il and gas (O&G) companies are continuously striving to optimize overall equipment effectiveness, performance and profitability within a highly volatile and regulated environment. Several of those regulations are coming from an increasing industry effort toward the reduction of emissions that affect both health and the environment. Initiatives are growing from industry and governmental groups. For example, O&G companies that operate in the U.K. Continental Shelf are stepping up to reduce carbon emissions to net zero by 2050 in the U.K. Another example is the decarbonization efforts led by the European Commission, which aims to initiate the transition toward “a climate neutral economy” by 2050. This will require the active involvement and investment of different industry, technology and governmental sectors for mid- and long-term solutions.
To start getting results today, it is key to take advantage of underutilized data in combination with process expertise that is already in place. Aim to improve process workflows to have a better and more efficient emissions control and reduction.
There is an increasing need to exploit the large set of data being generated from sensors, instruments and assets. Traditional methods of Big Data solutions require complex IT projects and data scientists to build and maintain models. Aside from being costly and time-consuming, this way of working can also create resource bottlenecks in the organization and underutilize the process and asset experts. Turning big industrial data into actionable information might seem like a huge task, but self-service industrial analytics makes it easy for process engineers to optimize the processes by themselves. Results are delivered fast, directly into the hands of the process experts who can really provide meaningful interpretations to the data, allowing them to uncover insights at all levels of production, improving day-to-day decision-making.
The process expert decided to set up a monitor to follow the pattern of sudden increases of the H2S content independent of the absolute value. Through patented pattern recognition technology, the process expert was able to identify particular behaviors for periods longer than 20 minutes. By saving the search, the H2S content behavior can be monitored in real time. Each time a user-configurable percentage of similarity is matched, an alert via email is sent to the operator for taking appropriate measures to control the process.
All this information can be used to create an analytics-driven production cockpit (Figure 2). Looking at a timeframe set by the user (in this case one week) containing the live status of the H2S content as an alert, a quick overview is provided to all relevant time-series data following the Sulphur recovery in MT/D (megatons per day), the total steam production in lb/h as well as several temperature measurement upstream of the Superclaus. Lastly, the operator has access to a counter that shows the history of the behavior of the H2S content alert for a determined period of time.
In this use case, with the dashboard in place for the control room, an increase of H2S was detected for more than 20 minutes, triggering the H2S status alert. During the shift handover, it was decided to further investigate the issue with the self-service analytics software. Just with one click on the alert tile, the engineers move to the time-series data universe to start a root cause analysis with the time frame and tags of interest available.
Since the issues are not immediately clear by using the tags around the H2S content analyzer, it is decided to look further upstream. Instead of trial and error, the self-service analytics software can suggest root causes through using the recommender engine. In this use case, the recommender engine suggests a strong negative correlation between the operating temperature of the first Claus unit and the H2S content value. An immediate call to action to bring the process and the recovery back is to check fluctuations in the sulfur flow and steam around the first Claus unit and/or increase the process of its inlet process gas temperature.
The self-service analytics tool has helped the process expert to easily visualize and monitor the process, assess the size of the problem, drill down to a series of root causes and finally set up a monitor to prevent the issue from happening in the future. There was no need for a long multidisciplinary data analytics project, the process engineers could easily do all the data analytics themselves, including the use of contextual data from other business applications and easily create a production cockpit to monitor, control and improve the process. In this way, it helps reduce emissions, maintenance costs and production losses.