Improving asset reliability through predictive and prescriptive maintenance techniques is one of the key trends. These technologies have huge potential across the sector, offering a better alternative to the traditional calendar-based approach to asset maintenance.
With a predictive maintenance approach, the focus is on analysing issues known to cause a problem such as vibration in a pump, or compressor. Sonic monitors can be added to the device and when vibration exceeds a certain level, alerts can be sent to advise operators that remedial action is needed.Prescriptive analytics adds a new layer of sophistication to the methodology, moving it from a product-based to a broader process-based approach. Critically too, the approach also tells operators the root cause of the problem. It can inform them not only that the compressor is going to fail but also that its impending failure is directly linked to the leakage of liquid into the gas lines at a certain concentration, or even just a slow change in the pressure recorded, for example.
Over the course of 2019, we expect interest and excitement around this approach to continue gathering pace. We also anticipate significant demand for these kinds of predictive and prescriptive analytics tools across our core markets during 2019.Do you have any predictions for the refining and petrochemical sectors in the Middle East in 2019-2020?
The last two years have seen massive advancement in the desire and demand for true innovation and transformation in the region, adoption of emerging technologies is starting to happen at pace, and we are pleased to see the region advancing in this way. Supported and driven by government initiatives, we see growing demand for tools that drive asset performance management and that will be a key focus area for us during 2019/2020.
Across the oil and gas sector, the volume of connected data available to operators will continue to grow quickly. Much of this is being driven by the ongoing expansion of the Industrial Internet of Things (IIOT) market, which according to research firm, Markets and Markets, is expected to grow from $64bn in 2018 to $91.4bn by 2023, at a CAGR of 7.39% during that forecast period.Coupled with this, we are seeing rapid growth in machine learning, making insights about plant and equipment available faster to senior decision-makers; and in mobility, visualisation and analytics, providing simple interfaces and insight to data and models.
What are some challenges facing the industry at the moment?
Digital transformation is no longer simply a future option for oil and gas companies. It is an urgent necessity. With competition fierce, capital more scrutinised, prices volatile and trade wars taking place, operators cannot afford to be ‘leaving money on the table’, or throwing it away by encountering delays in bringing assets online, or seeing unplanned downtime of all assets dig into profits.
Today, data is inexpensive to collect, richer in context, and, in fact, exploding in quantity. But executives often have access to less than 10% of this data as it is often too complicated to support agile decision-making. In parallel, teams are often telling the executive team they need to recruit scarce data-scientists to make sense of it all.
By use of artificial intelligence, machine learning and multivariate analytics, further enabled by advances in computing speed and mobility, process companies can begin to address previously unsolvable issues, without turning themselves into technology experts.
They can start to generate deeper insights from data to drive digital transformation projects, to extend asset life; maximise return on capital employed and drive additional profit. The ability to detect patterns and opportunities across a business can save vast sums and create competitive advantage. Avoiding a compressor failure at a well site, gas pipeline, or oil refinery, through early detection and diagnosis, for example, can avoid downtime losses running into millions of dollars.Oil and gas companies are increasingly looking to embrace digitalisation. But, in doing so, they face a problem. How do they prioritise the multitude of available/possible digital initiatives without reducing the chance of success? How do they distinguish between science projects and compelling initiatives? How do they take the best advantage of their current staff? In short, where do they begin?
While they should never try to do everything at once, that does not have to mean going after small problems, or profit opportunities. In implementing new digital technologies, businesses can be pragmatic around a significant problem that represents major value.
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