As the oil and gas industry embraces the advantages of generative AI and its implications for optimizations, the implications for natural gas demand increase are certainly being celebrated.

Data center power demand is soaring as AI rapidly expands across many applications in industry and natural gas can guarantee the 24×7 reliable power that data centers need. The industry is naturally enthusiastic about the insatiable nature of AI’s appetite for electricity as much as it is for the incredible potential AI offers for value creation and to increase productivity in their operations.

The rapid increase in adoption of generative AI, powered by the success of ChatGPT (launched in November 2022) and other AI chatbots that have arrived such as Copilot, has led to a reality that data center operation is one of the fastest-growing industries worldwide as reported by EPRI Research. There has been an equally rapid rise of AI applications for the energy/oil and gas industry and begging the question of why the industry is embracing AI so strongly.29dk2902l

Forbes reported last year that faced with cyclical volatility and the impact of economic downturns, oil and gas producers were increasingly looking to AI to facilitate operational predictability as well for assistance in meeting their carbon emissions targets.  The report stated that  “All of the top 20 global oil and gas producers, be they state-owned entities or public-listed ones, have a clear AI strategy for their upstream, downstream and, where applicable, midstream businesses… technology-industrial software vendors, as well as major oilfield services companies (e.g. Baker Hughes and Halliburton) that routinely offer AI solutions for more efficient technology-enabled operations.”

Enter Drishya.AI a Calgary-based deep-tech company using AI-driven, smart digital twin technology for brownfield and greenfield facility digitalization and decarbonization across Energy, Oil and Gas, Utilities and other process-oriented industries.

Most digital twin companies like Drishya have identified a specific energy transition problem -with a huge installed base of oil and gas infrastructure that predates the digital era, companies have data about those assets held largely in near-paper formats -JPGs and PDFs of engineering diagrams. Drishya worked to develop an AI-assisted cloud solution to convert engineering diagrams and documentation into actionable data and a “computable” facility. Net Zero plans for decarbonization demand an extremely fast level of mechanization across the industry and in order to make the required changes, companies need high-quality structural data to better understand their operations and future direction.

I spoke with Paul Blaha, VP of Market Development, Drishya AI about their technology using AI to read engineering and facility operations documentation.

“That’s what we’ve been working toward and are achieving – a smart digital facility which can connect to many corporate functions,” Blaha said. “ You can use this smart digital twin to make more efficient diverse work processes ranging from purchasing optimization, facility retrofitting, process simulations, and material take-offs (MTOs) for maintenance planning and work orders. The benefits are various owner functional groups can share and access accurate asset information and be much more productive with assistive AI leading to material cost decreases.

According to Paul Blaha, what often happens in a review of a company’s total facility inventory and it’s asset hierarchy is that they are often incorrect, yet are connected to important ERP functions. The parent-child relationships are often inconsistent and this can limit business work process effectiveness. AI can be used to identify all the anomalies going back to wide sets of engineering documentation.

“In the industry, when you show up and look at your operating facilities – your “as found” is often different than your “as designed”, Blaha said. “Your design can be much different than the operating facility on site. This happens for nearly all global facilities more than 15 plus years old. So now owners can get that current view more efficiently and better manage the asset change into the future having its own reliable single source of truth.  ”

An important AI use case for Drishya is process simulations resulting from its contextualized digital twin.   In an industry with huge quantities of operational data AI algorithms can streamline further development of economic use cases. They are particularly well suited to assets with a high level of complexity- like refineries or natural gas processing facilities.

“Why are refineries complicated?” Blaha was asked. “They have a very large number of inputs and a tremendous number of outputs.  Most efficient operation can depend on the economics of the products they are producing, changes in feedstocks or capacity limitations of key unit operations. Process simulations are required to optimize the short term or long term facility configuration and operations.  We have recently worked closely with super majors and leading software providers on applying our smart digital twins and AI technology to process simulations.  This can lead to an 8-to-10 fold reduction in set up time for process simulations and presents an opportunity to scale across oil and gas facility fleets.

In a recent presentation on digital twins at the Digitalization in Oil and Gas-Canada conference, David Lod Chief Executive Officer of VEERUM stressed that visualizing assets in 3D fostered better understanding and intuitive collaboration,

“This is the power of visualization in industrial operations,” Lod said. “An MIT study notes that 90% of information transmitted to the brain is visual and visuals are processed 60,000 times faster than text. Just by looking at a visual, you will retain that knowledge 400% better. So when we look at our oil and gas industries, we have to stop providing our employee base with manuals and spreadsheets and text and ways to operate facilities in that old antiquated way. We need to start to present that data in a visual capacity.”

Lod says visual data can help companies realize real productivity improvements. He asserts the oil and gas industry is basically technology agnostic, with a focus on getting molecules out of the ground for less money than we do today -cheaper, faster, and safer.

“We have companies that are seeing over 33% improvement in productivity. Just looking at payment planning as an example. A company used to log into eight different systems to create a maintenance package. And then they’d have to send someone out to do a field site to validate site conditions. So we’re talking about eight hours of effort plus a site visit. We now have that same client that’s only logging into an application for 20 minutes of work, using fewer systems and no site visit required.“

Digital Twins and the visualization they facilitate can create a real reduction in field exposure hours. The work can be done remotely, reducing the need to validate site conditions.

As companies look to implement AI, the creation of a detailed digital roadmap is key to the successful adoption of the AI tools they choose to ensure they select the tools that are linked to use cases, skills and data.

Lod says ROI is extremely important and new technology has to have time to value meeting in the next 30 days after implementation which digital twins can achieve. Another advantage digital twins offer is the connection to real-time operating data – linking with a SCADA system and knowing the temperature, pressure, and humidity of a particular facility, This live connection is put in the hands of employees to make decisions integrated with all the existing systems, whether it’s SAP or Maximo or whatever that might be in a company’s engineering models, democratizing it and making it simple and accessible.

Added to this, an interesting new AI breakthrough is developing. There have been fears of the impact of the dramatically increasing energy demands related to AI use which could pose a significant challenge to decarbonization goals and also impact energy security. The tech sector is already finding solutions for more efficient computing methods. As reported in oilprice.com, researchers may have discovered a new integer-addition algorithm that could reduce AI’s energy footprint by as much as 95 percent.

This is cause for optimism for even more positive AI developments in 2025.

Maureen McCall is an energy professional who writes on issues affecting the energy industry.

As published in the BOE Report titled "Soaring AI implementations and Next-Gen AI tools for the Energy industry".