Green and lean: Data driven asset optimisation for transmission pipelines

Rising energy prices induce increased operational costs. The energy industry faces immense pressure to lower OPEX and participate in global efforts to decarbonise. Pipeline operations, the backbone of the global energy landscape, are no exception. With the rise of large-language models like ChatGPT, artificial intelligence (AI) is the tool on everyone’s minds. Embracing big data analytics and AI offers exciting opportunities for pipeline operators to unlock hidden insights for greater efficiency and reduced emissions.

AI relies on and benefits from large datasets for pattern recognition and predictive modelling. Pipeline operators already harvest vast amounts of data, such as flow and pressure, so they must exploit this resource. The use of big data analytics, in combination with AI algorithms, allows operators to harness the power of their available data for a more comprehensive understanding of pipeline behaviour and optimised operations.

Intelligent pipelines: a harmony of AI and human analytics

In the world of AI-driven asset optimisation, pipeline operators face a choice between three approaches: adopting classical analytical methods; pure artificial intelligence (AI) models; or a hybrid approach.

The more traditional analytical methods can be alluring thanks to their relatively straightforward implementation and predictable, deterministic outputs. They are also more reliable, especially when applied to older assets without historical data for AI training. However, these models struggle to account for the complexity and nuance of real-world situations, a weakness not shared by AI trained on real-world data. AI methods are also more flexible regarding pipeline instrumentation, allowing the integration of far more data sources. AI methods can even compensate for some lacking inputs a classical model might insist upon. AI’s ‘black box’ nature, however, typically necessitates subsequent investigation rather than immediate action and results. Despite this, AI is an obvious choice for complex analysis challenges (such as predictive maintenance). The small sacrifice to informational reliability and repeatability is tolerable given the prohibitive costs to manually evaluate assets. In many cases, a hybrid approach unites the best of both approaches.

There are many ways for operators to harness the power of AI and data analytics. One area that Klarian believes deserves particular attention is the optimisation of pipeline pumps and compressors. These unsung heroes play a pivotal role in the industry, and it is high time we explore how big data analytics can transform their efficiency and sustainability for the better.

Optimising pumps and compressors: pillars of the energy industry

Pumps and compressors are the mechanical workhorses of pipeline infrastructure, ensuring a steady flow of hydrocarbons through the vast networks of large diameter transmission pipelines spanning the globe. The challenge operators face is that pumps and compressors, at scale, are energy intensive machines. The US Department of Energy’s Office of Industrial Technologies (OIT) reported that pumping systems account for 20 - 25% “of the energy usage in certain industrial plant operations.”1 High electricity usage generates high operating costs and carbon emissions. Over time, these increase with the everyday wear and tear of equipment, decreasing efficiency and causing unplanned downtime, further harming productivity and profitability.

We cannot overstate the importance of pumps and compressors, so optimising these assets should top the list of areas operators are looking to improve. The industry must harness new data-driven technologies to take care of and improve the use of these vital assets.

Systems-level modelling for optimisation (routes, products, VSDs and more)

The performance of pumps and compressors depends highly upon…

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Read the article online at: https://www.worldpipelines.com/special-reports/12102023/green-and-lean-data-driven-asset-optimisation-for-transmission-pipelines/