary workflow executions and saving analysis time. Automation conserves engineers’ valuable time, mitigates human errors, and results in a significant reduction in data processing and validation time. Consequently, engineers can make more informed decisions, and manage resources more effectively. Daily evaluation of pipeline flow efficiency provides a clear measure of how effectively the pipeline transports gas compared to its maximum potential capacity, while load rate estimation indicates pipeline utilization relative to its designed capacity. Regular monitoring of these metrics ensures timely maintenance, reliable gas delivery, identification, and management of operational inefficiencies. This optimized approach enhances operational reliability and supports sustainable, cost-effective management of UGS facilities. The surface network model, along with boundary conditions, accurately mirrors alternating production and injection operations in a digital environment, reflecting real-world dynamics. Monthly ambient temperatures are integrated to adjust for temperature variations affecting gas operations. Calibration involves fine-tuning pressure and temperature drop, the latter being a crucial factor for injection networks due to its impact on system performance. Furthermore, the Weymouth equation is employed for estimating pipeline gas flow rate, owing to its compatibility with the specific pipeline design and operational conditions for the networks used in the study, thus offering enhanced accuracy in flow efficiency estimation compared to alternative equations.