Use AI to pump up operational efficiency in Oil and Gas networks
When it comes to efficiency in the oil and gas industry, improvements are often measured in small increments. Remote and dangerous locations, the long distances that pipelines traverse, the sensitivity to external threats, these are all major points of risk that are not easily resolved.
Still, any margin of improvement in how oil and gas companies connect their vast territories of operation to HQ is welcome. And artificial intelligence (AI) has the potential to be a real game changer.
In my meetings with customers there is legitimate excitement that AI and big data–leveraging machine learning (ML), edge computing, cloud and the Industrial Internet of Things (IIoT)–can be true disruptors when it comes to making exploration, extraction, refining and marketing oil and gas noticeably more efficient.
Innovation by a global oil and gas company
The world’s largest oil and gas company has embraced AI and digitalization as central to its 21st century revolution. They recently conducted a Proof of Concept (PoC) with Nokia, in which we explored using AI capabilities for anomaly detection along pipelines in an application called fiber sensing.
The opportunity lies in the fiber already buried in the ground to transport data along the length of the pipeline. Optical fiber is quite remarkable, as the reflection of light through the fiber can be impacted by the simplest of things. Vibrations from rain, a truck crossing a road, tapping on or drilling near a pipeline, an earthquake—these intrusions are all able to be detected by Nokia’s DWDM multiplexer up to 140 kilometers away from a pipeline.
With pipeline safety and security such a significant focus for any oil and gas company, this kind of early intrusion detection can have a phenomenal impact on operations.
AI use cases in oil and gas
Many oil and gas companies are exploring opportunities to leverage AI. The vast troves of telemetry data transported between pipelines and HQ are at the heart of some promising new use cases. Nokia’s network automation solution, which manages both Optical and IP/MPLS infrastructure, plays a critical role in analysis of this telemetry data.
One use case is to combine AI with ML technology for predictive purposes. Using the available data, the 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 involve using deep analytical tools to monitor the Optical and IP/MPLS networks and make recommendations around how to optimize the network for performance and resiliency. The network automation system can also assess and recommend the optimal network set-up for precise timing across the network to ensure optimal time synchronization. Additionally, by reviewing the telemetry data, the network automation system can identify routers or switches that may be deviating from performance norms and recommend re-configuration options.
Running new technology test scenarios prior to actual deployment on a live network is another opportunity. Testing can be done entirely in-house to avoid external data center facilities, with scenarios fed into the AI system to determine impacts across the network. Digital twinning not only optimizes technology rollouts, but it can also help lower overall carbon emissions.
These kinds of use cases improve network performance, resiliency and operational efficiency, which become even more critical after this slew of next generation technologies is introduced into the mix. Expectations around performance, reliability and security are far greater with AI. Quality is crucial to success, since a glitch in an AI model can harm the validity of the results—and thus waste time and incur higher costs.
Robust security
With so much data transmitted over huge areas of operations, oil and gas companies remain vulnerable to cybercrime. Use cases that can enhance safeguards against attacks have grown even more attractive.
These use cases can be effective in protecting against modern cyberthreats. However, many oil and gas companies are now seeking solutions to address the coming quantum threat.
The cryptographically relevant quantum computer (CRQC)–which will be capable of breaking modern encryption codes–is not yet available. Already, though, we see bad actors engaging in a technique called Harvest Now Decrypt Later (HNDL).
A defense-in-depth protection system is recommended to safeguard 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 achieve operations-wide protection for modern and future cyberthreats.
Nokia solutions
To fully exploit the power of AI and digitalization, oil and gas companies are turning to networks that can deliver ultra-high speeds, far greater computing power and the most robust security.
Nokia has solutions across these requirements, honed over years of delivering future-proof, quantum-safe wide area networks using Segment Routing or IP/MPLS over DWDM technology. That leadership has expanded into support for AI and high-performance computing inside and across data center and cloud locations. To extend robust, reliable and low-latency connectivity of critical assets and applications in challenging industrial environments, Nokia offers private 4G and 5G wireless networks.
Visit this link as well as the related blogs below to learn more about how Nokia can help you gain even greater efficiencies in your oil and gas network operations.
