Why integrity data analytics is key to developing vital new ways of working.

Faced with a continued pressure on oil price, manning restriction due to COVID 19 and no way of knowing how long this will all last; integrity, operations and asset managers are having to quickly find sustainable new ways of working to ensure continued safe, profitable operations. With a multitude of difficult decisions to be made and ever more limited resources, many are defining the optimum course of action by making use of both data already collected and the rapidly changing data gathered every day.

How do you navigate in an ocean of data?

Back in 2014, The HSE report on Asset Ageing and Life Extension (KP4) noted; “The KP4 programme found a need for better data collection and trending to improve predictions of equipment residual life to support ALE decision-making, and to facilitate proactive equipment maintenance”. Since 2014, the industry has certainly taken great strides in data collection, with drones collecting visual data and inspectors using tablet reporting all now quite normal. Additionally, as sensor data starts to pour in, the volume of data stored has become vast. This shouldn’t really be a surprise, data collection is what the industry already did: Images were gathered, readings taken, reports written and databases populated (sometimes) and as technology improved more images were gathered and more readings were taken and stored, faster and more accurately than ever before. However, unlike data collection, data analytics wasn’t previously part of day to day operations and until relatively recently remained far less familiar. Consequently, knowing how to improve trending and prediction was much less obvious. Nevertheless, as the concept of analytics becomes something that people are comfortable with and the benefits are realised across different sectors, it’s clear the adoption of analytics isn’t something that should happen over time but something that must happen now.

Addressing complexity to create efficiency.

Due to the way modern inspection, reporting and data storage technologies have been “organically” adopted over time; things have, inadvertently, been made more complex. Integrity related data is often relatively unstructured and stored in many different formats and locations making it difficult for engineers to have a ‘full picture’ of the integrity of an asset’s components. This has an impact on the decision making and risk management processes related to inspection, maintenance and integrity management. Integrity related data may be held in multiple databases, Excel spreadsheets, PI, and historical reports with varying levels of quality and accuracy. It is therefore difficult (or even impossible) for an engineer, using conventional means, to view all relevant data in a manner which allows even basic correlation and trending to support decisions relating to integrity and direct resource appropriately.

Without the tools to sort, cleanse, analyse and visualise data, many integrity departments have been left with no choice but do their best to collate and relate this data manually. Trying to cope with the sheer scale of this task is no doubt the biggest obstacle to interpretation. It can frequently result in the omission of relevant information, as there is often no unified framework for viewing the data. Despite these difficulties and limitations, every effort still must be made to make best use of what’s available. However, in addition to being somewhat ineffective, manual effort is time-consuming and costly. For some large assets, annual onshore integrity costs may be in excess of £1m, with potentially 40% of this cost attributed to managing the data that’s been gathered.

Because of these challenges, business and safety-critical decisions may be made with incomplete, incorrect, or poorly understood data.

Ultimately meaning:

• Failure patterns and genuine anomalies are not identified, leading to preventable failures.
• Data trending remains the goal but not the reality.
• Inspections are carried out which do not add value, incurring costs and reducing the resources available for higher priority scopes.

So, what’s changed?

Advanced analytics using new statistical methods and rapidly improving computing power to
spot patterns among thousands of variables in continual changing process conditions are now becoming more accepted across the industry.
In areas such as exploration & drilling, reliability and even shipping & transportation, the use of analytics to improve efficiency and profitability are becoming common.

At IMRANDD we’ve built our operations and service around the belief that integrity management should be no different. We understood that advanced analytics are particularly effective in environments that involve huge amounts of data and highly complex and variable operating conditions—that is, the same environment that integrity departments must contend with every day. Since our foundation in 2016 we’ve pioneered the use of data science in integrity management bringing completely new approaches and tools to the market, including AIDA© our Advanced Integrity Data Analytics software which helps cleanse, condition, trend and visualise inspection data in way that previously simply hasn’t been possible.

With the majority of our team having an Oil & Gas operator background we’ve leveraged our extensive domain knowledge to ensure that we use our analytics capability to develop tools that clearly add value and are easily deployed, providing unique solutions to common problems. Building on our analytics tools we’ve created a planning and resource optimisation solution that considers budgetary and physical constraints. We’ve also developed ‘RBI+’, our software that ensures knowledge management and auditability are built into the RBI process.
Meeting the demands of today while preparing for tomorrow.

In recent years we’ve successfully proven that analytics can be used effectively to better understand ageing, changing processes and degradation patterns across an asset, alerting operators to potential threats they may not know exist now, as well as those that will arise months and years into the future. In turn, this insight has been used to respond effectively, optimising plans for both today’s constraints and tomorrows challenges.