How is AI Digitizing Offshore Oil and Gas?
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How is AI Digitizing Offshore Oil and Gas?

By Energy CIO Insights | Monday, November 18, 2019

There are two major applications of AI technology in the oil and gas industry–data science and Machine Learning (ML). In the offshore oil and gas business, ML facilitates the enterprises to examine multifaceted operations and respond with instant outputs that human operators may not have noticed.

FREMONT, CA: Offshore activities take place in waters of more than half the nations in the world. Wells are drilled with a wide range of technologies with newer advancements addressing the challenges of a digital economic setting. Artificial intelligence (AI) is one technology that is tapping on the doors of the offshore oil and gas industry with a promising Return on Investments (ROI).

Employment of Data Science, AI and its Subsets in Oil and Gas

There are two major applications of AI technology in the oil and gas industry–data science and Machine Learning (ML). In the offshore oil and gas business, ML facilitates the enterprises to examine multifaceted operations and respond with instant outputs that human operators may not have noticed. ML also permits the detection of patterns according to a range of inputs. Furthermore, AI also helps to test the impacts of innovation or to measure the environmental risks in the industry.

Data science leverages AI to gain valuable insights from data while using neural networks to connect similar data pieces and creating a larger overall picture. AI can be used to understand the complex data sets and also to find out new exploration opportunities while ensuring the competent utilization of the present ones.

Top Onshore/Offshore Technology Solution Companies

AI Applications

In January 2019, BP Ventures funded in a Houston-based startup Belmont Technology, which resulted in an AI-based platform nicknamed “Sandy” to hasten the company’s AI capabilities. Sandy enabled to interpret geology, geophysics, and reservoir project.

The technology bridges the gap and links the data together while recognizing new connections and workflows, and forms a robust image of BP’s subsurface assets. With its neural network, the oil company can then consult the data in the knowledge graph.

In another illustration, the SparkPredict platform uses ML algorithms to analyze sensor data that allows the company to recognize suboptimal operations and the approaching failures in advance.

Potential of AI in Oil and Gas

As known, AI has already been integrated by the oil and gas companies across many sectors. With a rapid change of the digital landscape, the industry will need progressive advancements to proceed at the same pace. Cognitive environments can offer the decision-makers with a common platform to enable target analysis and simulation, share insights, and assimilation of complex data sets into the system.

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