Focus July 2020

“We initially began managing that data through a data centre so that we could create fleet-wide status reports for our operator clients. “But, as the data analysis and consulting side of the business grew, we realised that we needed to change our business model to provide higher-value services to our customers and make our delivery more efficient. So, we moved our services to the cloud and developed a proprietary data analytics platform that we call BMT Deep TM . “This digital platform allows us to provide operational insights and generate data quality assurance and other detailed reports in near real-time. Accordingly, we’ve moved away from the unit pricing of reports to a subscription model, driving costs down for our customers. “Our analysts, data scientists and customers can now focus on mining the data to look for long-term trends, which can be anything from seabed subsidence to structural fatigue. It’s this high-value work that drives efficiencies and cost savings. “But, for these ‘big data’ analytics to be useful, it’s important to have a high degree of confidence in the base data sets, so we’re very focused on addressing the issue of long-term data quality and making sure that we organise these multi-year data sets efficiently. “In recent years, we’ve adjusted our internal instrumentation systems engineering processes to include data traceability analysis. “This allows us to trace processed data variables back to our instruments, providing a fully audited data trail. We’ve also included real-time automated quality assurance (QA) on our local acquisition system software. “This gives operators an early indication of a sensor malfunction so that they can pre-emptively schedule maintenance, thus reducing downtime and risk. In addition to automated data QA checks, we’ve added numerous additional features to BMT Deep TM over time, such as custom analytics. For example, for one major operator in the North Sea, we’re developing a set of applications that automate hull fatigue analysis and deliver reports that can be accessed directly by local regulatory authorities.” So, is BMT Deep TM an IoT data analytics platform, then? “An interesting question. BMT Deep TM can be described as a data management platform that provides customers with a digital replica (or twin, if you like), of their assets, whatever those assets might be. “We do this by applying data fusion and machine learning principles to sensor data and converting this into graphics. “This gives engineers and operations managers a complete virtual and interactive picture of how their assets are performing in real-time and flags up where pre-emptive actions can be scheduled to reduce downtime or increase safety. The same principles apply whether the asset is an oil rig, an industrial plant, a vessel or a piece of mining machinery. “Some commercial data analytics platforms can either be somewhat generalist, or in the energy sector, they may focus on something very specific, such as optimising production, so the tool monitors control data, such as temperature, flow of oil or pump pressure. “What our engineers have understood is how critical asset design and performance is across a number of sectors, and they have embedded these specialist insights into the BMT Deep TM platform to provide a fully integrated whole-of-life picture to the operation of the asset. “BMT Deep TM was designed by engineers who have been analysing industrial sensor data on behalf of other engineers and operations managers for many years so the dashboards and graphics are very intuitive for these end users, they are also interactive and customisable, allowing them to drill down into each dataset. “This is important, because not everyone who has access to data may know how to process, clean and organise that data in ways that make sense, and not everyone who may be asked to look at a dataset will understand what they should be looking for within the data or even how to interpret that data to add value and save costs. “Adding structure and highlighting the value of this data is precisely where we can help, relieving the end user’s frustration at having to filter large data sets to find the event or behaviour of the asset that they’re most interested in. “Let’s take a large asset, like a Floating Production Storage Offshore (FPSO) platform that is located out at sea. During a storm event, the asset may suffer damage. An unexpected event, such as a large wave, may impact the platform and break a mooring line. “Without data on the asset’s behaviour, this may go undetected and to understand more about what has happened, knowledge about how the onboard sensors have been configured, the sampling rates and the synchronisation of their in-built clocks will be critical to forensically investigate what has caused the damage.” Andy Aldrich, BMT It’s our understanding of data-centric engineering that makes what we do unique in this field and BMT Deep TM is our platform of choice because it’s a game-changer. “ “ 11 10 Cont’d