In a previous blog, I looked at how effective content management is an important factor driving operational excellence for oil and gas companies. Your team need access to the right information at the right time in the right format. However, in today’s Big Data world, the combination of Artificial Intelligence (AI) and analytics is essential to improving operational decision-making. In this post, I’d like to look at the rise of AI-powered analytics and its growing role in operational excellence.
With crude oil prices rising steadily over the past few years, the oil and gas industry’s focus on operational excellence looks set to pay dividends. If companies can continue to drive cost and inefficiency from their operations, it should lead to greater profitability. However, as always, things are rarely so clean cut. Bain & Co point out that factors such as lower prices, technological disruption, and increased regulatory complexity have caused the global profit pool to shrink by 60%.
Even so, some international oil companies were confident enough to inform Bain that they expected capital expenditures (capex) to increase by 15% through 2018. Market conditions may be improving, but the outlook has to remain volatile, so a relentless focus on excellence in all aspects of operations remains critical. The results, according to EY, can be impressive. Operational excellence can lead to a 29% rise in oil and gas production, reduce costs by 43%, and deliver savings of $30 billion over five years.
Analytics everywhere – especially operations
Oil and gas production creates a lot of data, especially with the increasing deployment of Internet of Things (IoT) devices. However, this data is not fully exploited. In fact, it is very underutilized. There is the often-cited finding from McKinsey that, after studying sensor data from rigs around the world, only 1% of data from over 30,000 data points was made available to inform operational decisions. Bain suggests that better data analytics could boost production up to 8% for oil and gas companies.
AI and advanced analytics have become key enablers for CIOs in almost every facet of business in almost every industry—and operational excellence is no exception. A recent survey found that three of the top five technology priorities for achieving operational excellence were related to AI and analytics: AI and RPA, data analytics, and real-time analytics.
This is reflected in the oil and gas industry where six out of 10 predictions in IDC’s Futurescape involved AI and analytics, including the expectation that by 2019, to overcome production data silos, 75% of all companies will be able to apply analytics to track hydrocarbon production to the well level. In this way, AI-powered analytics can deliver one of the promised benefits of operational excellence. It can break down silos and connect data sources to deliver accurate information, enabling people to make better, safer, faster, and smarter decisions.
Predictive maintenance – an illuminating use case
McKinsey’s survey uncovered a very important piece of information. When they looked at production in the North Sea, it found that, on average, rigs were running at 82% of capacity. Considering the target figure should be around 95%, this is extremely bad news for operators. The reason for this poor performance was unplanned downtime and maintenance.
Too often, operators were connecting IoT sensors to production equipment but only using them to monitor and affect their current operations. The data was not being fed into an analytics solution that would allow patterns and trends to be identified, alerting the operations staff before an issue arose.
AI-power analytics solutions, such as OpenText™ AI & Analytics, deliver a solid platform for predictive maintenance. The data from IoT sensors on one rig can be combined with data from sensors across all operations and other production systems to enable predictive models that quickly identify potential issues. The solution can be automated across the entire process so that it immediately orders the required parts and schedules the work when physical maintenance is required.
This significantly reduces downtime. Forbes suggests that almost half of offshore equipment is at least 15 years old and has 13% downtime. Predictive maintenance driven by AI-powered analytics can yield astonishing results. The analysts cite one example where production was optimized across 500 rigs a million times faster than it would have been possible without the technology.
AI-powered analytics and operational excellence
We’re still early in the adoption of AI-powered analytics solutions, but I believe that operational excellence is an area where the technology will deliver incredible benefits. It will allow you to understand in real-time exactly what is happening across all your operations and have the information to hand–at every level of operations–to make the best decisions on how to maximize production as well as identify new opportunities and ways of working.
Just one final example: drones are one of the disruptive technologies entering operational business processes in the oil and gas industry. They offer the ability to improve the monitoring and maintenance of plants and assets in each stage of the oil refining process. The data gathered and analyzed from drones reduces the cost and improves the quality of maintenance decisions.
I’ll be covering drones in more detail in my next blog.
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