FUTURE DIGITAL TWIN
Advanced Analytics Solutions Drive O&G Efficiency and Sustainability As time-series process data compounds over time, spread throughout multiple systems and databases, advanced analytics solutions help teams make sense of it all with centralization, contextualization, analysis, and insights.
By Morgan Bowling, Seeq Corporation
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ver the decades, there has been a wide variety of process control and software systems deployed throughout refineries and petrochemical plants to monitor, gather, and process data in real time. These various systems include distributed control, supervisory control and data acquisition, laboratory information management, and others. As information has increased, there is a growing volume of time-series data that can be used to identify operational optimization opportunities to increase efficiency and reduce upsets, aligning with critical corporate initiatives. Yet, many organizations face challenges accessing and connecting data from so many systems, analyzing it efficiently, and operationalizing insights in an effective manner.
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Addressing these and other issues, modern advanced analytics solutions are enabling operating organizations throughout the energy and chemicals industries to automate data collection and cleansing. This enables companies to decrease time to value, by shifting operations and maintenance from reactive to proactive and predictive, and enabling teams to share insights more broadly with multidisciplinary teams.
Spreadsheet limitations Although analytics applications have come a long way over the years, a shocking number of engineering experts are still stuck using spreadsheets for data aggregation and analysis, requiring time-consuming manual data preparation and cleansing from multiple sources. Spreadsheets present myriad limitations, including subpar computational capability, lack of live data connectivity, prohibitively difficult shareability, and clumsy visualization and reporting functionalities. Void of live connections to both historical and live data sources, engineering experts are forced to manually query individual databases, extract the data needed for analysis, then aggregate and align mismatched timestamps in a spreadsheet. Whenever a new time period of interest is identified, the process must be repeated. With so many hoops to jump through, it is easy to understand why nearly 80% of engineers, scientists, and analysts surveyed in a 2016 CrowdFlower study reported spending more time collecting and wrangling data into formats suitable for analysis than any other task (Figure 1).
