Predictive maintenance for offshore oil and gas platforms

Asset failures and accidents often cost millions of dollars due to lost production and repairs. Without sufficient lead time in identifying future asset failures, oil and gas operators can only react—and count their losses. SparkCognition's predictive maintenance for offshore oil and gas platforms solution helps lessen the probability of a critical asset failure.

SparkCognition’s scalable predictive maintenance for offshore oil and gas platforms solution uses artificial intelligence (AI) and machine learning to proactively mitigate equipment failure for oil and gas platforms.

SparkCognition’s predictive maintenance solution for offshore oil and gas platforms have enabled close to 99% efficiency in past deployments, driving hundreds of millions of dollars in increased annual production output, and significantly lowering maintenance project costs.

Gain the power of expecting the unexpected:

  • Identify unexpected subsystem failures faster—up to 9 days faster
  • Protect the safety of workers by preventing catastrophic accidents
  • Streamline maintenance activities, reducing costs by 5-10% annually

“Cutting edge technologies, like AI-based predictive analytics, are key enablers to improving the efficiency of our operations and meeting our ambition to become a net zero company by 2050 or sooner. Working collaboratively with SparkCognition, we have delivered this project in an agile way.”

– Fereidoun Abbassian, VP of Transformation, Upstream Technology at bp.

Forecast unexpected subsystem failures

The SparkPredict® product has been used to identify 75% of customer’s production-impacting events. For one oil and gas supermajor, the projected economic impact of SparkCognition’s predictive maintenance solution across their offshore platforms is $800 million annually.

Tap the potential of predictive + prescriptive maintenance

SparkCognition’s DeepNLPTM natural language processing product delivers the most relevant documentation to streamline repair and capture user inputs to improve actionable results. It’s been deployed to reduce maintenance costs by 5-10% annually.

Improve employee and asset safety

Scalable predictive analytics solutions from SparkCognition model normal performance to more accurately detect deviations in the data. In turn, this helps to reduce field visits, lower health and safety risks, and lessen the chance of a catastrophic asset failure.

Achieve sustainability and efficiency goals

While AI-powered predictive analytics act as a safeguard against catastrophic asset failures that could release tons of hydrocarbons into the environment, they also help to improve production efficiency and proactively reduce carbon footprint. By moving beyond reactive, resource-intensive maintenance projects, oil and gas operators can proactively maintain equipment only when needed, using time and energy more wisely.

For one customer, the projected economic impact of SparkCognition’s predictive maintenance solution across their offshore platforms is $800 million annually

While the offshore oil and gas industry faces constant pressure to reduce maintenance costs and extend equipment life, the biggest challenge, by far, is the unknown. When and where will an asset fail, and how bad will the consequences be?

Consider this: just 12 hours of downtime for a 200K bpd production platform can result in $6-8M in lost production opportunity. And that doesn’t include the cost of labor, the need for new parts, or the cost of the company’s public image when unforeseen accidents occur.

Machine learning technologies are already allowing leaders in offshore oil and gas companies to fully leverage the potential of their data, enabling safer and more predictable operations.

In past deployments for offshore oil and gas, SparkCognition has increased the ability to identify unexpected subsystem failures by 75%, and more than doubled previous lead times.

For one supermajor with high-volume offshore platforms, the SparkPredict predictive analytics product was deployed across 20 critical subsystems to predict impending failures and optimize maintenance activities.

That’s the power of expecting the unexpected.

Read the full case study to learn how we did it and the results of the project.

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