Tapping into the data well
How self-service analytics contribute to asset reliability and operational performance
The oil & gas industry has seen some tough times in years past, but now with the Covid-19 world economy and drop in oil prices, it has taken a brutal beating. It’s clear that this is, in many senses of the term, a transformative period for the industry. Market volatility is just one challenge faced on a daily basis — there are so many aspects to think about to strategically run and manage operations. As we continue to digitalize and technology continues to evolve, there are more and more solutions that can also help ease the burden — solutions that can allow the use of an enormous amount of data available to reduce costs, waste, and emissions, as well as improve operational and personnel efficiency and safety.
The key to that, however, is making sure the entire team of process experts and engineers not only have access to that data but have the right tools to analyze it. After all, the main goal of running or working in a refinery is to achieve operational excellence, day in and day out. And this goal is within reach through the use of a self-service industrial analytics platform that uses artificial intelligence and machine learning to analyze plant data. Let me explain what it can do.
Your data tells a story
A self-service advanced analytics solution may sound too complex at first, but it doesn’t have to be. It’s specifically made for users who need to get the most out of the data but are not necessarily trained to be data scientists. It uses pattern recognition along with machine learning to look at time-series data sets, allowing users to gain powerful insights into what the data is saying. And these new insights drive action — from larger, data-backed decision making to troubleshooting day-to-day occurrences.
Self-service industrial analytics empower those who work directly with operations to combine their expertise with the data available. It lets them get knowledge out of the data, to understand the story it is telling. But in order to get the full picture, data from various sources — time-series, contextual, operational — needs to be integrated, used, and shared across all horizontal and vertical levels of the organization. Little can be gained from information sitting in silos. Like characters in a book, data is only meaningful in context, depending on other to tell a complete tale.
When process experts can easily gain insight into their processes using a self-service analytics tool, they can solve more day-to-day questions independently, without the help of data scientists. They can enhance their own effectiveness in running an operation, saving time and money, and providing organizations with new insights based on the ability to interpret what the data is telling them.
What you get is maximized industrial manufacturing operations by more efficiently leveraging both human and data-rich resources. You also get value delivered to the owner-operators at all levels of the organization. To better understand this huge operational and profitability potential, we’ll look at two use cases demonstrating the application of a self-service industrial analytics software.
Case #1: critical valve reliability
An oil rig operator in the North Sea experienced an unexpected increase in flare flow at 10 am for about 15 minutes. Although not a critical event on its own, leaving the root cause unchecked could lead to more severe incidents in the future. Furthermore, more frequent occurrences of similar events could add up to significant losses.
In the past, the engineers would have needed to turn to a more modeling-intensive approach to investigate the situation, but with self-service industrial analytics software, they were able to investigate and solve this problem with a couple of clicks. They highlighted the flare incident in the visualized data before them and searched for similar incidents. They found that 34 similar incidents had occurred before, which was more frequent than they thought. Using the software’s functionality, the team was able to issue a report describing the investigative action taken and the results they found. They were also able to document the information for future reference and to alert co-workers on other shifts. Then the patterns of these incidents were overlaid to graphically see the similarity in process behavior.
Experts investigated the event further using a powerful search engine and feature calculations of the software, helping them to inspect the severity and the maximum volume of the flare. As the software uses machine learning, it can be used to recommend possible reasons for the issue. This capability helped the team to find different behavior before the time of the flare and at the time of the flare. They were able to quickly find the root cause of the event: prior to any flare, a valve closed too quickly. The experts easily eliminated the flare by changing the control logic.
The team had several investigative goals: to determine if such a spike has occurred before; determine the root cause of the incident; and prevent similar incidents from happening in the future. Using the software, the team carried out a systematic investigation of the event. By eliminating the source of the flaring, they improved the critical valve reliability.
Case #2: compressor trip diagnosis
Asset reliability and availability are key to meeting production targets, but this can be hindered by common production problems, like a compressor trip.
At a separation plant, the total throughput had decreased in the last month. Following plant level KPIs, the central team and plant manager noted that one of the critical areas of the plant was having recurring problems: the sales gas compressors had been tripping more often in the last month. In parallel, during the last shift, the operator reported a trip in one of the three gas compressors. To find the root cause, a taskforce of plant process and reliability engineers launched an investigation using their self-service analytics software.
The team wanted to determine the root cause of the recent production losses due to the problems with the sales compressors, to enable automated proactive monitoring that could lead to higher uptime, and to enable process experts to analyze, improve and benchmark the process. They started the analysis by looking at the global KPIs and the status of the compressor dashboards.
Next, they investigated the previous trip and found potential root causes using the software’s recommendation engine — a software feature that suggests possible root causes from analyzing historical time-series data of past events. Lastly, they used the multivariable pattern recognition feature to identify if the previous trips were brought on by the same root cause. They found that two out of the three trips in the previous month had the same cause – an increase in temperature after the fin-fan cooler. To solve this problem, they used the software to set a fingerprint monitor to provide an early warning for engineers and operators. And based on contextual information from maintenance systems and operators, automated recommendations for corrective actions were set as part of the proactive monitoring.
Case #3: improving sulfur recovery
Sulfur recovery units (SRUs) are used to capture sulfur, reduce emissions and ensure compliance for operators. Encompassing a series of technical processes, they are becoming ever more important due to the rising demand for sulfur in various applications and the regulatory obligations around emissions control. Conventional means of monitoring the operational performance of SRUs and assessing the scale of any issues as they arise involve time-consuming, multidisciplinary data analysis projects.
One of the features of the self-service analytics tool is what is referred to as the “production cockpit,’ which can be applied to monitor for when – for any of a variety of potential technical reasons – the non-converted Hydrogen Sulfide in the process is higher than expected, signaling a decrease in sulfur recovery rates.
In one instance, the process of finding the root causes associated with an underperforming SRU train – then making the necessary changes and introducing proactive monitoring – produced highly positive results. Train performance was even better than before the data analysis work, resulting in 8% higher unit utilization, an increase in sulfur recovery from 96.5% to 99.3% and a 15% reduction in SO2 emissions.
In principle, this strategic approach of putting advanced analytics in the hands of subject matter experts allows them to handle 80% of energy-related cases that contribute to corporate goals for reducing the carbon footprint.
With self-service analytics, it is possible to dramatically increase the number of data-driven decisions since analytics is closer to the process experts and for the majority of the cases no modeling is required to achieve a valuable solution. With the current industry and market challenges, the benefits also come from having a scalable and easy to replicate data analytics strategy to continuously improve the operational excellence of O&G assets.