The shale boom unleashed a flurry of new construction of gas processing plants in the U.S. With it, came a major surge in gas processing capacity: from 77 billion cubic feet per day (Bcf/d) in 2009,1 to 83 Bcf/d in 2013, to an estimated 95 Bcf/d in 2017.2
Now, with capacity up and a lull in oil prices, construction has slowed substantially. Focus is shifting from building new plants to the long-term operation of those facilities recently built. In particular, there’s a desire to identify and capitalize on any optimization opportunities that can boost plant productivity and maximize profitability.
Understanding how to do this, however, first requires understanding of how the rapid buildout of these plants played out over the last 10 years.
Gas Production Accelerates Process-Plant Buildout
Natural gas production far outstripped gas processing capacity during the shale boom. This pushed companies to get new processing plants up and running as quickly as possible in places from Ohio and West Virginia to Oklahoma and Texas. It also pushed companies to rethink the construction process used for deploying new plants.
The traditional approach of designing and building plants from the ground up took several years. This approach was making it too time consuming to meet demand during the shale boom. Instead, companies opted for a modular approach to construction, reducing the build cycle time to as little as 12 to 18 months.
This helped the plants more quickly catch up with production. However, the short-term benefits came with challenges affecting the operation of the plant.
For instance, the new modular approach involved using multiple skids from multiple vendors. One vendor could have provided the inlet compression equipment, another the treating equipment, and yet another the refrigeration and process equipment.
This put a burden on workers to learn how to operate a wide variety of equipment, often lacking a common look and feel. It also created integration and interoperability challenges, with different skids having different architectures and programming.
The ongoing need to build new plants meant that most personnel were primarily dedicated to building the next new plant. As a result, they had little time or resources to devote to optimizing operations in each plant before moving on to the next one. This also meant that data-collection systems were not deployed to their maximum usefulness.
Consequently, many gas processing plants today now have challenges in accessing and analyzing data, and as a result may not be able to fully realize the potential of their own information.
Identifying Optimization Opportunities
The modular construction approach left many plants performing below their potential. However, with the pace of buildout now having slowed, there are significant opportunities to improve efficiencies and profitability in gas processing plants across the U.S.
Some opportunities are immediately visible. The indicators of some of these opportunities are in plain sight, and include process upsets, equipment failures and process bottlenecks. Other opportunities require an investigative effort to be unearthed., Any of these optimization opportunities can offer more potential for increased recoveries, energy savings and expanded throughput. Companies can use information-enabled systems, tools and technologies in a Connected Enterprise to turn these opportunities into financial success.
Developing and Supporting a Culture of Optimization
It’s not just a matter of upgrading infrastructure, in many cases a cultural shift must occur. The infrastructure, however, can be a key component in successfully fostering a new culture of optimization.
For example, key decision makers will need to be convinced that any optimization effort can recoup its cost and deliver long-term value. Plant workers must be empowered with the appropriate tools to be sure the necessary changes are smoothly and fully implemented, and that their expected value continues to be realized.
The bottom line: Significant opportunities exist, but any effort to capitalize on them must be careful and diligent.
Optimization opportunities will be unique to every plant based on factors like existing infrastructure and business needs. To help uncover the opportunities that are specific to them, companies should work to identify their technology and data gaps.
They can do this by answering three key questions:
1. How extensively does my automation system measure my process?
Amid the construction frenzy of the last 10 years, some companies purposely limited the number of sensors or inputs built into a plant’s automation system. The idea was to help save time and money during construction. But again, such short-term gains have long-term challenges. In this case, the challenge is a reduced ability to leverage the Internet of Things (IoT) to monitor and improve production.
By deploying internet-connected sensors on critical process equipment – such as pumps and compressors – companies can begin to collect real-time data on virtually any aspect of their operations. They can then use this data to better understand their operations, whether it is equipment performance and health, KPIs, or even worker behaviors, to inform optimization strategies.
2. How am I recording data?
Too often, plants only have a bare-bones system in place for recording their data. They may use the data they collect for compliance-reporting purposes but otherwise do little else with it.
Plantwide optimization is severely challenged using this limited data approach. Instead, a robust and reliable historian capable of high-speed data capture is needed to collect and record data produced by hundreds or thousands of sensors.
Modern historians can collect up to 2,500 data points from multiple local and remote controllers, and have collection speeds as fast as 10 milliseconds. Such capabilities are necessary for monitoring effective equipment usage and performance, and for driving continuous improvements. Historical data also can help speed up troubleshooting during a downtime event or support predictive-maintenance strategies that help mitigate downtime in the first place.
3. What tools do I have to visualize and analyze data?
Gaining access to data is one thing. Being able to easily understand it and act on it is another.
This is why visualization and analytical tools are critical. They take raw machine and sensor data, and contextualize it into clear, easy-to-understand production information for operators and decision makers. Users can also use visualization to compare data sets produced by separate systems. And they can deliver information via multiple channels, which can help confirm workers receive information on their preferred devices and also help connect plant workers with colleagues and consultants across long distances for greater collaboration.
Creating Cultural Change
The greatest hurdle to implementing can be justifying the required investment, even if specific optimization opportunities are identified. After all, optimization efforts will go nowhere if key decision makers, whether they’re upper-level managers or outside investors, aren’t sold on them.
Given that a plant’s infrastructure may be in a state where it cannot fully support desired optimization efforts, the best place to start may be with two simple tools: a pencil and clipboard. It can be a tedious process, but manually collecting data on the improvements – and the savings – that an optimization effort can deliver is often the best way to justify a proposed investment.
Another challenge is seeing the optimization effort through to its desired results. If operators don’t understand a new technology, for example, they either won’t be able to apply it to its fullest potential or they simply may not use it at all. Likewise, if engineers have poor visibility into the system, then they may not make important activities, like sensor calibration, a priority.
It’s critical that a culture of support be in place to perpetuate the optimization project and build support for future efforts. Tactics for building this support include providing training for any new processes or technologies, using visualization tools that provide clear visibility into systems, and developing thoughtful lifecycle plans to keep new technologies running.
Efforts Already Underway
Today, gas processing plants are already implementing a variety of changes to help optimize their operations and improve profitability.
Many plants, for example, now collect compressor data to help improve troubleshooting and overall maintenance strategies. As a result, they’re seeing improvements in the form of higher uptime and reduced maintenance costs. There is a wide spectrum of sophistication in compressor-monitoring systems. Simple systems record basic parameters of runtime, temperatures, pressures and maintenance. More sophisticated systems add vibration monitoring and analysis, requiring a respective improvement on the data-collection infrastructure. And a few companies today are delving into the realm of advanced analytics. The latter promises unique and valuable improvements, along with venturing into new infrastructure such as the cloud and big-data software.
One processing plant in Texas recently implemented a model predictive control (MPC) software to improve production. This project took several years to move through the cultural and infrastructure issues before it was approved and implemented. But once implemented, the increased throughput paid back the project costs in just a few months.…
From Nicety to Necessity
The technologies required for optimization efforts, from process-measuring sensors to advanced visualization and analytics tools, shouldn’t be seen as luxury or discretionary items. Rather, they should be considered necessary components that every well-run plant should have in place to be competitive. There couldn’t be a better time to implement them, as more companies are seeking to maximize plant performance and counter the impact of low oil prices.
1 U.S. Energy Information Administration, Natural Gas Processing Plants in the United States: 2010 Update, June 17, 2011
2 Energy.gov, Natural Gas Infrastructure, 2015