Industry4.0: Oil&Gas & the Digital Twin


In the upstream, each new Exploration & Production asset comes with numerous components and equipment from various suppliers. Once built, the operator has a good view of: 

  • how the asset has been built
  • a system of how it will operate
  • an ERP system detailing what it'll cost to maintain, run & staff
  • a Maintenance program and team
  • and lastly a Finance system built for economic performance predictions

And then, there sits numerous layers of data and variables from Engineering content (diagrams, specs, configurations), to Physical constraints (operating capacities, throughputs, pressures) an overall of how it behaves, to Operating parameters (inputs energies, emissions), Financial costs ( to build and operate) and lastly Customer demand and weather patterns.

The challenge remains over how you keep them all in sync?

The True Digital Twin 

Today, as many jump aboard the Industry4.0 (r)evolution, we must seek out the true digital twin. Not the excel and 2D AutoCAD version, but instead the thorough simulation model of how the equipment and components combined actually work, both visually and statistically in variable conditions. 

In so doing, it reveals and demonstrates:

  • flawed assumptions (re-validated as over time, they may no longer be valid)
  • extracted and increased value, asset utilization or reveals higher costs
  • potential capital tradeoffs eg bottlenecks (additions, takeaways)
  • and even offers the ability to build a twin of your competitors in order to compete better

Twin Types

Over the lifecycle of the asset, there are three different types of digital twins, including the prototype, the instance and the aggregate. 

  • a digital twin prototype is a single digital version of something that exists before something is made (the creation phase)
  • while a digital twin instance is the paired digital version of an object connected with the physical object (the build phase)
  • a digital twin aggregate pairs multiple digital twins in existence, with all known configurations, to help remotely monitor equipment, predict how technology will react to future conditions it is placed in, and to learn how to make products better in the future. (the operational phase)

The concept of the digital twin has many possible use cases in the upstream oil and gas industry, from replicating a complicated well design to enable well productivity, to refining a well control system to sharply decreasing the risk of a blowout. 

Today, we continue that focus on the "DT-Aggregate"

'Shop-Floor' Benefits

The Aggregated-Digital Twin (ADT) is deployed to provide operational personnel with intelligence. The intelligence blends tremendous amounts of data, including statistical sciences, rules-based logic, AI, ML, and the digital twin phases, through which operations can discover and uncover the complex problems and arrive at options for resolving these. This move from descriptive to predictive to prescriptive analytics is where the industry will realize the real payback from the Digital Twin and associated advanced analytics.


The Digital Twin Challenges

The great potential of the DT comes with a number of hurdles to be overcome and not surprisingly revolves around the human factor, be that from Suppliers/OEMs and Operational Personnel:


The success of the ADT relies on the quality of the assets lifecycle data, through design, manufacture, operational performance and service data. This then asks the question as to who understands the ADT best?

Arguably, it is the OEM and therefore for an effective Digital Twin strategy, it relies on either transparency and sharing of OEM Asset Lifecycle Data and or the OEM to provide a service-based business to monitor and optimize the performance of both its digital and physical assets.

For manufacturers of long-lifecycle products, ranging from gas turbines to pumps, implementing an Aggregated Digital Twin service model for smart, connected products will improve service and support efficiency and enable a shift from reactive service to preventive, proactive, and remote service.

Digital Culture: OT/IT

Industry4.0 in Oil & Gas relies firmly on the convergence of IT and OT to connect the operational data with business processes for the end-to-end lifecycle asset and machine view. 

Today the merging of mindsets across OT/IT defines a successful Digital Culture 4.0:

The convergence has led to steep learning curves for both 'departments' and is leading to the trend of increasing demand for tighter integration and more information and advanced analytics. Furthermore, it contributes to the adoption of IoT, the digital twins, connected workers, advanced analytics, ML, and augmented reality applications, resulting in driving the need for high-availability systems to help eliminate unplanned downtime, built on a scalable, enterprise-wide platform.

If we can fix this challenge and bring the two together it means:

  • end-to-end process excellence, with vertical enterprise integration
  • visibility that leverages existing systems and the strengths of industrial products.

Getting it Right

When the industry gets it right across the value chain, it has the potential to impact Global GDP by almost 1%. Increased collaboration, operational mindset shifts and agility will make the difference in the company of the future.

While much of the 'new' technologies aren't new to the sector, many continue to struggle to manage and leverage the abundance of data and information. IT providers understand the systems and many OT providers have the deep subject matter experience and access to physical assets. These separate worlds must now converge.

Technologies such as the digital twin provide a window into both the problems and potential solutions. However, these technologies also require oil & gas companies to select resources that have expertise in the oil & gas industry applications, the physical assets and understand the importance of effective asset models.

Data alone is not the solution. It’s critical to also know how you use the data for intelligence, decision making, and modeling to affect the needed business change.

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