Oil and Gas Industry

Oil and Gas Industry

With the current advent of AI becoming accessible for industries. There is a whole lot of AI services which are untapped in this space which can be used in Oil and Gas services. While we look into this industry from an operations perspective, there are reasons a business owner might be unaware due to their hectic operations in this sector. We would like to bring into notice through the transcript below, how AI can offer considerable value in cost savings, operations efficiency, accuracy and awareness to the owner in its operations. While we look into the whole gamut of operations in O&G industry be it in Upstream, Midstream and Downstream, there are already applications in AI which are bringing considerable cost savings to all the three areas.

Oil and Gas Sector

While there are many cases available as mentioned below where AI is bringing a significant change, we are elaborating a case study in Mid stream oil and gas section on how it brought in automation, reduced costs, brought in better efficiency through the use of machine learning algorithms to achieve the same.

Use Case: Monitor health and predict failure of systems, subsystems, assets and components using advanced machine learning algorithms.

The case is a deployment of a reliable application using AI for a petrochemical plant. The plant had two key challenges that was aimed to address with reliability application. The first was to improve the reliable operation both of the equipment in the plant and the processes in the plant. And the second challenge doing that at scale. That meant doing it at scale across the entire plant and all of the equipment in that plant and then also being able to replicate that for all of those 200 plants. An initial analysis was to look at the unplanned maintenance costs at this particular plant which translated to 100 million dollars on the table that the reliability solution could address. 

The goal of the application was to adjust any available data that included maintenance data, asset data and tag data. So sensor data can predict when things might go wrong. And that included events both at the equipment level and at the process level. This deployment covered five unique data sources and configured machine learning models for over a thousand individual piece of equipment. This was done by training and evaluating tens of thousands of individual models and identified issues on those thousand pieces of equipment both stationary equipment like heat exchangers or distillation columns and rotating assets like compression units and turbines.

The result is a structured database that tells us where for example a pressure sensor is relative to a particular pump. So that means to ingest over 4 billion individual measurement points in a matter of weeks and within that time frame already be configuring machine learning models and iterating very quickly to improve performance. The types of benefits the customers see with the reliability application are improved process uptime equipment performance improved efficiency and higher safety and operations.

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