When it comes to efficiency in the oil and gas industry, improvements are often measured in small increments. Remote and dangerous locations, long distances pipelines traverse, and sensitivity to external threats are all key risk points that cannot be easily resolved.
Still, it's welcome that there is room for improvement in the way oil and gas companies link their vast operating areas to their headquarters. And artificial intelligence (AI) has the potential to be a real game changer.
In our customer meetings, there was understandable excitement that AI and big data, powered by machine learning (ML), edge computing, the cloud, and the Industrial Internet of Things (IIoT), can be a true disruptor in significantly increasing the efficiency of oil and gas exploration, extraction, refining, and marketing.
Innovation from a global oil and gas company
The world's largest oil and gas company has put AI and digitalization at the heart of its 21 strategy.cent Revolution of the century. They recently conducted a proof of concept (PoC) with Nokia. There, they explored using AI capabilities for anomaly detection along the pipeline in an application called fiber sensing.
There is an opportunity for fiber already buried underground to transfer data along the length of the pipeline. Optical fibers are quite remarkable, and the reflection of light through them can be affected by very simple things. Vibrations caused by rain, trucks crossing the road, knocking or drilling holes near the pipeline, earthquakes – all of these intrusions can be detected by Nokia's DWDM multiplexers up to 140 kilometers away from the pipeline.
Pipeline safety and security is a critical focus for oil and gas companies, so this type of early intrusion detection can have an incredible impact on operations.
AI use cases in oil and gas
Many oil and gas companies are exploring opportunities to leverage AI. The vast amount of telemetry data transferred between pipelines and headquarters is at the heart of several promising new use cases. Nokia's network automation solutions manage both optical and IP/MPLS infrastructures and play a key role in analyzing this telemetry data.
One use case is combining AI and ML technologies for predictive purposes. Using available data, network automation can detect early signs of congestion and recommend incremental jumps in bandwidth when and where needed to support the grid.
Other use cases include monitoring optical and IP/MPLS networks using deep analysis tools and making recommendations on how to optimize networks for performance and resiliency. Network automation systems can also assess and recommend the best network settings to achieve accurate timing across the network, ensuring optimal time synchronization. Additionally, by reviewing telemetry data, network automation systems can identify routers or switches that may be deviating from performance standards and recommend reconfiguration options.
Another opportunity is to run test scenarios for new technologies before actually deploying them on a live network. Testing can be done entirely in-house to avoid external data center facilities, and scenarios can be fed into an AI system to determine the impact on the entire network. Digital twinning not only helps optimize technology deployment, but also reduces the overall carbon footprint.
These types of use cases improve network performance, resiliency, and operational efficiency, which becomes even more important after the introduction of many next-generation technologies. Expectations for performance, reliability, and security from AI are much higher. Quality is critical to success, as defects in AI models can undermine the validity of results, resulting in wasted time and increased costs.
robust security
With so much data being transmitted over vast areas of business, oil and gas companies remain vulnerable to cybercrime. Use cases that can provide additional protection against attacks are becoming more attractive.
These use cases are effective in protecting against modern cyber threats. However, many oil and gas companies are currently seeking solutions to address the coming quantum threat.
Cryptography-related quantum computers (CRQCs) that can break modern cryptographic codes are not yet available. However, malicious actors have already been seen engaging in a technique called Harvest Now Decrypt Later (HNDL).
A defense-in-depth protection system is recommended to protect oil and gas operations. By deploying encryption at multiple layers of the network, including OTNsec at the optical layer and ANYsec/MACsec at the data link layer, oil and gas companies can protect their entire operation against current and future cyber threats.
nokia solution
To take full advantage of the power of AI and digitization, oil and gas companies are turning to networks that can offer lightning-fast speeds, far greater computing power, and the most robust security.
Nokia has solutions that meet these requirements, honed through years of experience in delivering future-proof, quantum-secure wide area networks using segment routing or IP/MPLS over DWDM technology. Its leadership has expanded to support AI and high performance computing within and across data centers and cloud locations. To extend robust, reliable, and low-latency connectivity for critical assets and applications in challenging industrial environments, Nokia provides private 4G and 5G wireless networks.
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