{"id":10341,"date":"2017-11-08T01:00:21","date_gmt":"2017-11-08T09:00:21","guid":{"rendered":"http:\/\/www.palada.net\/index.php\/2017\/11\/08\/news-4114\/"},"modified":"2017-11-08T01:00:21","modified_gmt":"2017-11-08T09:00:21","slug":"news-4114","status":"publish","type":"post","link":"https:\/\/www.palada.net\/index.php\/2017\/11\/08\/news-4114\/","title":{"rendered":"How Big Data and Analytics increase Production Asset Uptime in Oil Fields?"},"content":{"rendered":"

Credit to Author: Eddy Lek| Date: Wed, 08 Nov 2017 08:08:44 +0000<\/strong><\/p>\n

Oil and Gas producers continue to be highly focused on reducing their operations costs to reach break-even prices below $40\/bbl.\u00a0 Although oil prices have recovered to above $60\/bbl recently, companies must continue to make investments in improving operational efficiencies to sustain and improve their profitability in the new \u2018normal\u2019 era of low oil prices.<\/p>\n

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Where to focus to sustain continuing cost reduction\u2026.<\/h4>\n

According to ARC Advisory Group, companies could lose up to 5% of production due to unplanned downtime. The average impact of unscheduled downtime has caused process companies to lose more than $20 billion in production annually. As production assets in the oil fields -whether offshore or onshore- are always exposed to harsh environments, maintaining equipment to keep up with production is often challenging.\u00a0\u00a0 Due to the asset-intensive nature of oil and gas companies, any slight improvement in asset utilization can result in a huge gain in revenue and cashflow.<\/p>\n

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Big Data and Analytics to reduce unplanned downtime\u2026<\/h4>\n

Oil and gas companies have been generating huge amounts of operational data for several decades, long before the term IIoT was coined.\u00a0 However, the recent improvement in cloud technology, analytics and computing power has enabled the transformation of data into insights that can provide meaningful decision support to both the operational teams and processes, helping enterprises to gain the next level of operational efficiencies.<\/p>\n

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The advanced pattern recognition and machine learning algorithms in predictive analytics have enabled reliability and maintenance teams to identify and diagnose asset problems prior to failure, effectively improving asset reliability through early warning before failures happen, resulting in reduced unplanned downtime and improved asset availability. Digital twins of assets and their operating models including past loading, ambient and operational conditions are created enabling advanced process modeling and simulation. A unique asset signature for each type of equipment is created such as offshore pumping stations, compressors, drilling rigs or any other critical piece of equipment.\u00a0 Real-time operating data is then compared against these models to detect any subtle deviations from expected equipment behavior, allowing reliable and effective monitoring of different types of equipment with no programming required.\u00a0 This enables the maintenance and reliability teams to focus on what they do best \u2013 keeping their assets healthy and operational.<\/p>\n

4 different types of Analytics to increase asset uptime<\/strong>:<\/strong><\/h4>\n