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Data Science in Oil and Gas

Impact: Artificial Intelligence, Machine Learning, and Data Science in Oil and Gas

The cascading effect that digitalization has left on ANY industry, is not unknown to many. From spending weeks to perform simple tasks, to getting them done in a single click: No, this is not science fiction. This is the power of digitalization. This technology encompasses multiple concepts like Big Data, Artificial Intelligence & Data Science, etc. Their applications, and especially Data Science in the Oil and Gas industry; have been immensely helpful in transforming how everything works, and this is just the beginning! Read on to know more. 

What is Oil and Gas analytics with Data Science in Oil and Gas?

Oil and gas analytics or what some might call, “Oilfield Analytics” utilizes most of the statistical methods that are used in other industries to pinpoint patterns among hundreds of variables in constant flux. It uses large amounts of field and rheological data while frequently employing machine learning algorithms to enable Condition-based Predictive Analysis and Big Data insights that operators and managers use to maximize yield and efficiency, propagate innovation and reduce risk.

Advancements in process automation, remote instrumentation, process engineering, and the Industrial Internet of Things (IIoT) and data Science in Oil and Gas have paved the way for transformation in the oil and gas industry, similar to changes underway in retail, manufacturing or financial services. With assets and people everywhere, energy producers can leverage remote sensors and predictive analytics to safely monitor operations in real-time and optimize field maintenance. Data Science in Oil and Gas analytics poses as an excellent tool to be utilized for increasing efficiency, reducing NPT or Non-Productive Time for which the industry loses millions every year and ensuring economic viability. For upstream producers working offshore, companies can use advanced analytics and submersible platforms to optimize production settings and raise output. Meanwhile, midstream and downstream organizations can use operational data and analytics to optimize production and reserves recovery efforts, as well as promote collaboration across the organization.

Transcendence into future: Predictive Maintenance using Data Science in Oil and Gas

Ever since the inception of Predictive Maintenance, the way any industry functions has been changed. Developing predictive simulation models of any operation that might involve a life/environmental hazard has become the new order. Predictive maintenance doesn’t require anything more than an informal mathematical computation on when machine conditions are at a state of needed repair or even replacement so that maintenance can be performed exactly when and how is most effective. However, the onset of Machine Learning and Data Science in Oil and Gas; has bolstered the efficiency by multitudes. It eliminates the need for guesswork and ensures that the managers (present on-site) get to focus on other important work that requires human presence. 

Data Science in Oil and Gas has increased asset optimization, operational efficiency, and has reduced downtime errors. Data Science has ensured that we leverage past and continuous data, to predict the future: yes it is as ridiculous as it sounds. With the help of Data Science and Machine Learning in Oil and Gas, we create and foster simulation models that help us to detect errors and issues from past experiences. 

An interesting take on how digitalization will transform the future of this industry

What follows is an example that will help you understand how Data Science and Artificial Intelligence have transformed the Oil and Gas industry. 

Let us assume that, a Well “A”, has recently experienced a kick which later turned into a blowout causing the entire operation to catch fire; and hence end up being devastated. Now, to ensure that something similar does not happen again; a firm collects all the data and information relevant to WHAT caused the devastation. With the help of technologies like the Internet of Things, Artificial Intelligence and Machine Learning, predictive models and Data Science’s applications in Oil and Gas can be utilized to simulate the entire incident again. 

How does this help?

It helps us to train management staff, and operators in a way such that they are well aware of the procedure to tackle such a situation.  Thus saving a lot of money and human lives. 

How can one create such models?

Predictive Models or what are also known as, “Surrogate Models” are devised with the help of Artificial Intelligence, Internet of Things (IoT) and Data Science in Oil and Gas. The intertwining of these concepts can be used to develop simulators where past and continuous data is combined to pre-empt what various situations that might occur on an oilfield. This is an extremely essential tool that has been instrumental in saving millions.

Final Words on Data Science in Oil and Gas 

In conclusion, early adopters of Artificial Intelligence, Machine Learning, and Data Science in Oil and Gas industry; will likely develop a significant competitive advantage, and the adoption rate of new technology is exponentially faster than it was a few decades ago. Mainstream adoption of these new technologies is expected to rise even further, over the course of the next few years. Companies will soon experience significant upthrust in efficiencies that will translate into competitive advantages over the rest of the industry. It is likely time to consider how your company could begin to harness these technologies to remain relevant!

(The views and opinions expressed in this article do not necessarily reflect the views of Energy Dais.)

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Shayantan Shivam Chakraborty

Currently pursuing a Bachelors in Petroleum Engineering, Shayantan is an ardent reader by day and a passionate writer by the night. He wishes to contribute to the academia, by emanating his comprehension of the Energy industry.

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mm

Shayantan Shivam Chakraborty

Currently pursuing a Bachelors in Petroleum Engineering, Shayantan is an ardent reader by day and a passionate writer by the night. He wishes to contribute to the academia, by emanating his comprehension of the Energy industry.

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