Refinery Blending Operations: Using Advanced Data Analytics To Improve Performance

Recovering fuel demands and record high prices have led to an increase in refining margins for the first time since the Covid-19 pandemic.

This industry is plagued with archaic, siloed systems and the recent attrition due to global pandemic has led to processes inefficiencies and knowledge gaps in several areas of the refining business. Currently more than ever communication and data analytics are proving to be key to success.

The Issue

Data is stored in several different databases – Orion, LIMS, Pi are examples of a few. These databases store data at different frequencies, in addition individuals keep spreadsheets to track specific metrics. Data in spread sheets is prone to corruption and requires several steps to populate. The data not bein easy to access and lack of transparency are all pain points of the industry.

Data Analytics

An important tool for gaining business insights and providing tailored responses to customers, “Data Analytics” has become increasingly important for organizations and has evolved and broadened over time, providing many benefits. With the development of Big Data, Data Warehouses, the Cloud, and a variety of software and hardware, Data Analytics has evolved, significantly. The term “Business Intelligence” is ideal to describe decision-making based on data technologies.

Connecting the Dots

Various analytic tools on the market today are both powerful and cost effective. Creating interactive dashboards connected to live databases helps reduce manual data manipulation and provides quicker access to information to make data-driven decisions. In the blending process for example analytic software can help bridge the gap between:

• Components used
• Blending operations
• Lab data
• Crew performance
• Giveaway results

Operationalizing for Action

Operationalization of analytics requires four facets, decision making, organizational, application and business. Decision making is the most important, the dashboards developed helps streamline the process and pre-empt potential issues rather than fighting fires. Being able to see historic performance versus live performance is an invaluable tool in early identification of a potential problem.

Taking action needs to be the corner stone of analytics. What good is the analysis if you don’t act on it? Hence it is important to imbed the analytics into the existing process.

The business facet ensures there is agreement on the tools (dashboards) and their value. Understanding how the discissions influenced by the analysis are expected to impact results.

The Organization facet ensures that all parties roles and responsibilities are identified.

The application facet defines the process in witch the new analytics will be used as well as system requirements, (databases, visual requirements etc..)

Challenges

Although companies and management are in favor of advanced analytics to drive business decisions many of the issues arise from people’s behaviors and processes rather than technology. IT restrictions, political agendas and governance tend to be the largest barriers to progress.

Solution and Results

Starting with small pilot projects owned by the business to prove benefits before pushing to a wider audience, focus on people behavior and process first, to get executive buy-in and encourage discussions within the organization. Education and training are vital to ensure sustainability. Finally, communication plays a very important role in the success of new analytic projects. Using communications demos and actively engaging the business, having BI meetings with stakeholders and publicizing results help to achieve acceptance. Changing process, systems and behaviors are the pillars to effect positive change and sustainability.