Digital data management helps national oil companies improve operations
National oil companies (NOCs) face a host of unique challenges compared to their private counterparts. NOCs, owned by national governments and their populace, fuel a tremendous source of civic pride, while also making major contributions to the national economy.
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NOCs can significantly optimie their operations with more data, which can be very effective for asset management and enhancing maintenance practices. However, it’s not just enough to collect data you need to make sense of it.
With a robust industrial operations data platform, NOCs can rely on the structure, context, and reliability of their data to produce actionable insights and improved maintenance outcomes.
A change in strategy
NOCs often collect data from a patchwork of sources, such as multiple SCADA systems, but have no real- time data infrastructure in place to proactively address maintenance. Without a data infrastructure, maintenance is either reactive, when something stops working, or calendar/runtime-based, like a scheduled replacement every quarter, regardless of the condition of the parts.
This traditional strategy is labour-intensive and inefficient. Waiting for equipment to break can inflate the cost of repairs and possibly create a massive replacement expense. Downtime can wreak havoc on production schedules and create an even larger financial loss. On the other hand, performing maintenance before it is necessary, just because the calendar says it’s time, is a waste of both physical and human resources.
With a robust data infrastructure, NOCs can develop the insight needed to implement condition-based maintenance (CBM), and understand the actual health of each asset. CBM avoids unnecessary work and the risk of a failure slowing production.
Implementing a CBM-based system requires collecting large amounts of raw data, but gathering data is just the first step to generating deep insights. The reality is that it’s nearly impossible to integrate sophisticated tools and platforms without the proper structure and context for the data. A management strategy that integrates data into a proper framework is necessary to maximie the benefits of a digital transformation.
A robust operational data management infrastructure facilitates a CBM program, and is a first step to incorporating predictive analytics to deliver additional value across the enterprise.
An industrial operations data platform that provides a single source of truth also simplifies the routine processes of running applications, generating reports, and analysing performance. The less time your analytics team spends tracking down the data they need for a report, the more time they can spend uncovering insights that lead to optimiation and efficiency.
Case study – PETRONAS Carigali
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, integrating equipment from multiple manufactures can prove to be a challenge without an industrial operations data platform, because different companies process data in 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 SystemTM, the leading industrial operations data platform, to collect the data and provide notifications, and PI VisionTM to display the results. Within two months, PETRONAS knew it was on the right track. The automatic email Notification system successfully alerted the team when issues arose. The dashboards 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 complex algorithms in tandem with the PI System to make the infrastructure more intuitive and predictive. It is also designing a fault tree based on previous data so that repair crews can investigate, diagnose, and fix problems as they receive alerts. Additionally, PETRONAS is using the PROTEAN system to move away from scheduled maintenance and toward condition-based maintenance, using real-time data to alert engineers to any status changes.
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 recommendation for NOCs is to start with an operational data platform to achieve a comprehensive maintenance strategy. This will help stretch assets, save money, avoid catastrophic failures, and improve uptime. Then, build upon that foundation by optimiing production further using those same tools. Once that level is reached, it is possible to generate even more value with advanced analytics.
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