Applying IIoT and AI to midstream asset management
Asset management, intelligent workflows and automation play critical role in operational efficiency
Even as oil prices around the world have improved alongside the reopening economy, operators of liquefaction terminals, gas pipelines and organizations engaged in other midstream activities face challenges. In the face of uncertainty about the cost of oil in the future, companies have no choice but to continue to seek operational efficiency. At the same time, they must advance decarbonization, meet greater safety imperatives and apply learnings from the pandemic.
One study from IDC estimates that the benefits of smarter, more digitized asset management enabled by machine learning and AI alone can reduce an organization’s total costs by up to 20%, improve asset availability by 20% and extend the lives of machines by years. These savings can free up resources to invest in profit-seeking opportunities while also improving productivity and reducing downtime. Critically, they also can help advance sustainability goals.
For pipeline operators, the benefits are particularly critical in the prevention of leaks, which is why companies such as Bridger Pipeline are deploying artificial intelligence (AI) solutions that use deep-learning techniques to reduce false alarms and detect legitimate leaks more rapidly and efficiently.
Of course, predictive maintenance requires investments of its own. Users need sensors, beacons and even drones to monitor assets and collect data, as well as 5G-enabled connectivity to power workflows in the field and at the edge. Users need advanced AI models capable of analyzing that data against asset histories to make holistic determinations about asset health and what is likely to break down in the future. Also needed is AI capable of helping organizations decide where to deploy technicians and, once technicians are in the field, help them make inspections and repairs more quickly and efficiently. Critically, users need a hybrid cloud based digital infrastructure capable of bringing all these pieces together while maintaining cybersecurity and resilience in the face of disruptions or threats.
The democratization of AI
Fortunately, these technologies are all advanced and in robust deployment today. They are also becoming more accessible, part of a movement toward the greater democratization of AI. Only a few years ago, using AI required technological expertise and domain expertise. Users needed to understand how AI models worked and how oil refineries worked. Today, breakthroughs in computer vision and machine learning are making it much easier to train and deploy advanced AI with significantly less training and resources. This is giving midstream operators of all sizes access to the immense potential of advanced technologies like AI.
AI is also going mobile, with significant ramifications for technicians and the assets they maintain. Thanks to hybrid cloud, companies can gather, reconcile and display data anywhere, including in the field. They can run software, too, including advanced AI models even without a Wi-Fi signal. This gives them the ability to take advantage of AI that helps them do their jobs more quickly and safely, for example assisted repairs, advanced parts recognition and optimized scheduling. Technicians can access these capabilities using applications with simple interfaces run on common smartphones, and even take advantage of augmented reality to access further assistance when needed.
Tapping advanced technologies requires upfront investment. But benefits outweigh the costs and more frequent industry disruptions, and the push toward more sustainable operations, make digitalization an imperative. Fortunately, due to the valuable assets that midstream energy companies must maintain, the benefits of AI are quantifiable. By tapping the data an organization produces and putting it into use through AI, companies make organizations more efficient, sustainable and profitable.
Original content can be found at Oil and Gas Engineering.