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The last ten years has presented major changes to the oil and gas companies. The demand for oil and its products is drastically increasing, but the prices are facing volatility and have dropped massively. Due to volatility and reduction in the prices, most companies are keeping in mind their investments and internal operations to determine what they can reduce in order to keep the business running and while at the same time maximizing the use of the company’s assets.

On that note, companies have realized the contribution of machine learning in increased uptime and higher recovery rates. Those companies that utilize predictive, data-based approach experience less than 36% unplanned downtime and can save up to $17 million on average annually. Besides, the adoption of the Internet of things (IoT) can grow the GDP by over 0.8%.

 

IoT In The Oil And Gas Industry

Basing on facts from Research and markets, the estimated Global IT spending in the oil and gas will be valued at $48.5 billion by 2020. The efficiency of resources in the industry is a significant determining factor for growth. IOT has been crucial in offering affordable and new strategies to work more smarter for a low cost.
The affordability of these technologies will enable both large and small companies to change their enterprise asset management (EAM) and increase uptime. Besides, the industry has adopted the use of sensor technology and it will be widely used in the collection of more and large amounts of data.

 

The two methods of machine learning for asset management

 

Simulated Modeling

It is also known as the Digital Twin concept and is generally a simple concept to understand, but seems more complex. For this method, a virtual clone of a machine asset is developed by utilizing the blueprints of the machine. For its operations, it needs data scientists and design technicians.
It utilizes the supervised machine method and needs the virtual machine to quickly learn the asset behavior before identifying any performance issues.

 

Manual Statistical Modeling 20

The most important approach to machine learning is to develop an internal group within the organization that can create predictive models. For this to be possible, sample data is obtained from machines and data scientists can then develop statistical models. However, the greatest challenge working with such an industry is the shortage of engineers and data scientists. On that note, this machine learning will be limited to a few industry giants. The advantage is that you can create an internal data science center of excellence for this type of concept.

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