Next-Generation Gas-Lift Monitoring: Eliminating FGS Dependency

Abstract In gas-lift artificial lift systems, accurate prediction of pressure losses remains one of the most complex challenges, particularly in offshore wells where high-frequency fluctuations in total gas-liquid ratio (TGLR) occur. Conventionally we rely on flowing gradient surveys (FGS) to calibrate bottomhole pressure (BHP) estimations by matching industry established vertical & horizontal pressure loss correlations. However, these surveys are expensive, logistically burdensome, and infrequent especially in offshore unmanned platforms. The well flowing pressure gradient profile estimation is always a big challenge in such cases leading to inaccurate/ high tolerance estimations, accuracy of which improves with more stoppages to record measurement points which conversely leads to higher downtimes/ costs. The novel method described in this paper gives a permanent solution to this problem. This paper introduces a groundbreaking method to estimate flowing pressure gradient profiles and BHP without any dependency on FGS data or predefined industry correlations. It estimates fairly accurate flowing pressure gradient profile without relying on any industry published fluid pressure loss correlation. This method is field tested & verified with FGS (flowing pressure gradient survey) results with good accuracy The proposed solution centers on the iterative algorithm & new empirical correlation-based estimation, which compares the actual gas-lift injection rate to the ideal rate. By analyzing thousands of well test data from hundreds of offshore gas-lift wells, we have developed a method to dynamically adjust & estimate pressure loss calculations more accurately, reducing the need for multiple correlations. This algorithm can identify critical changes in flow conditions, allowing operators to maintain accurate pressure loss predictions and well performance even under extreme Total GLR fluctuations without any need for FGS data. The novel correlations and iterative algorithm successfully account for more accurate flowing bottom hole pressure estimation, additional pressure loss places such as partial choking in gas-lift orifice valves, less pressure loss across valves than modelled, etc across varying Total GLR conditions. Validated through multiple flowing gradient surveys, this method has demonstrated increased accuracy in bottom-hole pressure predictions, while eliminating the need for changing correlations based on Total GLR fluctuations. The results enable real-time well monitoring, offering more reliable estimations and minimizing the uncertainty that has long plagued gas-lift wells. The paper addresses how this innovative method will transform operators’ approach to managing well performance without the need for FGS data Through a combination of empirical modelling, iterative calibration, and algorithmic pressure loss estimation, a scalable solution is validated across hundreds of wells with very less deviation from historical FGS-based models. The methodology eliminates the need for manual correlation switching, streamlines real-time monitoring, and introduces a paradigm shift in gas-lift optimization. No current industry method provides a consistent solution for predicting pressure loss in wells if no FGS survey data is available with fluctuating Total GLR. By accounting for dynamic flow changes and slip velocities, this method offers a reliable, real-time solution that bypasses the limitations of traditional well modeling software & dependence of FGS survey saving millions of dollars on intervention jobs. This breakthrough can radically improve operational efficiency and well optimization, especially in highly fluctuating-Total GLR gas-lift oil producing wells