How to Harness The Power of IIoT for the Oil and Gas Industry with a Limited Budget

The Oil & Gas industry is experiencing difficult times due to the falling price of oil. Within the Oil & Gas industry, the refinery sector in particular is under pressure to run refineries at full capacity to capture the oversupply in the oil market in order to maintain profits. Additional pressure is being applied by Wall Street, forcing the industry to cut capital expenditures amid a decreasing appetite for new capital projects. The need for better operations and maintenance with existing equipment is becoming more critical for refinery owners. With the technological capabilities provided by the Industrial Internet of Things (IIoT), large real-time data analytics and machine learning, energy companies can enhance operations and improve their bottom line.

A small decrease in the downtime of an energies system, like a refinery, grid, or power station, could potentially be worth hundreds of millions of dollars. Subsequently, the need to process and predict events, using large amounts of digital refinery data in a timely manner, is fundamental to a solution. However, traditional structured data warehouse solutions cannot effectively accomplish these tasks. They are expensive to build and maintain, ineffective in rapidly identifying root causes of asset reliability problems, and do not scale well.

Additionally, the world of digital data sources is expanding (new IIoT-ready equipment, financial data, etc.), bringing in huge amounts of new data from new sources. Trying to fit this data into a traditional data warehouse solution becomes a very costly IT project. Simply adding new IIoT devices to infrastructure will prove fruitless if they cannot be effectively managed or their data is not put to its potential in a complimentary analytics platform. Data, strictly speaking, is not information. Data is merely a collection of unorganized, raw figures. Information is what we can learn from data (if we can analyze the data correctly) when put into organized formats. Therefore, new IIoT devices need an equally cutting-edge analytics platform. The current analytics solutions being used by the energy industry are hard to program, poorly display information and require additional resources in the form of data scientists.


Bit Stew: Solving the Data Integration Challenge with a Purpose-Built IIoT Platform Refineries need an extensible, and purpose-built IIoT platform to help them effectively integrate the ever-growing data into useful information, which can be used to investigate and predict issues. The system also must facilitate the evolution of additional capabilities. In light of these needs, Blackstone Technology Group and KPMG have partnered with Bit Stew Systems, who has built the premier platform for handling complex data integration, data analysis, and predictive automation for connected devices on the Industrial Internet. Through our partnership we bring to market a complete solution addressing the data integration challenge faced by refineries and energy companies in today’s world of new digital data.


In extending Bit Stew’s cutting-edge MIx Core platform, which provides a model solution, Blackstone and KPMG can create customized solutions for individual customers. These custom solutions provide the next steps in the customer’s business evolution. They provide refinery owners, when coupled with experiential knowledge, the information needed to make more informed decisions and the communications capabilities needed to carry out these decisions, without the cost of large pre-built system.

Key features of the solution are:

  • Important refinery insights with pre-built Interactive refinery dashboards for Operators and Executives
  • Predictive Analytics Built- in methodology / knowledge of refinery operations and maintenance
  • Open and scalable platform for real-time ingestion of refinery related multi source data

Going forward, the impact of new and emerging technology such as IIoT, cloud solutions, and analytic engines will be as revolutionary to the refining industry as the introduction of electronic instrumentation and distributed process controls. The winners in the industry tomorrow will be the progressive downstream companies investing in data analytic systems today.