“If only I had known then what I know now” is a phrase most of us have uttered at one time or another in our lives. Using it in reference to problems with oil and gas assets, however, is a very expensive regret. 

The typical drilling platform, refinery or other processing facility is composed of thousands of complex moving parts. Add in the fact that some of those parts may be hidden under the sea or underground and it’s easy to see why ensuring the proper operation of every critical component has always been a daunting challenge no matter how large the on-site workforce. 

In today’s environment, it can be even more challenging. The need to reduce costs in order to survive and protect against huge market swings means not just fewer bodies to react but also a concentration of institutional knowledge among fewer people. As those people retire, they take that knowledge with them, making it even harder to keep complex mechanisms running at peak efficiency.  

This is where “digital twins” can prevent an irreplaceable knowledge drain. These 3D representations of systems and physical components show the inner workings and monitor the performance of every major component throughout a facility. 

Where they differ from previous 3D models is that they are not simply representations of component locations or static guideposts for process flow; they are integrated into applications, data, and documentation making information accessible and relevant.  When combined with Internet of Things (IoT) sensors with facility components, digital twins monitor the status of assets 24/7 in real time. That means producers need fewer workers permanently on site and can reduce the number of outside visits required to assure assets continue to operate optimally. However, this is still a basic, reactive use of digital twins. 

There is another option that ends costly disruptions.

Becoming proactive through artificial intelligence

By incorporating artificial intelligence (AI) into their digital twins, producers can proactively manage individual components and facilities or the entirety of their assets. Aggregating the data from all of the IoT sensors and comparing it to historical data from all of the organization’s assets enables AI technology to identify patterns that indicate an issue may surface soon. 

In parallel, false positives can be reduced by recognizing that a reading outside of stated parameters always occurs when other events happen upstream. 

Take the case of a sudden pressure rise that quickly approaches safe operating limits. While this may ordinarily be a cause for concern, the AI identifies the same rise occurs in conjunction with other operations and then returns to normal. In this case, there are two possibilities: first; that the producer may not have been aware of this combination and potential risks; and second, that if it is a byproduct of normal operations, no alert may need to be generated unless it exceeds normal patterns.

The proactive nature of AI in digital twins becomes even more valuable when integrated with predictive and prescriptive analytics. Predictive analytics help facilities stay ahead of maintenance needs and deploy their limited resources to issues that could affect production capacity or even shut down an asset entirely. With prescriptive analytics, producers can use their AI and digital twins to run “what-if?” scenarios to pinpoint which option out of many yields the greatest benefits at the lowest cost and level of disruption. 

Following are some of the ways progressive oil and gas producers are taking advantage of digital twins:

  • Gaining information on the real-time health of equipment. A single pump failure in an offshore rig can cost producers anywhere from $100,000 to $300,000 per day due to lost production. With data from IoT sensors, engineers can eliminate hours of troubleshooting, directing on-site crews to the source of issues in minutes so they can quickly bring the platform back up to full productivity. 
  • Detecting faults and predicting remaining useful life of assets. By running the massive amounts of data generated by gas processing units with IoT sensors through deep learning and AI technology, digital twins can help producers get ahead of many issues. They can also provide guidance on the parts and skills that will be needed to service problematic assets as well as the work field service teams will be required to perform. 
  • Reducing non-productive drilling time. Using real-time data gathered on-location as drilling occurs, along with edge analytics that compare it to the producer’s historical data and outcomes in similar projects, organizations can make better and faster decisions that improve overall drilling performance while keeping drilling productivity high. 

Start with the business problems

The first step when considering whether to implement digital twins technology is to look at the organization’s business problems and consider all the options available to solve them. The only reason to acquire and implement this (or any) technology is if it answers specific needs and delivers the highest value to the organization. Anything else will eventually become one more application gathering virtual dust.

If it is the right fit, the next step is to look across the organization to ensure it is ready for this type of transformational change. Digital twins’ greatest value is derived when delivered on an enterprise basis rather than taking a “whack-a-mole” approach to modernization. 

The good news is that when applied at scale, producers can expect the value of digital twins to double every year the original cost of implementation. The result is an outstanding ROI today while also preparing producers for a more productive and efficient future. 

Good time to start

If implementing digital twins in your organization is the right choice, this is an ideal time to get started. Slower production means it is easier to deploy without significantly disrupting schedules and employees probably have more time to dedicate to digital twin projects and process and workflow optimization. Then when production ramps up again, the producer will be better prepared to succeed. 

Most of our regrets in life come from either not having enough information to make a good decision at the time or not understanding what the real consequences of our decisions will be. For oil and gas producers, digital twins offer an opportunity to avoid both. By providing a deeper understanding of the thousands of complex components and processes that make up a modern asset, and how they interact with one another, digital twins help producers meet the business challenges they face today–and prepare for those they will face in the future.

About the Author:

Daniel Sawyer is a Principal Consultant at CTG (NASDAQ: CTG). With more than 20 years of experience delivering information technology in logistics and oil field support services, he has been a key consultant on 3D visualization and Digital Twin initiatives for major oil companies in Alaska since 2013. Sawyer’s expertise is in deploying cost-efficient technology with a focus on user workflow to increase business productivity.