使用实时数据和集成模型诊断结垢问题,提高泵的性能

Baraa Al-Shammari, Nitin L. Rane, Shareefa Mulla Ali, Aala Ahmad Sultan, S. A. Sabea, Meqdad Al-Naqi, M. Pandey, F. L. Solaeche
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引用次数: 0

摘要

Burgan油田01采集中心(KwIDF GC-01)的科威特综合数字油田项目从油井和处理设施中获取实时数据,作为其生产监控计划的输入。现场的实时数据被输入到一个集成的生产模型中,用于分析和优化泵的性能。自动化的工作流程会对关键井和设施参数发出警报,以识别存在潜在结垢问题的井。KwIDF工作流程与更新的井模型相结合,可以可视化结垢对井口性能的影响,从而帮助量化结垢沉积造成的相关生产损失。此外,还进行了敏感性分析,以确定当前和最佳的泵工作条件,并优先考虑结垢清理工作。在Burgan油田,利用电潜泵(esp)对井的关键实时参数进行异常监测,大大提高了对井口堵塞和流线结垢的诊断。自动化工作流程实时校准集成生产模型,使工程师能够快速分析当前泵的运行状况,并制定积极的行动计划。实时数据和自动化模型的应用帮助作业者的生产团队做出明智和及时的决策,使他们能够在最佳工作条件下运行泵,从而使他们能够将油井产量维持在目标水平。本文介绍了一种创新的方法,将实时数据和集成模型应用于自动化工作流程中,以提高诊断地表流动网络中结垢沉积的能力。举例说明了集成技术在识别井口节流道和管线结垢方面的应用,以及优化泵性能的结垢清除方案的优先级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Real-Time Data and Integrated Models to Diagnose Scale Problems and Improve Pump Performance
The Kuwait Integrated Digital Field project for Gathering-Center 01 (KwIDF GC-01) at Burgan Field acquires real-time data from wells and processing facilities as input for its production-surveillance program. Live data from the field is fed into an integrated production model for analyzing and optimizing pump performance. An automated workflow process generates alarms for critical well and facility parameters to identify wells with potential scaling issues. KwIDF workflows are integrated with updated well models to visualize the effect of scale build up on the wellhead performance and thereby assist in quantifying the associated production losses caused by scale deposition. A sensitivity analysis is also performed to identify current and optimal pump operating conditions and prioritize scale cleaning jobs. The exception-based surveillance of key real-time parameters for wells utilizing electrical submersible pumps (ESPs) in Burgan field has significantly improved diagnostics of scale deposition at wellhead chokes and flowlines. Automated workflows calibrate an integrated production model in real-time, which enables engineers to run a quick analysis of current pump operating conditions and make a proactive plan of action. The application of real-time data and automated models has aided the operator's production team in making informed and timely decisions that enable them to run pumps at optimal operating conditions, with the result that they are able to sustain well production at target levels. This paper describes an innovative approach to applying real-time data and integrated models in an automated workflow process for enhancing capabilities to diagnose scale deposition in the surface flow network. Examples are presented to demonstrate the application of integrated technology for identifying scaling at wellhead chokes and flowlines and prioritizing a scale removal program for optimizing pump performance.
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