A Robust Surveillance and Optimization System for Unstable Flows in Offshore Gas Lifted Wells

Abstract

In [1] and [2], an automated surveillance and optimization system has been developed and deployed to multiple offshore gas lifted assets. In this paper, we will present key enhancements to the system that improve the detection and management of unstable flows in offshore gas lifted wells. The enhanced system provides engineers and operators with a comprehensive solution for well flow surveillance and optimization. Examples will be presented to show the successful implementation of quantitative slug scanning, gas lift multi-pointing diagnostics, and production surveillance for unstable flows. The slug scanning module supplements the pattern recognition and machine learning features in GLOWTM [1]. It scans the historical high-frequency pressure data, quantifies the amplitude and frequency of the cyclic oscillations, and then aggregates the data into bins of varying severity levels and a simple slugging index that can be used by operations for effective communication. To better understand gas lift multi-pointing that damages gas lift valves and harms production, transient flow models were developed with accurate modeling of gas lift valves, and then calibrated to historical well tests. Models accurately replicated the amplitude and frequency of pressures that may appear like hydrodynamic slugging, and revealed the open-and-close behaviors among multiple gas lift valves. Finally, the calibrated models were used to generate operational maps with different flow regimes, and such maps aid either asset staff or the GLOW™ system to optimize asset production while managing various types of unstable flows. The developed system combines data-driven and physics-based approaches for online advisories, and utilizes a novel history matching procedure to enhance the model's predictive capability for complex well behaviors. In summary, the system provides a comprehensive, robust, and integrated set of tools that meets the surveillance and optimization needs of asset staff.