Self-service industrial analytics allow subject matter experts to analyze, monitor and reduce the Carbon Footprint.
The discussion about climate change has been taking place for many years and is still a hot topic. This debate has led to global initiatives to reduce carbon footprints, which is high on the agenda of almost every country’s government. Regulations on a global, regional and local scale have been established to reduce greenhouse gas emissions, and this heavily impacts the oil and gas industry. To achieve those goals and prove regulatory compliance, companies in the industry are rapidly adopting the ISO 50001 standard to improve energy performance and make climate part of their corporate strategy. The role of Big Data Analytics for Energy Management in, for example, the Oil & Gas Industry can be a huge contributor to meet carbon footprint goals.
“The climate has been fully integrated into Total’s business and strategic vision as well as its organizational structure,” said Patricia Barbizet, lead independent director, in a recent presentation.
Reducing the carbon footprint has an overall profitability benefit. Within the oil and gas industry, energy often is one of the largest components of the company’s cost structure. Big data analytics for energy management in the oil and gas industry to reduce costs is not new, but it has become more important due to the imposed regulations. Most companies have formalized energy management programs and use automation and control technologies to help minimize energy costs. However, many companies need to take their efforts to the next level by monitoring and optimizing energy use in real time and leveraging Industrial Internet of Things generated data.
For many years process data have been captured in historians. All of these data need to be unlocked and leveraged for continuous improvement to lower the carbon footprint of the company. To some extent, data analytics has been utilized by large companies for their larger onsite energy issues. These time-consuming, centrally led data modeling projects are less suited for process-related optimization projects that require subject matter expertise. New tools put advanced analytics in the hands of subject matter experts (SMEs) such as process and field engineers. This allows them to handle 80% of energy-related cases that contribute to the corporate goals for reducing the carbon footprint.
Energy management 4.0
Global interest in Industry 4.0 has accelerated digital transformation in the process manufacturing industry, including the oil and gas sector. Many companies have engaged in technology pilots to explore options for reducing costs and increasing overall equipment effectiveness and regulatory compliance. One of the best ways to leverage these new innovations is to apply advanced industrial analytics to production data generated by sensors. All the data provide unique opportunities for improving energy efficiency.
In general, energy savings can be achieved in various ways: through change in daily behavior (switching off the light), through installations of more energy-efficient equipment, through equipment maintenance or through process optimization and ensuring the use within the best operating zones. Process and asset performance optimization is probably the biggest area for energy savings, but it requires a deeper understanding of the operational process and asset data (available in the historian).
Analyze, monitor and predict W.A.G.E.S. consumption
Subject Matter Experts (SMEs), such as process, operations and maintenance engineers, have deep knowledge of the production process. The major process-related energy consumers include water, air, gas, electricity and steam (WAGES) and can be directly or indirectly analyzed through all sensor data. The data can be descriptively analyzed to determine what has happened, providing a better understanding if a long period of performance can be assessed. Sometimes, certain issues happen only a couple of times per year but can have a big impact on energy consumption (a trip causing a shutdown, for instance). Discovery analytics helps engineers understand what has happened, and through diagnostic analytics the organization can start monitoring the performance of the site.
Since asset performance is contextualized by the process function, the best performance windows need to be extracted from actual process behavior. Based on the historical data, fingerprints with an energy consumption focus can be created to monitor good and bad behavior. Additionally, monitoring live operational performance can be used for predictive analytics.
Practical Use Cases
There are already multiple instances where advanced analytics were successfully used to analyze, monitor and predict the process and asset performance of energy management.
One example is related to energy consumption within the cooling water network. Reactors consume cooling capacity from the utility network to cool water. Sufficient cooling capacity is critical for these reactors as thermal runaway could occur when the available capacity is insufficient. To avoid this undesirable situation, a monitoring system using advanced analytics was set up. Early warnings were created and only triggered on actual problematic situations, avoiding false positive alarms that could be triggered by measurement noise or spikes in the data. Upon receiving a warning, the process engineer and operators have ample time to rebalance the reactors and deprioritize other equipment so that critical systems can consume the maximal cooling capacity and the overall energy consumption is within target boundaries.
Another example is a predictive maintenance case for fouling of heat exchangers. In a reactor with subsequent heating and cooling phases, the controlled cooling phase is the most time-consuming. Fouling of the heat exchangers increases the cooling time, but scheduling maintenance too early leads to unwarranted downtime, and scheduling too late leads to degraded performance, increased energy consumption and potential risks. To enable timely maintenance, a cooling time monitor was set up, which extended the asset availability and reduced the maintenance cost and safety risks. All these benefits, including controlled energy consumption, ultimately led to a 1%-plus overall revenue increase of the production line.
Continuous Improvement 4.0
In general, finding and solving root causes for process deviations and anomalies results in more energy-efficient operation. Monitoring the live production performance allows control of various production parameters, including energy consumption. When the total energy consumption of a specific year is taken as a baseline, monitoring of performance against corporate goals becomes possible.
Energy management also is important in other process industries. Covestro, a chemical company, initiated three major energy-savings projects for its polyether plant in Antwerp as part of the energy savings goals and ISO 50001 directives. Self-service industrial analytics solutions were implemented for online detecting (including root cause analysis and hypothesis generation), logging and explaining unexpected energy consumption, and for comparing the results with the reference year 2013. Using specific formulas and calculated tags, various energy consumers are monitored and controlled. Through monitoring the performance against the reference year, it showed the energy consumption has effectively decreased year over year, meeting the corporate goals. More importantly, with a growing knowledge and insight into the production process, Covestro is continuously improving its overall performance.
Big Data Capabilities in Oil & Gas and Energy Sector
Big Data Analytics for Energy Management in the Oil & Gas Industry is not new; many companies have a structured energy management program in place. Companies leveraging the Big Data potential by using self-service analytics tools are twice as likely to be on top of financial performance. Furthermore, it is more likely to make faster decisions than their peers. Subject matter experts to analyse, monitor and predict process and asset performance, which can result in a huge contribution to meet the organizational carbon footprint goals. Especially when the low hanging fruit for energy savings has been picked and more knowledge is needed to improve operational performance, with the added benefit of improving overall profitability and increased safety.
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