新型数字化转型工具在海上地面设施瓶颈分析中的成功案例

Pimpisa Pechvijitra, Manisa Sangwattanachai, N. Atibodhi, Supha-Kitti Dhadachaipathomphong, Janejira Srichaitumrong, Jirat Juengsiripitak, Ratipat Techasuwanna, Supaluck Watanapanich, Kantkanit Watanakun
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Dehydration unit (Memguard): the dew point is monitored to evaluate the unit performance. Heat exchanger and waste heat recovery unit (WHRU): the software is able to predict the chemical/mechanical cleaning time, based on the actual tube fouling condition and the maximum acceptable flow rate that the equipment can handle to achieve the required outlet temperature or heat duty. Gas/condensate/produced water filter: the predictive trend for the filter change-out time, based on the pressure drop trend and the maximum flow through the filter at the maximum differential pressure, is provided. De-oiling/de-sander hydrocyclone: the predictive model for the internal cleaning and the replacement of the liners, based on the deviation of the actual versus design performance curve, is displayed on the online dashboard.\n Apart from monitoring and prediction, this application further provides the prescriptive recommendations for event investigation. 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引用次数: 0

摘要

随着技术革新和大数据趋势的不断发展,将现场工艺工程师每月手动监测的过程性能监测实践转变为基于预测模型的实时监测,对于海上天然气生产领域至关重要。因此,地面设施瓶颈分析(SBA)计划被提出,通过持续识别限制生产的因素来提高产量,并通过对关键生产设施的自动过程性能监控的预测能力来获得洞察力。在SBA中,先进的过程建模程序,包括过程设计和仿真以及先进的过程监控,用于预测地面生产设施的能力和性能结果。开发了一个在线仪表板,该仪表板具有可视化的主要功能,可以显示现场生产速度、当前整体油气田潜力以及未来与地面设施产能的生产概况。使用此功能,可以即时识别任何时间段的瓶颈,并将通知分发给相关方,以便在需要消除瓶颈时了解并安排计划/活动。此外,在线仪表板还提供关键地面生产设施的实时性能监控,如除汞吸收装置、热交换器、气体/冷凝水/采出水过滤器、除油/除砂水力旋流器。根据设备的实际性能,实现了更精确的维修干预时间预测。通过利用SBA的新型数字化转型工具,量身定制的监控系统通过检查操作窗口的实时数据,并通过预测性能能力与资产性能管理保持一致,从而提高了设施设备的可靠性。在SBA范围内选择监测的主要地面设施有:除汞吸收单元(MRU):根据预测的床层饱和度情况,实现对吸收床换床时间的预测。脱水装置(Memguard):通过监测露点来评价装置性能。热交换器和余热回收装置(WHRU):该软件能够根据实际管道污垢状况和设备可以处理的最大可接受流量来预测化学/机械清洗时间,以达到所需的出口温度或热负荷。气体/冷凝水/采出水过滤器:根据压降趋势和最大压差下通过过滤器的最大流量,提供了过滤器更换时间的预测趋势。除油/除砂水力旋流器:根据实际与设计性能曲线的偏差,在线仪表板上显示内部清洗和更换衬套的预测模型。除了监视和预测之外,该应用程序还为事件调查提供了规定性建议。因此,SBA展示了在理论模型上对实时过程数据的自动分析以及在检测到异常事件时的建议。SBA的业务驱动力是最大限度地利用设备,减少由于过程异常事件造成的资产停机而导致的生产损失的机会。这一举措不仅通过实时数据管理工具来最大限度地提高设备效率,即时调查限制生产的瓶颈,而且通过基于算法的咨询工具来缩短分析关键地面设施性能的时间,自动监控过程性能并提前预测维护干预周期。最后,它能够使人们通过在线仪表板改进决策制定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Success Story of Novel Digital Transformation Tool for Offshore Surface Facilities Bottlenecking Analysis
With technology disruption and the increasing trend in big data, it is crucial for offshore gas production fields to transform process performance monitoring practice from manually monitoring on monthly basis by site process engineers to real-time monitoring with a predictive model. Hence, Surface Facilities Bottlenecking Analysis (SBA) initiative has been raised to provide production uplift through continuous identifying factors that constrain production, and to gain insights through predictive capability of automatic process performance monitoring on key production facilities. In SBA, the advanced process modelling programs, including process design and simulation and advanced process monitoring are used to predict the capacity and the performance outcome of surface production facilities. An online dashboard with key features to visualize the live production rate, the current overall hydrocarbon field potential, and the future production profile against the surface facilities capacity is developed. With this function, the bottleneck in any period of time can be instantly identified and the notification is distributed to related parties to be aware of and arrange plan / activity if de-bottlenecking is required. Furthermore, the online dashboard provides the real-time performance monitoring of key surface production facilities, such as mercury removal absorbent unit, heat exchanger, gas/condensate/produced water filter, and de-oiling/de-sander hydrocyclone. With this, more precise maintenance intervention time prediction as per actual equipment performance based is achieved. By utilizing the novel digital transformation tool for SBA, the presence of the tailored monitoring system leads to the enhancement of facility equipment reliability through examining live data with operating window and alignment with asset performance management through predictive performance capability. Main surface facilities, selected to be monitored under SBA scope, are as follows: Mercury removal absorbent unit (MRU): the prediction of the absorbent bed change-out time, based on the predicted bed saturation condition, is achieved. Dehydration unit (Memguard): the dew point is monitored to evaluate the unit performance. Heat exchanger and waste heat recovery unit (WHRU): the software is able to predict the chemical/mechanical cleaning time, based on the actual tube fouling condition and the maximum acceptable flow rate that the equipment can handle to achieve the required outlet temperature or heat duty. Gas/condensate/produced water filter: the predictive trend for the filter change-out time, based on the pressure drop trend and the maximum flow through the filter at the maximum differential pressure, is provided. De-oiling/de-sander hydrocyclone: the predictive model for the internal cleaning and the replacement of the liners, based on the deviation of the actual versus design performance curve, is displayed on the online dashboard. Apart from monitoring and prediction, this application further provides the prescriptive recommendations for event investigation. Therefore, SBA demonstrates the automated analysis of real-time process data on theoretical models and the suggestion when the anomaly event is detected. The business drivers of SBA are to maximize the utilization of equipment and reduce the chance of production loss due to downtime of asset from process anomaly events. This initiative not only maximizes equipment efficiency through the live data managing tool to instantly investigate bottlenecks that limit production, but also shortens time spending for analysis of key surface facilities performance via the algorithm based advisory tool to automatically monitor the process performance and predict the maintenance intervention period in advance. Lastly, it is capable of enabling people to improve decision making through the online dashboard.
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