使用严格的多相流模型进行泄漏检测和在线流量保证

K. Havre, C. Trudvang, G. Kjørrefjord, Sonia Smith, Colin C King, Jaqueline Vinicombe, Trevor A. Roberts
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引用次数: 0

摘要

Cenovus Energy公司在加拿大纽芬兰和拉布拉多省St. John’s以东350公里的White Rose油田和卫星延伸区部署了一套严格的多相流保证在线解决方案,用于检测泄漏和监测水合物形成状况。25口生产井通过水下管汇连接到SeaRose浮式生产储油和卸载(FPSO)系统,通过4条柔性管线和立管。该公司开发了一种在线海底咨询器,为控制室操作人员提供了更好的监控/可视性,以检测海底系统(包括机械管线连接器)内的潜在泄漏和水合物形成。在线解决方案利用了商用多相流模拟器。开发了一种在线实时模式(RTM)来模拟将管汇连接到seaose FPSO的生产循环。这些井配备了多相流量计,在试井期间定期进行校准。在实时多相模型中,从仪表得到的协调流量被用作入口边界条件。该RTM充当生产网络的数字孪生体。作为在线海底顾问泄漏检测系统(LDS)的一部分,斯伦贝谢为多相生产网络提供了改进的泄漏检测算法。该解决方案使用了14个表示泄漏的签名,这些签名构成了广义多变量LDS的基础。使用人工智能和数据聚类来确定签名向量是否表示泄漏。通过使用多重泄漏签名,系统在传感器故障和漂移方面变得更加鲁棒。多重签名还减少了假警报的数量,并使LDS减少了对模型校准的依赖。与传统的基于质量平衡模型的LDS相比,签名、人工智能和数据聚类的使用是新的。该理论是用论文中这14个签名中的4个签名的结果来描述的。顾问系统监测潜在的水合物形成条件,并计算管线连接处的水合物裕度,这些连接处已被确定为潜在的“冷点”。严格的流线连接器模型已经在沿着流道的位置实施,因为它们在现场存在。该模型经过微调,可用于估计机械管线接头壁温度。这为控制室操作人员提供了一个实际的反应时间估计,以管理紧急关闭,并在达到水合物条件时启动警报,提示立即采取预定义的保护措施。
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Use of Rigorous Multiphase Flow Models for Leak Detection and Online Flow Assurance
Cenovus Energy has deployed a rigorous multiphase flow assurance online solution to detect leaks and monitor hydrate formation conditions at the White Rose Field and satellite extensions 350 km east of St. John's, Newfoundland and Labrador, Canada. Twenty-five production wells are connected via subsea manifolds to the SeaRose floating production storage and offloading (FPSO) system, through four flexible flowlines and risers. An online subsea advisor has been developed to provide control-room operators with enhanced monitoring/visibility in detecting potential leaks and hydrate formation within the subsea system, including the mechanical flowline connectors. The online solution makes use of a commercial multiphase flow simulator. An online real-time mode (RTM) was developed to simulate the production loops connecting the manifolds to the SeaRose FPSO. The wells are equipped with multiphase flowmeters, which are calibrated at regular intervals during well test campaigns. Reconciled flow rates from the meters are used as inlet boundary conditions to the real-time multiphase model. This RTM acts as a digital twin of the production network. As part of the online subsea advisor leak detection system (LDS), Schlumberger has delivered improved algorithms for leak detection in multiphase production networks. The solution makes use of 14 signatures indicating leaks, which form the basis for a generalized multivariable LDS. Artificial intelligence and data clustering are used to determine whether the signature vector indicates a leak. By making use of multiple leak signatures, the system becomes more robust with respect to sensor faults and drift. Multiple signatures also reduce the number of false alarms and make the LDS less dependent on model calibration. The use of signatures, artificial intelligence and data clustering is new compared to traditional mass balance model-based LDS. The theory is described with results from four of these 14 signatures in the paper. The advisor system monitors the potential for hydrate formation conditions and calculates the hydrate margin at the flowline connectors, which have been identified as potential "cold spots." A rigorous flowline connector model has been implemented at positions along the flow path where they exist in the field. This model is fine-tuned to estimate mechanical flowline connector wall temperatures. This gives the control-room operators a realistic estimate of reaction time to manage an emergency shutdown and initiates an alarm when hydrate conditions will be reached, prompting immediate action of predefined safeguard measures.
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