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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