Machine-Learning Approach Improves Deepwater Facility Uptime

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 195875, “Improving Deepwater Facility Uptime Using Machine-Learning Approach,” by Ajay Singh, Sathish Sankaran, SPE, and Sachin Ambre, Anadarko, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September–2 October. The paper has not been peer reviewed. Deepwater oil and gas facilities encounter up to an estimated 5% annual production loss, estimated at billions of dollars, because of unplanned downtime. This paper describes an automated work flow that uses sensor data and machine-learning (ML) algorithms to predict and identify root causes of impending and unplanned shutdown events and provide actionable insights. A systematic application of such a method could prevent unfavorable operational situations in real time using equipment and process sensor data. Overview An assessment of the magnitude of deferred production resulting from unplanned shutdown from one of an operator’s deepwater facilities revealed that, overall, 43% of all shutdown events belong to unplanned-but-controllable events. Here, “controllable” means that, if the event is identified ahead of shut-down, mitigation is possible to avoid the shutdown. Fig. 1 provides further classification of unplanned downtime contributed by automation hardware failure, equipment failure, process trips, and production ramp-up. The existing toolkit and systems in place are not always adequate to identify and predict abnormal events that could lead to unplanned facility shut-down. The interaction among process subsystems and disturbances that propagate across them with changing operating conditions are hard to predict without a fit-for-purpose model (or a digital twin). Often, engineers and operators visualize key sensor data, and, using their knowledge of process and control strategy, first attempt to identify anomalous process behavior on the basis of alarms received. Topside facilities often have alarms installed with minimum and maximum trip settings. As sensor data approach those trip settings, a pre-alarm is generated and the control-room operator is notified. If the trip involves multiple process variables and if the alarms are not rationalized adequately, the operator may be inundated by alarms, because alarms are designed on each individual sensor and not at system level. With good alarm rationalization philosophy and best practices, alarm flooding can be mitigated, but the multivariate precursors will not always be captured by looking at the sensors one at a time. Additionally, early signs of an impending abnormal event would require analysis of hidden process signals before they manifest themselves as alarms. Therefore, an intelligent advisory system is desired which can: Ingest numerous sensor data Generate a single alarm indicating the health of a particular system or piece of equipment Predict abnormal events that could lead to a shutdown Potentially provide insight to prevent upcoming shutdown through root-cause analysis

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