Opinion
Big Data and Analytics: An Opportunity under Development in Downstream
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n an increasingly digitized world, the global oil and gas industry has had to explore turbulent waters to reinforce investments and not be relegated. In this sense, various companies in the sector have begun to consider advances in digital technology to take advantage of their benefits. In particular, the applications of several technology solutions have proven to be highly beneficial in oil refining activities, as well as in distribution and retail. This is particularly relevant since today, even with the growing electrification of transport, downstream remains one of the energy sectors with the highest demand globally. Faced with the reconfigurations of global demand —influenced by factors such as the importance of energy efficiency and the energy transition—, refineries will have to increase their investments in big data and analytics; mainly, to reduce its carbon emissions even more and transcend its processes to more resilient and profitable forms of energy. In this regard, Big Data can help oil and gas companies in the downstream sector manage and process large datasets and improve their production processes. As various industry experts acknowledge and underscore, "data is currently the oil of the new economy." 8
By Rubi Alvarado General Director, Energy Capital
Worth noting, Big Data and analytics have a long history in refining. For instance, the analytic equation was introduced in some refineries in the late 1980s for property prediction in rotating equipment and detect poor performance. More recent advances in big data technologies include data logging, storage, and processing. Accordingly, some of these solutions can be applied in the refinery sector, including estimating energy efficiency and its use to correctly assess downtime maintenance. Likewise, big data can be used in repair costs through various models and analysis methods. On the distribution industry side, too, it is used in maintenance and to predict process and equipment failures. As a complement to big data, the analytics part is also being introduced in the downstream sector with enthusiasm, particularly with the aim to investigate and understand the inner meaning of the large datasets an asset produces. Some of the processes throughout analytics work include machine learning and data
