Though it’s possibly playing it down a little, it’s fair to say that the beginning of the new decade has been far from exceptional where business is concerned. In fact, 2020 has been a tumultuous year in which prediction, expectation & business best-case models have been routinely refuted. This is certainly true when it comes to Oil and Gas.
Covid-19 is a singular challenge for the oil and gas industry, the triple threat of supply shock, demand drop and a global humanitarian crisis are proving difficult to shake and distinguish this crisis from those faced during the credit crunch and the oil plunge between 2014 and 2016. How then, can big data analytics help to reduce the impact of Covid-19 on Oil and Gas?
Big Data Analytics in Oil and Gas
Traditionally, Oil and Gas companies have collected massive amounts of data from their equipment, fields, and other operations. The challenge is and has always been harnessing actionable insights from this data in real time to aid decision making and increase efficiency exponentially, in an age in which Covid-19 is continually upending our abilities to progress, big data represents a powerful means of disrupting back.
A lack of visibility across the organization is a near universal business issue in the 21st century and the field of oil and gas is no different. Data, though it is far from the most glamorous resource that these companies generate, it should be seen as just that, a resource. With the use of big data, companies can not only cut costs but also capture large data in real time. Such use of analytics can help in improving production by 6%-8%.
We’re starting to see an uptick in investment where data is concerned as the difficulties of performance improvement, equipment life cycle management, logistics complexity, and meeting environmental regulations are problems that just don’t seem to be going away. Big data is the means by which executives can more effectively pursue a solution.
It is estimated that drilling a deepwater oil well often costs over $100 million, to that end, executives want to be sure that there’s a significant return on their investment. Big data analytics assists in the upstream process as it can help to manage seismic data and determine the best drilling points.
From there, data can be used to optimize drill processes. Equipment is usually fitted with sensors which can collect data throughout operations and this can then be used to generate algorithms that identify usage patterns that are likely to result in breakdowns or equipment issue.
Finally, data can help to improve reservoir engineering as downhole sensors – whether they be temperature, acoustic, pressure etc. – produce insights that can be spun into reservoir management applications which increase visibility across the process.
Sensor analytics are vital to the transport oil and gas with the lowest risk possible as they ensure the safe logistics of the product. Predictive maintenance software can also be used at this juncture to analyze this sensor data to detect abnormalities and prevent accident.
The greatest boon of effective data practices where downstream is concerned pertaining to asset management. Big data analytics can reduce downtime and maintenance costs of the refining equipment by checking the performance of the tool, predicting its future and replacing where necessary.
According to a survey by Accenture and Microsoft from 2017, over the next few years, investment in big data and automation are expected to increase from 56% to 61% and 53% to 65%. Covid-19 has undoubtedly invalidated this study, and its possible that the shift to big data will be even greater. Executives can no longer afford to ignore reality.
Continue the debate at the NG Oil & Gas Summit NA, a GDS Summit, where we bring together senior Oil and Gas executives who are actively seeking to share, learn, engage, and find the best technology solutions.
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