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Machine-Learning Approach Improves Deepwater Facility Uptime

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 shutdown, 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.

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 The paper has not been peer reviewed.
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Machine-Learning Approach Improves Deepwater Facility Uptime

01 May 2020

Volume: 72 | Issue: 5

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