September 2018

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Software: Operations intelligence software for real-time visibility

The tiny company town of Sinclair sits in south-central Wyoming.

Pawlewitz, J., Siemens

The tiny company town of Sinclair sits in south-central Wyoming. With a population of only 450, it is named after the Sinclair Wyoming Refining Co. facility that was built in 1924 and provides a backdrop to the town’s eastern edge. It is one of the largest and most complex refineries in the Rocky Mountain region.

Capable of processing 85,000 bpd, the refinery runs a mix of Canadian and US sweet and sour crudes. Its top five outputs are propane, gasoline, jet fuel, diesel and road asphalt for customers throughout the Western US. Over the plant’s life, management has made major investments to install gasoil hydrocracking, gasoil hydrotreating, delayed coking units and associated hydrogen generation and sulfur plant capacity. These added process units cut the sulfur content in gasoline and diesel fuels, while improving the capability to process heavy sour crude oil into high-quality transportation fuels. They also increased the plant’s complexity in terms of operations, maintenance and sparing for its 500 employees.

To track and visualize such a complex operation, as well as to increase safety, environmental compliance, training and asset utilization, the management team was challenged to develop cross-functional operational views with real-time data inputs. These challenges spurred the implementation of a continuous process improvement program.

A core outcome of that effort was the establishment of key performance indicators (KPIs). Monthly KPI reports from the plant’s various functional departments were created as spreadsheets and known around the refinery as “one-pagers.” While these provided management with operational insights, there was a recognized need for more operational data.

It was identified that the monthly reports were historical vs. reflecting current conditions, as well as functionally oriented, which reinforced siloes of behaviors. While the reports were posted for everyone to see, they were posted in the functional work areas and generally provided limited cross-functional awareness.

The established reporting methods were primarily manual processes with typical sustainment challenges. Some required inordinate levels of effort to acquire data that was often inconsistent, and where content and data continuity from month to month presented clear improvement opportunities.

Most importantly, the KPI reporting mechanism needed a way to combine the plant’s operating data from its fixed assets with transactional data associated with activities done to those assets, such as maintenance and repairs.

Faced with these challenges, the solution was to implement a sustainable practice of KPI-driven plant performance monitoring software. The project took a two-pronged approach with parallel, interrelated work efforts. The first addressed the opportunity to establish a unified asset model. From the start, each system—maintenance management, inspection, process safety and plant management—had its own way of handling data, as well as different nomenclature for identifying the same data. The incremental benefit of establishing an asset model with the ability to aggregate views of data from these various systems is substantial.

The second work effort—named “iDino” after the company’s widely recognized dinosaur logo—involved augmenting the monthly static Excel and Word reports with functional and cross-functional dashboards displaying real-time data from the refineries’ varied operational systems. To accomplish this, the operator used a proprietary softwarea system that collects information from all refinery systems (e.g., monitoring corrosion and vibration) and aggregates it in real time for more structured, consistent and auditable views. This data can then be subject to analytics, as well as provide better situational awareness and decision support.

While the two work efforts run on parallel tracks, the proprietary software bridges the two, facilitated by the available software connectors. The vision for the iDino environment was to sustain the asset model via synchronization with an existing smart piping and instrumentation diagram (P&ID) package. Updates to the drawing would be automatically propagated to the digital model. With smart P&IDs, all changes automatically propagate throughout the system database, with a historical record documented along the way.

The software implementation will provide the Sinclair refinery’s leadership with more dynamic, real-time association of its three core assets: plant, people and product. For example, the operator can select an asset (e.g., pump) and access detailed data, such as when it was last serviced, who worked on it and what product has been running through it, along with a wealth of additional data that can provide the broadest insight possible into the life of the asset. This solution removes the technological barriers between users and the information they need to do their jobs, enabling them to make informed decisions at a faster pace. HP

Notes

  a Siemens’ XHQ operations intelligent software

The Author

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