AI in Oil and Gas | Machine Learning in Oil and Gas

Don’t wait until it’s too late to prevent the next critical asset failure.

LEVERAGE AI TO INCREASE PRODUCTION, REDUCE COSTS, AND IMPROVE SAFETY IN THE OIL & GAS INDUSTRY

Prevent unique catastrophic accidents

From onshore to offshore to petrochemical to LNG plants, nothing is more crucial for worker safety and continuity of production than predicting and preventing critical and/or catastrophic asset failures before they happen.

With SparkCognition’s machine learning-based predictive analytics, operators get additional insight that will help them avoid unexpected downtime and astronomical costs. In a past deployment, our solution successfully identified 75% of production-impacting events, on an average of 8 days in advance.

Optimize maintenance and respond faster to emergent events

Recurring failures in assets and subcomponents drive 10% or more unexpected downtime annually and cost millions of dollars of lost production.

Our solution gives upstream, midstream, and downstream oil & gas operators invaluable advance warning to plan corrective actions and optimize maintenance scheduling for emergent asset failures—more than doubling previous lead times to address pending failures in some deployments.

Improve efficiency and sustainability through digital transformation

Operational efficiency is critical in the oil and gas industry, and with the exceptional amount of data at hand, there are many opportunities to generate more output.

SparkCognition enables operators to leverage AI and machine learning to dramatically improve productive uptime and efficiency, reduce their carbon footprint, and leverage both predictive and prescriptive insights derived from currently collected enterprise data.

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,
former VP of Transformation,
Upstream Technology at bp

Reduce unexpected downtime

Get notice of pending asset failures up to weeks in advance, reducing risk exposure to workers and preserving uptime. Our solutions have been able to identify 75% of production-impacting events.

Minimize operational costs

Machine learning-based predictive analytics unlock new modes of visibility into complex operations, identifying precursors to system and subsystem failures. Our solutions have been shown to reduce maintenance costs by 5-10% annually.

Optimize asset performance

Advanced behavior modeling flags underperformance from actual observations and system behavior within the contextual environment, dramatically improving production in terms of increased annual output.

Streamline decision making

Prescriptive analytics understand both structured unstructured data to improve the decision-making process under fire. Our solutions can recommend and analyze ramifications for different potential plans of action, and even help set a chosen plan into motion.

Prevent zero-day cyber attacks

Preventing 99.9% of never-before-seen attacks, SparkCognition provides highly trusted, highly awarded protection against zero-day ransomware, viruses, malware, and more

Rapidly scale AI

Using unsupervised machine learning techniques with models that adapt and maintain themselves dynamically, SparkCognition solutions scale efficiently for onshore, offshore, FPSO, refinery, LNG, and petrochemical operations.

Use Case Library

Predictive maintenance for offshore oil and gas platforms

Without sufficient lead time in identifying future asset failures, oil and gas operators can only react—and count their losses from halted production and repairs. SparkCognition offers a scalable predictive analytics software solution using AI and machine learning to proactively mitigate equipment failure for oil and gas operations. Our solutions have enabled close to 99% efficiency in past deployments, driving hundreds of millions of dollars in increased annual production output and conserved maintenance costs.

Read Use Case