Scaling AI in Oil & Gas: How can O&G companies optimally leverage data
AI can help companies in many ways including predictions in exploration, driving efficiency in drilling and production, predictive and preventive maintenance, and optimization in transportation and distribution, strengthening workforce safety, and compliance with regulations, etc., to drive profitability. However, there are various challenges that are acting as barriers to scaling AI in the O&G industry-
- Data Digitization – Oil and gas companies have huge backlogs of documents stored in the physical form like papers, tapes, and maps, etc. The data stored within these documents can help them in various ways including finding oil wells, demand forecasting, workforce security, and compliance, etc. This historical data needs to be converted into digital form to integrate it with modern systems and AI workflows.
- Data Origination – A lot of useful data is generated and collected by sensors installed in the equipment of the suppliers. The suppliers sell fully integrated services coupled with products to the oil companies and the closed systems do not support APIs for integration with other systems. So, a piece of equipment with a sensor can generate large volumes of data but that data remains unused as the companies lack the technology to integrate that data with their systems. To get access to these data and integrate it with their processes, O&G companies have to update their procurement specifications and invest in equipment that is open, secure, and interoperable.
- Data Quality – A Gartner study highlights that poor data quality is one of the major reasons behind up to 40 percent of business initiatives failing across industries and highlights that data quality can impact productivity by up to 20 percent. To leverage artificial intelligence, businesses must set processes and leverage technology to collect clean data and organize them to plug into AI tools.
- Dark Data – Oil and gas companies have been using immense data sets and files for years. But the collected data often remains unused. The unknown and unused data also called dark data comprises half of all data collected. Gartner defines dark data as “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.” A third of respondents in a survey agree that over 75% of the data in their company is dark. Only 11% report that less than 25% of their organization’s data is dark. AI and Machine Learning can help businesses to convert dark data into data intelligence and get insights from it.
- The high cost of failure – The cost of failure in Oil and Gas is very high in comparison to other industries. For example, a study by Kimberlite reveals that just 1% of unplanned downtime can cost oil and gas organizations $5.037 million each year. The probability of high losses due to downtime resulting from technology failures limits companies from experimenting with new solutions and compelling them to work with mature solutions.
The Oil & Gas industry is currently facing a range of economic, social, political, and environmental challenges. It needs to find creative ways to cope with these challenges. AI can help companies build the capabilities required to address these challenges.
At Qualetics, we specialize in playing the role of the Intelligence Strategy partner and assist our clients in identifying the best use cases for their data and seeing through the implementation with our proprietary AI platform. Get in touch with us should you want to explore opportunities in implementing AI in your business.
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