Decarbonisation Technology - February 2022 Issue

Digitally enabled decarbonisation Decarbonisingwith integrated engineering, asset performance, operational/ carbon datamanagement, and analytics in layers from the edge to the cloud

Craig Harclerode AVEVA

O il and gas companies will need to increase the use of data and innovative digital capabilities to achieve mandated decarbonisation targets, including layers of analytics from the edge to the cloud. Solid asset performance, operational/carbon data management with self-serve, real-time decision support are foundational to reaching these targets. While each company will have unique environmental, social, governance (ESG) and associated decarbonisation strategic areas – including but not limited to the common dimensions of accelerating traditional energy efficiency efforts, optimising value chains to minimise carbon generation, electrification and associated electrical infrastructure upgrade, grey, blue, or green hydrogen production, and modifying their business strategy to include new services – one enabler common to all is an integrated asset performance, operational/carbon data management with asset performance management. Solving tomorrow’s problems today Global leaders continue to embrace ever stricter carbon cutting measures, acknowledging at the COP26 summit in Glasgow that the world must cut emissions by 50% in the next nine years to avert a climate emergency (Aveva, 2021). In response to rising anxieties about climate change, the world has undertaken massive efforts on a number of fronts to decarbonise, including electrification and the increased use of hydrogen to reduce fossil fuel use; by 2050, experts expect renewable power to account for roughly 80% of global demand (McKinsey & Co, 2021). The oil and gas industry must redouble its efforts accordingly to stay relevant. To reach the lofty emissions goals leaders have set to meet social and political mandates, the world will have to rely on a combination of efficiency measures, renewable electricity The maintenance and engineering team at Malaysian oil and gas company PETRONAS Carigali is responsible for overseeing an upstream plant that includes 130 pieces of gas turbine-driven equipment, including compressors, generators, reciprocating engines, and pumps. When they began the company’s digital transformation, the team started with real-time asset monitoring. However, i tegrating equipme t from multiple manufactures can prove to be a challenge without an industrial oper tions data platform, because differe t companies process data i different ways. The team at PETRONAS began by developing a monitoring system for two critical gas turbine- driven compressor units as a trial. The team used the PI System TM , the leading industrial operations data platform, to collect the data and provide notifications, and PI Vision TM to display the results. Within t o months, PETRONAS knew it was on the right track. The automatic email Notification system successfully alerted the team when issues arose. The d shboards were clear and easy to read and delivered insights. As PETRONAS followed the path toward digital transformation, the team structured its data using Asset Framework, adding context and hierarchy to its assets. Over the course of two years, PETRONAS developed a proprietary solution based on the architecture of the PI System that would be known as PROTEAN (for PETRONAS Rotating Equipment Analysis). The company is developing more omplex algo ithms i tandem with th PI System to make the infrastructure more intuitive and predictive. It is also d signing a fault tree based on previ us data so that repair crews can investigate, diagnose, and fix probl ms s they receive alerts. Addi ionally, PETRONAS i using the PROTEAN system to move away from scheduled maintenance and toward condition-bas d maintenance, using real-time data to alert engineers to any status changes. Case study – PETRONAS Carigali

generation techniques, as well as green fuels like ethanol, hydrogen, renewable diesel, and biofuels. For green or low carbon fuel producers, adjusting to this shifting regulatory landscape – and the accompanying set of challenges it presents, such as novel, complex systems for carbon accounting – demands innovation, precision, and agility driven by quality operational and carbon data management and layers of analytics, including operational streaming analytics, events, and notifications with integrated AI-based insights. In short, fuel producers must use every tool at their disposal – particularly foundational enabling digital technologies – to solve tomorrow’s problems today. For decades, operational data and associated operational intelligence has been one of the most potent tools by which companies of all industries have optimised their processes, cut costs, and

Every NOC can benefit from an industrial operations data platform, given the clear value proposition. However, NOCs need to avoid the trap of reaching for Big Data applications without first having a strong data foundation. Digital twins, machine learning, and AI reside in a layer of advanced analytics that can generate significant value, but typically produce poor results if a robust analytical framework isn’t already in place to put the data in context. Our recomme dation for NOCs is to start with an operational data latform to achieve a comprehensive maint nance str tegy. This wi l help stre ch ssets, save money, avoid cata trophic failures, and improve uptime. Then, build upon that foundation by optimizing production further using th se same tools. Once that level is reached, it is possible to gen rate even more value with advanced analytics.

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