Can Artificial Intelligence Be Trusted To Keep the Oil and Gas Industry Safe?

As artificial intelligence (AI) systems begin to control safety-critical infrastructure across a growing number of industries, the need to ensure safe use of AI in systems has become a top priority. DNV GL, a global quality-assurance and risk-management company, has published a paper to provide guidance on responsible use of AI. The paper asserts that data-driven models alone may not be sufficient to ensure safety and calls for a combination of data and causal models to mitigate risk.

The paper, “AI + Safety,” details the advance of AI and how such autonomous and self-learning systems are becoming more and more responsible for making safety-critical decisions. The operation of many safety-critical systems traditionally has been automated through control theory by making decisions on the basis of a predefined set of rules and the current state of the system. Conversely, AI tries to learn reasonable rules automatically on the basis of previous experience.

Because major incidents in the oil and gas industry fortunately are scarce, such scenarios are not well captured by data-driven models alone because not enough failure-data is available to make such critical decisions. AI and machine-learning algorithms, which currently rely on data-driven models to predict and act upon future scenarios, therefore, may not be sufficient to assure safe operations and protect lives.

“The emergence of AI and digital-based solutions is the next natural step for the oil and gas sector to drive efficiencies, and 40% of senior oil and gas professionals say that digitalization has improved safety over the past 3 years,” said Simen Eldevik, author of the paper and a principle research scientist with DNV GL Risk and Machine Learning. “The industry is already developing and working with autonomous robots capable of performing a plethora of complex actions, including reading dials and gauges and navigating around obstacles on offshore assets. However, a combination of data-driven models and the causal and physics-based knowledge of industry experts is essential when AI and machine learning are used to inform or make decisions in safety-critical systems.”

The paper stresses that, if the industry can supplement these data-driven models by generating physics-based casual data, it will be significantly closer to the safe implementation of AI in safety-critical systems.

DNV GL has joined forces with Norway’s largest universities and companies, including Equinor, Kongsberg Group and Telenor, to establish a Norwegian powerhouse for AI. The Norwegian Open AI Laboratory aims to improve the quality and capacity for research, education, and innovation in AI, machine learning, and big data.

“We are supporting the industry to confidently make use of the most appropriate modeling and analytical approaches to better understand AI and reduce the high-risk scenarios we have pledged to safeguard against,” said Liv Hovem, chief executive officer of DNV GL, Oil and Gas. “As the sector accelerates its digital ambitions, confidence and trust in AI will be a huge step forward in its adoption and implementation.”

Find the paper here.



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