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The secret to unlocking efficiency for the energy transition? One exec says data governance

Nick Purday, IT director of emerging digital technology for ConocoPhillips, presented at the Reuters Events Data-Driven Oil and Gas Conference 2023 to help dispel any myths about digital twins. Photo courtesy of Shuttershock.

As Nick Purday, IT director of emerging digital technology for ConocoPhillips, began his presentation at the Reuters Events Data-Driven Oil and Gas Conference 2023 in Houston yesterday, he lamented at missing the opportunity to dispel any myths about digital twins given his second-to-last time slot of the conference.

He may have sold himself short.

No less than a hush fell over the crowd as Purday described one of the more challenging applications of digital twins his team tackled late last year. Purday explained, “The large diagram [up there], that’s two trains from our LNG facility. How long did that take to build? We built that one in a month.”

It’s been years since an upstream oil and gas audience has gasped, but Purday swept the crowd with admiration for the swift, arduous task undertaken by his team.

He then addressed the well-known balance of good/fast/cheap in a rare glimpse under the hood of project planning for such novel technology. “As soon as you move into remote visualization applications – think Alaska, think Norway – then you’re going to get a pretty good return on your investment. Think 3-to-1,” Purday explains. “As you would expect, those simulation digital twins, those are the ones where you get huge value. Optimizing the energy requirements of an LNG facility – huge value associated with that.

“Independently, Forrester did some work recently and came up with a 4-to-1 return, so that fits exactly with our data set,” Purday continued before casually bringing up the foundation for their successful effort.

“If you’ve got good data, then it doesn’t take that long and you can do these pretty effectively,” Purday stated plainly.

Another wave of awe rippled across the room.

In an earlier panel session, Nathan McMahan, data strategy chief at CoP, commented on the shared responsibility model for data in the industry. “When I talked to a lot of people across the organization, three common themes commonly filtered up: What’s the visibility, access, and trust of data?” McMahan observed.

Strong data governance stretches across the organization, but the Wells team, responsible for drilling and completions activity, stood out to McMahan with its approach to data governance.

“They had taken ownership of [the] data and partnered with business units across the globe to standardize best practices between some of the tools and data ingestion methods, even work with suppliers and contractors, [to demonstrate] our expectations for how we take data,” McMahan explained. “They even went a step further to bring an IT resource onto their floor and start to create roles of the owners and the stewards and the custodians of the data. They really laid that good foundation and built upon that with some of the outcomes they wanted to achieve with machine learning techniques and those sorts of things.“

The key, McMahan concluded, is making the “janitorial effort [of] cleaning up data sustainable… and fun.”

The sentiment of fun continued in Purday's late afternoon presentation as he explained how the application went viral upon sharing it with 1 or 2 testers, crashing the email of the lead developer responsible for managing the model as he was flooded with questions and kudos.

Digital twin applications significantly reduce the carbon footprint created by sending personnel to triage onsite concerns for LNG, upstream, and refining facilities in addition to streamlining processes and enabling tremendous savings. The application Purday described allowed his team to discover an issue previously only resolved by flying someone to a remote location where they would likely spend days testing and analyzing the area to diagnose the problem.

The digital twin found the issue in 10 minutes, and the on-site team resolved the problem within the day.

The LNG operations team now consistently starts their day with a bit of a spark, using the digital twin during morning meetings to help with planning and predictive maintenance.

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A View From HETI

Researchers have secured $3.3 million in funding to develop an AI-powered subsurface sensing system aimed at improving the safety and efficiency of underground power line installation. Photo via Getty Images

Researchers from the University of Houston — along with a Hawaiian company — have received $3.3 million in funding to explore artificial intelligence-backed subsurface sensing system for safe and efficient underground power line installation.

Houston's power lines are above ground, but studies show underground power is more reliable. Installing underground power lines is costly and disruptive, but the U.S. Department of Energy, in an effort to find a solution, has put $34 million into its new GOPHURRS program, which stands for Grid Overhaul with Proactive, High-speed Undergrounding for Reliability, Resilience, and Security. The funding has been distributed across 12 projects in 11 states.

“Modernizing our nation’s power grid is essential to building a clean energy future that lowers energy costs for working Americans and strengthens our national security,” U.S. Secretary of Energy Jennifer M. Granholm says in a DOE press release.

UH and Hawaii-based Oceanit are behind one of the funded projects, entitled “Artificial Intelligence and Unmanned Aerial Vehicle Real-Time Advanced Look-Ahead Subsurface Sensor.”

The researchers are looking a developing a subsurface sensing system for underground power line installation, potentially using machine learning, electromagnetic resistivity well logging, and drone technology to predict and sense obstacles to installation.

Jiefu Chen, associate professor of electrical and computer engineering at UH, is a key collaborator on the project, focused on electromagnetic antennas installed on UAV and HDD drilling string. He's working with Yueqin Huang, assistant professor of information science technology, who leads the geophysical signal processing and Xuqing Wu, associate professor of computer information systems, responsible for integrating machine learning.

“Advanced subsurface sensing and characterization technologies are essential for the undergrounding of power lines,” says Chen in the release. “This initiative can enhance the grid's resilience against natural hazards such as wildfires and hurricanes.”

“If proven successful, our proposed look-ahead subsurface sensing system could significantly reduce the costs of horizontal directional drilling for installing underground utilities,” Chen continues. “Promoting HDD offers environmental advantages over traditional trenching methods and enhances the power grid’s resilience.”

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