1. Improvements in asset utilisation will continue
Cloud-based storage, the Internet of Things (IoT), and artificial intelligence will be the main areas of data application in the natural resources sector over the next decade.
In fact, the growing number of applications for big data is so significant that a number of CEOs from major natural resources companies have cited leveraging data as their main priority in making further operational gains and optimising use of assets.
Case in point: In 2015, Australian oil and gas producer Woodside set up a team to harvest and interpret the masses of data generated by its operations in order to improve efficiency, safety and profits at its LNG operation in northwest Australia. One aspect of the firm’s technological push is an artificial intelligence system that is learning to augment the site-based LNG operations control, allowing Woodside to optimise daily plant performance. This combined effort has been key to Woodside reporting world class LNG plant availability at close to 99.8 percent.
Other companies are using big data analysis to review real time data feeds to both optimise maintenance scheduling and predict future plant and equipment breakdown – a metric known as ‘Time To Failure,’ or TTF. The result of these big data implementations is that resources firms can significantly enhance operational productivity, reduce unit operating costs and potentially reduce sustaining capital intensity.
And then there are robots. Many diversified miners are using robot trucks or driverless long-distance railway systems – programmed according to big data-based inputs – to automate parts of the production process, achieving labour and cost savings.
Woodside is currently working with NASA as part of its Robonaut program to develop robot functionality – innovations that can be applied to robots working in the extreme conditions of Woodside’s onshore and offshore processing facilities. This physical functionality can be enhanced by real time big data analysis and fed directly into the robot operating systems, assisting in all onsite problem solving.
2. The next frontier: Applying data to the exploration and marketing phases
In recent years, many natural resources giants have aggregated data to identify the quickest, safest, and most cost-effective ways to discover and extract minerals from the earth and pre-empt major hazard and risk events.
Some resources firms can now pinpoint which day in the year was their most productive, as well as manage the performance on a daily basis to try and outperform their top productivity level. As one of our clients put it: “We use data to ensure we play our best game everyday.”
Looking into the near future, resources companies will also apply big data analysis to the exploration phase. When scientists and engineers search for undiscovered deposits, they have to acquire, process and interpret massive data sets, including geological, geophysical and geochemical information derived from the targeted exploration area. The stakes are high: in order to find the proverbial needle in the haystack, natural resources firms need to rely on largely visual interpretations of these processed and complex data sets to decide if they will allocate scarce risk capital.
Developments in artificial intelligence will soon be able to summarise innumerable layers of data in mere seconds with a very low margin of error – augmenting human talent and intuition in ways that were previously unimaginable.
On the other side of the value chain, the marketing of resources is another area ripe for big data applications. Soon, resources firms will be able to better focus their marketing efforts and more efficiently identify the most appropriate customers, based on data-driven analyses of the exact chemical and physical properties of the commodities they are selling.