Digital transformation in LNG requires a long-term, data-driven approach

The energy transition has begun, and natural gas, especially in its liquefied form, promises to play a key role in enabling the shift to a greener and cleaner global power picture. The potential is there, but the LNG industry finds itself facing continually mounting pressure from all fronts—the foremost being the rise in production levels while prices continue to fall, putting companies in a cost crunch at a time when investment is sorely needed.

The solution to this conundrum lies within the LNG industry’s ability to leverage three concepts: efficiency, profitability and sustainability.

The LNG players who have managed to tackle these challenges are the ones who have succeeded with a longer-term digital roadmap and an investment in a data operations (DataOps) platform on which to build their digital solutions. They have understood the underlying power of connecting data from across their operations and putting it to work to optimize decision-making, rather than driving one-off digital projects intermittently.

There are three commonalities seen among the digital leaders in the LNG industry. The first is that these frontrunners have stopped seeing their data as something in silos and, rather, have made concerted efforts to bring data together into one, bigger, connected picture. Second, they have used that data to gain real-time situational awareness about their operation. Lastly, they can act on that awareness to drive better, more sustainable and more cost-effective decision-making. The following will show these aspects in practice.

Break down the data silos across your LNG operation. This is an industry with a large and diverse set of operational data, production data, meta data and financial data. In a digital transformation, all this data must be brought together and made accessible from a centralized place. This could be a data platform or a digital twin, but, most importantly, this transformation is about combining the data from multiple sources so it is made meaningful and can be accessed by anyone from across the operation or even the entire value chain.

For example, monoethylene glycol (MEG) is injected into pipelines for thermodynamic hydrate prevention. Without knowing the minimum concentration for the current production rate, MEG is often either underdosed or overdosed, introducing risk of hydrate formation. The information needed to predict hydrate formation and to estimate the required MEG concentration, given operational conditions, is spread across production, sensors, piping and instrumentation diagrams and well data sources, as well as in pipeline layouts, height profiles and laboratory data. It is not until this data is consolidated and made available for easy use that it can be leveraged to estimate the current MEG concentration and to compute a recommended maximum pipeline flowrate.

Put the data to work to gain real-time situational awareness. According to a McKinsey & Company industry report on the digital era of the autonomous plant,¹ a carefully determined combination of conventional technologies, artificial intelligence (AI), ubiquitous data, connectivity and collaboration can work in concert to consider the future state of refineries or petrochemical plants. While data is at the core of the digital plant, it must be part of a bigger strategy of digital solutions, working in tandem, to extract its full potential. Data has no real value until it is put in context, which requires layers of analytics to give it meaning.

Success will depend on fast and accurate information, in context, from across the LNG value chain. For example, sensor data—when contextualized—can help forecast potential incidents before they happen, enabling the operators to take decisive action before an incident occurs. Connecting data provides a gateway to the world of predictive maintenance, optimized uptime, reduced downtime, and a significant reduction in safety and environmental risk. In an environment with increasing regulations and greater transparency requirements, this window into the live operation can reveal where there is excessive waste, where efficiencies can be gained and what steps can be taken to optimize production even further.

The author’s company has had conversations with Lundin Energy about what digitalization has done for its gas business. The following is how Lundin is working with the author’s company to reduce its carbon emissions: “Trouble in the gas chain means you may have to burn the gas, which is not good for the environment,” said Stig Pettersen, Principal Engineer Automation, Lundin Energy. “But we are looking into the data, using the dashboard for energy losses and carbon dioxide (CO₂) releases. Then, we can follow up, using key performance indicators. Now, there is even competition between the different shifts to release as little CO₂ as possible!”

Make smarter decisions that help you get closer to fully optimized production. There is no doubt that LNG companies want to lower their costs and operate more efficiently and sustainably. They also want to have the competence and capacity to invest in new technologies, and to ensure that those technologies are not wasted in cases of one-off use.

McKinsey & Company refers to digitally transformed refineries as “more profitable, safer, more reliable, more energy efficient, a more attractive workplace, able to leverage AI-enabled solutions and more sustainable.” Automation is the ultimate target on a digital roadmap, which is when multiple digital solutions are deployed and connected across the operation, enabling asset-wide digital twins and optimizations
across units.

For example, Wintershall Dea wanted to create a holistic overview of maintenance work to better analyze larger trends, such as the frequency of issues like corrosion, how often a particular component breaks down and how much the company is spending on corrective vs. preventive maintenance. By consolidating all its maintenance management data in the author’s company’s proprietary DataOps platform^a, Wintershall Dea was able to aggregate factors that cause equipment failure with reliability metrics and detailed cost breakdowns. The result is that the company can more easily draw conclusions about the reliability of its assets, improving its maintenance routines and optimizing production, thus saving Wintershall Dea’s experts an estimated 10 hr/wk. While this example is more specific to natural gas and oil production, the same concept of predictive maintenance can easily be applied to LNG facilities.

A promising forecast for the LNG industry. It is expected that LNG will continue to grow in demand as clean energy sources become increasingly dominant. In anticipation of future LNG demand, more plants are being built and more investment is being made.

It is a challenging time to be an LNG operator. There is no shortage of new technologies and new tools to enable smarter and faster processes, but the real value is when you bring the software, devices and data together. Imagine an LNG environment in which downtime is nearly nonexistent, in which human error is nearly eliminated, in which safety routines and measures are more accurate and effective, and where operators have all the information they need to make better business decisions.

LNG players who think about digitalization in the long term and across the spectrum of assets and operations will come out the winners. Fully liberated and contextualized data is vital, and those who use it to their advantage will be well-positioned when the demand strikes. GP

NOTE

^a Cognite’s Data Fusion™ platform

LITERATURE CITED

1. Chakrabarti, G., D. Don, M. Smith and P. Vora, “The autonomous plant: Entering a new digital era,” McKinsey & Company, September 2021, online: https://www.mckinsey.com/industries/oil-and-gas/our-insights/the-autonomous-plant-entering-a-new-digital-era