Data is at the core of day-to-day operations in the oil and gas industry. But delivering productivity and efficiency targets in the energy sector demands a new approach to data analysis that combines deep domain and physics understanding with advanced data analysis that can scale intelligence across an enterprise.
A recent survey found that 43pc of executives at integrated oil and gas companies identified the increasing availability of big data analytics and insights among the top trends expected to benefit their company’s growth in the next three years.
The opportunity is significant, with estimates suggesting that less than 5pc of oil and gas data collected is used or analysed. Organisations that can successfully harness the value of operational data intelligence at-scale will spend less time running manual, reactive, procedures and more time applying AI and machine learning applications for predictive insights, which will ultimately improve operational decision-making.
Siloed data stalls operational insights
Data-based decisions require that data be readily available for analysis. The challenge many oil and gas businesses face is how to harness the large variety of traditional and non-traditional sources of data important to an oil and gas system. In the case of a refinery, for example, new sensor data is constantly generated across critical and non-critical assets. Maintenance logs, operations data, case management systems, piping and instrumentation diagrams, process flow diagrams and even weather data all provide additional valuable information about the overall health of the refinery and can help identify potential risks and anomalies that cause unplanned downtime.
Creating the custom, in-house capabilities to aggregate, integrate and visualise all this data is a large undertaking. Patching together data from these various sources requires complex coding and takes significant time and resources away from other data science projects. Assuming the patchwork of data integration tools are accessible and successfully deployed in an oil and gas environment, continued maintenance of these data tools will be needed to allow for ongoing analysis. Eliminating that burden is an important step forward for oil and gas companies, allowing them to move from data management concerns to data intelligence.
Applying AI to unified operational data
Once data is available for analysis, traditional methods alone will not meet the needs of today’s energy enterprise. AI has been shown to derive 79pc more incremental value from data in the oil and gas industry than traditional analytical techniques. The value lies in the ability to analyse large volumes of data, provide predictive intelligence, and quickly and securely scale initial software deployments across any number of operational systems in an enterprise.
Unlocking this value requires platforms and applications designed to apply AI at enterprise scale, with the ability to provide full facility coverage and support millions of models running on trillions of rows of data.
Drawing on the power of the BHC3™ AI Suite to simplify and accelerate the deployment of AI at scale, the BHC3 Reliability Application integrates, federates and unifies data from internal and external sources into a unified, logical data image. Applying supervised and unsupervised machine learning techniques, the application learns from data-driven patterns, continuously and consistently ingests new data and couples the information with user feedback to improve insights. Maintenance managers, reliability engineers and operators can better predict risks across systems, sub-systems and individual assets such as turbines, compressors, fans or pumps, pipeline pup stations, and refining and chemical manufacturing plants.
AI and machine learning are transformative technologies helping organisations reach new levels of productivity and efficiency. In the case of predictive maintenance and reliability, AI will move oil and gas operations from reactive and calendar-based maintenance programmes—or those that rely solely on physics rules—to a data-driven approach to monitor and maintain industrial oil and gas assets and systems.
Download the BHC3 Reliability eBook to explore how advanced AI/Machine Learning technologies can create new opportunities for your enterprise to optimise operations—and drive profitability—using a unified, scalable, easily integrated data platform.
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