Building digital ecosystems

Despite oil demand recovering in the wake of COVID-19, McKinsey’s recent Global Energy Outlook Report projects a steady decline from 2025 until 2050. With geopolitical pressures compounding oil and gas prices, operators need to be smart about how they generate peak productivity before the industry begins its anticipated contraction.

Digitalisation is identified by the World Economic Forum as a US$1 trillion opportunity for the oil and gas sector, with data analytics helping operators avoid costly disruptions, and vastly reduce downtime. However, realising the benefits of digitalisation remains a significant challenge for the industry.

One considerable barrier is that current strategies for optimisation focus on discipline areas rather than assets as part of a wider portfolio. This traditionally siloed approach stifles the level of information sharing necessary for operators to access this jackpot.

Another notable hurdle is that operators use a plethora of niche software to gather and analyse data on each asset, often looking at a single challenge at a time. In most cases, these technologies have limited or no means of interacting and sharing data with each other, meaning decisions are often made reactively in response to isolated conditions or challenges. Only a small percentage of data collected is currently driving decision-making, demonstrating the gap that must be bridged.

This is despite the fact that the infrastructure to support portfolio-wide data sharing is already available. McKinsey reports that over two-thirds of the world’s oil production has access to advanced connectivity, such as fibre optic cables, however stakeholders make limited use of it.

Even when infrastructure is set up to allow data to be shared easily across an organisation, asset managers are rarely incentivised to consider how optimisation solutions can work beyond assets they are directly responsible for. Not to mention that they have tough production targets to meet and mandates to reduce spend within their specific remit.

While some may argue the isolated, case-by-case approach has served the industry adequately to date, there is no doubt that there is now a need for a paradigm shift from reactive to predictive management. In a new age of big data and technological advancements, the industry’s legacy approach is no longer sustainable, scalable, or smart.

To capitalise on the digitalisation opportunity, the oil and gas industry needs to take three key steps:

• Liberate its data – making it available across disciplines and asset boundaries.
• Generate connected insights – using a digital platform that can combine and analyse this data using artificial intelligence (AI) and machine learning (ML) to transform them into actionable insights.
• Monetise these insights – by using them to optimise assets and improve efficiency, paving the way for the advantages of future digital twinning and automation technologies.

Within this article, we will look at the key challenges digitalisation can solve for operators, what technology is needed now, and in the future, and detail the steps the industry can take to begin this critical organisational transformation.

What key challenges can digitalisation resolve for operators?

The industry is striving to replace its traditional ‘if it ain’t broke, don’t fix it’ corrective maintenance strategy with preventative methods. Digitalisation is the driving force behind this transformation, providing the oil and gas sector with game-changing foresight and giving it the capability to prevent and/or mitigate operational issues and failures.

It also plays a major role in enabling remote operations at times when moving technical expertise around the globe is a challenge; a problem that came to the fore during the COVID-19 pandemic and continues now with geopolitical crises, such as Russia’s war with Ukraine.

As well as these macro challenges, digitalisation can help the industry address persistent pain points across the value chain. Pipeline infrastructure failures, for instance, can have significant consequences for production performance and economic output, but maintenance comes with high costs and complex planning, compounded by issues of safety and ease of access.

Existing pipeline data acquisition methods are only able to send real-time monitoring data to a control room at a specific site. This prevents information from being shared between sites, and hampers operators’ abilities to monitor integrity changes across the portfolio and take preventative action to preserve productivity.

Operators can resolve this challenge with a digital strategy that enables data sources and processing sites to interact and share information. This approach can make it possible for operators to effectively monitor integrity risks in a connected way, simplify planning procedures, and better control the costs of essential maintenance across their full portfolio of assets.

What technology is needed?

To realise digital transformation benefits, an end-to-end digital ecosystem is needed to organise and analyse data from across the enterprise, all in one place.

There are numerous Industrial Internet of Things (IIoT) analysis platforms which translate information into insights, but not all of these have the capabilities necessary to deliver the three key steps outlined above.

The most important platform functionalities to realise a centralised digitalisation strategy, and deliver portfolio-wide optimisation and enterprise-wide value, are the ability to:

• Combine the power of edge computing with cloud-based solutions.
• Accommodate all monitoring and maintenance requirements within a single end-to-end solution.
• Centralise, process, and analyse data captured from the entire asset portfolio using data from all available sensors.
• Scale and adapt to new use cases as challenges evolve.

Edge computing and cloud-based solutions

One of the biggest difficulties an IIoT platform needs to solve is the big data challenge. Assets are drowning in data that can’t effectively be transported to the cloud, rendering it useless when it comes to combining it with other data streams and informing organisation-wide insights.

A unique, hybrid solution is needed – one that combines the power of edge computing and cloud-based solutions. Smart edge computing technology can manage data onsite and extract and push key features to the cloud, turning terabytes of data into kilobytes of actionable insights in a matter of seconds. Cloud-based technology is important in facilitating fast processing, providing easy access to data and ensuring solutions are scalable.

End-to-end solution

Individual operational challenges have not gone away. Asset managers still need solutions to monitor reservoir performance and targeted water injections, and manage well integrity and production system erosion risks, for example. Any analysis platform needs to have solutions for these common operational challenges, as well as the flexibility to adapt these solutions to meet the specific needs of an individual asset. When insights are drawn from a wider pool of performance data, these challenges can be addressed even more comprehensively.

Centralise, process, and analyse

The most advanced IIoT solutions have highly developed data processing capabilities, powered by AI and ML models, which enable disparate data streams to be merged into an orderly flow. This technology makes the centralisation of an organisation’s data and insights possible, and can serve as the backbone for any digital strategy by bringing all data into one place.

Meanwhile, multiple sensor data types such as acoustic, vibration, temperature, and strain, can be combined in real-time through sensor fusion to provide the most accurate production performance picture. The enhanced insights, made possible by combining multiple data sets, can unlock new ways to answer crucial operations questions, making this capability mission-critical for large oil and gas organisations.

While some IIoT platform providers can only incorporate simple sensor types, real value can be unlocked by extracting data from complex sensors, such as Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS). The combination of sensor fusion and advanced analytics allows the leading IIoT platforms to work with desynchronised datasets, and facilitate the full centralisation of insights.

Scale and adapt

ML models are inherently agile and flexible, undergoing continuous cycles of improvement and refinement as they interact with new data and use cases. This means IIoT platforms supported by ML can learn from, adapt to, and resolve new challenges as they arise, making the entire digital ecosystem scalable to emerging trends and requirements.

What does a digitised future look like? What comes next? Building a digital ecosystem around a sensor fusion and AI/ML-enabled IIoT platform now will help operators future-proof against emerging challenges. A connected digital strategy paves the way for the evolution of its own models and processes, and integration with additional digital tools that can elevate operational understanding and optimisation.

Co-creation

At present, IIoT platforms can capture and assess data and present insights across a vast array of use cases, providing an effective ‘catch-all’ solution for operators starting out in enterprise digitalisation. In future, collaborating IIoT platform providers and operators will be able to develop and deploy bespoke analysis models in-platform, tailored to specific portfolios, challenges, and objectives. This ‘build your own model’ or ‘co-creation’ approach will drive the generation of even more targeted insights, providing the precise information operators need to assess and improve fleet-wide productivity.

Dynamic prediction and twinning

Meanwhile, real-time insights generated can feed into emerging digital tools, like dynamic prediction models and digital twinning initiatives. Digital twins can increase predictability, product quality, and produce time and cost savings by using real-time data insights to simulate changes in digitally constructed assets. Similarly, dynamic prediction models, which depend on existing data to predict future issues, can be made more accurate when they receive real-time insights across the entire portfolio, allowing operators to mitigate issues before they occur. By feeding insights into these tools, operators can reliably understand how assets are performing now, and how they will perform in the future, across the enterprise.

Automation

This interconnectivity lays the foundations for one of the ultimate digitalisation ambitions – automated asset optimisation. References to AI and robotics have spiked considerably in annual reports from oil and gas organisations since 2016, with productivity benefits ranging from improved safety to faster maintenance timelines and error reduction. The industry can prepare itself for an automated future by establishing a robust digital ecosystem and centralised digital strategy today, which can scale up to accommodate new data applications.

So where do we start?

The case for operators to accelerate digital transformation to achieve portfolio-wide optimisation is evident. Improved certainty over operational decisions, reduced maintenance costs and timelines, increased efficiencies and production, and the evolution of predictive maintenance all warrant the consolidation of isolated data solutions into a unified approach. But it can feel like a formidable project.

Oil and gas organisations can start by taking these three actions that will enable this journey:

• Analyse the coverage of current digital infrastructure and connectivity.
• Consolidate software applications into a single, capable provider – recapturing significant value at the same time.
• Find and collaborate with the right digital partner who can support you throughout the digitalisation journey and accommodate your unique and evolving use cases.

These actions will empower operators to break down silos, build organisational ‘digital blueprints’ that use real-time data and insights to create a comprehensive operational picture across their assets, and work hand-in-hand with a partner that can facilitate a connected and digitised future.

This future is in reach and now is the time to maximise the potential of IIoT.

^{_{References available upon request.}}

Read the article online at: https://www.worldpipelines.com/special-reports/06032023/building-digital-ecosystems/