Data that drives change

NG producers see the same global market pressures as the rest of the world’s industries. The pandemic caused lockdowns of economies worldwide, energy demand crashed, gas prices plummeted, and LNG demand tanked. Emerging from the COVID-19 crisis, LNG supply fundamentals looked encouraging as markets, particularly in Asia, continued to expand. LNG demand increased when the Russian invasion of Ukraine created energy shortages, causing soaring oil and gas prices. Given such a backdrop, LNG futures look attractive. Nevertheless, the industry cannot escape the technology forces shaping other manufacturing production industries.

The drivers of the undercurrents that today affect LNG and other industries began 11 August 2011, when Mark Andreesen, Founder of Netscape and Grandfather of the Internet Browser, published an article in the Wall Street Journal where he stated: “Software is Eating the World!” Quite an esoteric proclamation, but his projection was a harbinger of things to come where great efficiency and profitability would be derived from software and its decedents: digitalisation, data management, big data, artificial intelligence (AI), and machine learning (ML). Such are the software artifacts that propel the new applications that are shaping the consumer world in social media, the online buying experience, media streaming, automotive technology, location services,

Mike Brooks, Aspen Technology, UK, describes how software and data management drive efficiency in the LNG industry.

and so on. But such software technologies are also propelling great advances in manufacturing software and data management that drive compelling applications.

Solving real problems

However, it is strategically important to understand that applications that do work on data solve the real problems, and the aforementioned software technologies provide only a supporting role. In the zeal and excitement over the new software and data management tools, the imperative must be to keep focus on the business problem to be solved – do not lose sight of the fact that technology is a potential enabler but not the solution. Clearly, the leading-edge applications in industry, including LNG, focus on analytics, especially rote, repetitive, high-frequency analytics over multiple dimensions that escape human capability.

Then, when the analytics have uncovered issues, humans can intervene to do what they do best in affording judgement and making decisions. The complete solution is a combination human-machine approach. However, to afford success, it is most important to identify the business problem, its explicit data requirements, and to carefully select the appropriate applications that directly align with the analytics job that will enable the business outcome. For example, some companies focus and invest heavily on building a data lake. A data lake is not a strategy: it might be a means to store the data, but it is not the only one. The strategy will be the right application to ensure the correct business outcome. That strategy will require the correct business data context for the solution; the data cannot just be stirred and algorithms applied and get meaningful solutions. This means that analytics work must align with important business goals. And today, a further pre-requisite insists the organisation develops a proper analytical aptitude that is factual and data-driven rather than opinion-driven. Truth comes from data which does not have opinions.

To reinforce this position, consider that the foregoing digitalisation, data management, big data, AI, and ML software and data management technology constructs are bandied around as initiatives. They are not; they are only the potential tools to support and enable an initiative. In the past, a focus on the software technology instead of the real initiatives caused many projects to fail, such as data warehouses. This showed that it was necessary to know what to do with the enablers before they were built. It was also necessary to lead your initiatives with a real business problem. That begs the question: what are the real initiatives in LNG where such technology constructs are the supporting cast? Real operational performance initiatives begin from high-level business drivers, such as: z Keeping the equipment running, as to avoid downtime and poor performance. z Ensuring the lowest use of energy and other resources in making products. z Achieving optimal yield and quality of high-value products.

Figure 1. Digitalisation supports and enables initiatives. z Optimising the cost and risk of all asset expenditures over their life-cycles.

The previous four are the highest-level key performance indicators (KPIs) for LNG and other manufacturing industries wherein deficiencies in each one will cause margin leakage, loss in profits, and can affect safety and the environment. Of these, keeping the equipment running and available to plan is by far the most critical since energy consumption, quality/yield, and cost/risk are inconsequential if the equipment is shut in.

Predictive Maintenance

Following Andreesen’s forecast, new software has emerged and is up to the maintaining equipment availability task. It goes by the general name of predictive maintenance (PdM), but not all entrants under the PdM banner contain predictions and can be trusted. An assertion is not the same as a prediction. However, the field of PdM has advanced so that the leaders in the market contain new technology capable of recognising the specific patterns of behaviour from sensors signals on and around the machines. They detect patterns of normal behaviour, and the outliers of abnormal behaviour, plus the explicit patterns of degradation that if not attended will lead to failure and non-operation. The best applications mean that the underlying technology is easy-to-use because the engineering technology and data science are abstracted, allowing current personnel to develop complex detection strategies. Inside, the technology performs explicit pattern recognition using unsupervised, supervised, and deep machine learning techniques across a myriad of sensor data stream dimensions and across time for explicit/accurate detection that limited capabilities prevent humans from seeing. Such is more accurate and much earlier pure pattern recognition and not simple engineering and statistical model trimming.

Transferable knowledge

The results provide warnings of impending events weeks and months in advance. Such advance warnings are critical for LNG operations since they provide the time to adjust the process to avoid an incident, or if necessary, the time to plan a safe orderly shutdown without emissions releases via the flare stack or pressure release valves. As an example, they prevent the damage in the first place where advanced warning can result in operators increasing cooling to avoid liquid entrainment into compressors and consequent compressor deterioration. The pinnacle PdM applications can also learn on one asset and do a one button click to transfer the learnings intact to other similar equipment. This represents extreme solution scaleability, since with little effort the software can detect normal and fault patterns automatically on new equipment. In this way, a pool of compressors or pumps are readily protected, and each piece of equipment shares its learnings with all the others, providing extreme blanket coverage. For example, analysis on an LNG train detected that it had compressor problems well in advance,

and the learnings were passed to a second train to detect the same issues there. In summary, extra time is money, and it also ensures safer and cleaner outcomes.

Additional software applications are readily available to monitor LNG energy usage, some with great sophistication to ensure the least energy input will provide the heat to secure hydrocarbon product separation at the lowest cost. A critically important application with enormous and rapid payback provides real-time optimisation of operational activities to ensure the maximum yield of product at the lowest cost. Additionally, digital applications transform LNG plant scheduling and longer-term planning activities with sophisticated supply chain analytics. Decision support digital applications provide both offline decision support tools wherein digital models (now called digital twins) can try out myriad ways of controlling operations in a safe, digital-only environment. Such models may be transformed into closed-loop operations that automatically adjust parameters and setpoints in the distributed control system and also project probabilities of future plant conditions.

Detecting latent variables

Addressing quality control with computer-based applications for petroleum-based industries has been around for decades, including sophisticated computer models of process behaviour and special constructs such as management by statistical process control. However, novel quality and yield applications have emerged and are readily available for LNG operations that perform intense computational analysis using hybrid models amalgamating first principles equations with AI and advanced statistical modelling. Such applications will examine months or even years of past data history to uncover the real-life performance and detect latent variables – combinations of measured variables that are far more accurate in measuring and predicting changes in process conditions. They detect the intrinsic cause of process deviations that make quality and yield go awry and offer prescriptive advice on what to adjust to get back on track to maintain the correct quality consistent with the highest yield.

Another key aspect in managing an LNG operation is to ensure that financial expenses on the process and the equipment provide the optimal lifetime return on assets. There are intensely computational applications that can examine all benefits and risks in operational eventualities that can influence LNG production, such as equipment redundancy effects, flow bypasses for heat integration and process control, other alternative operational strategies, and the availability of intermediate storage. The leading applications can understand and advise on other issues inducing risks to profitability, including changing demand, unit turn-up capability, weather and supply/delivery logistics, spare parts inventory, and the quality of service and repair. For full and efficient LNG train operation, the planners need first-rate understanding well in advance to implement the appropriate tactics and longer-term strategies at the appropriate risk and costs. Such a digital analytics application is critically important in understanding and planning ongoing OPEX and budget allocations in addition to CAPEX for major extensions or new projects.

Clearly, as Andreesen said, software is eating the world and is available to play a huge part in keeping LNG facilities safe, environmentally-friendly, and at the peak of efficient production and profitability.

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