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Oil & Gas

Cut downtime with innovative AI solution that guarantees ROI within one year

Key facts

50%

downtime reduction (up to)

40%

lower maintenance costs (up to)

85%

increase in downtime forecasting (up to)

55%

increased productivity (up to)

Next Steps
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Top business benefits

Reduce costly unplanned downtime by up to 50%

Free up valuable time for skilled engineers to focus on what matters

Gain the support of proven condition monitoring experts

Scale easily and optimise all machine operations

Achieve a typical ROI of less than three months

Revenues are hit hard by expensive unplanned downtime across the oil and gas industry every year. This innovative predictive maintenance solution can reduce unplanned downtime up 50% and guarantees ROI within twelve months. 

In more detail

Full description

Unplanned downtime can cost in excess of $10m per hour, presenting a constant challenge in the oil and gas industry as companies operate extensive arrays of mission critical machinery under huge pressure. Much of this machinery has to operate constantly, and this availability requirement makes malfunctions and costly downtime very hard to forecast with traditional means. The result of this is revenues suffering continuously.

This AI-based predictive maintenance solution provides engineers and managers with powerful real-time insights and significantly reduces downtime. The effectiveness of the solution is supported by the guarantee that ROI will be achieved in twelve-months or less, with the agreement backed by a leading global reinsurance organisation (SCOR). In the event that this target is not achieved, a full refund of your software licence investment would be issued.

To get up and running and start making an impact, this unique solution only requires less than one month of existing or new data to begin automatically generating powerful machine-health insights and already connects with leading data storage systems such as OSI PI, a defining quality above other predictive maintenance systems. Its ability to work with a relatively small amount of data makes it highly accessible, enabling organisations to rapidly enhance their predictive maintenance approaches without generating vast data sets in advance. As part of the solution, expertise and training will be provided to ensure that existing workforces are using the software effectively and getting the right results. This process negates the need to bring new technical talent on board and promotes fast, positive progress.

The challenge

Oil and gas operators rely on the machine-health of critical assets like cranes, pumps and turbines. When malfunctions strike without warning, extensive periods of costly downtime begin as productivity is stopped or slowed significantly. When operating vast arrays of critical machinery with no means of predicting maintenance needs, companies lose millions of dollars every year, not to mention the potential safety and contractual issues caused by lack of equipment availability.

Many organisations are collecting a sufficient amount of machine data but failing to analyse it and generate actionable insights. When data use is not optimised, value is lost as engineers and staff are forced to be reactive rather than proactive. Without foresight of maintenance needs, resources are routinely stretched thin in a rush to get systems back up and running, impacting both efficiency and productivity.

In addition to the immediate revenue challenges presented by unplanned downtime, unexpected machine malfunctions cause other delayed issues. A prime example is a reduction in the lifetime of assets, with engineers unable to take pre-emptive action on smaller faults that could become more serious and critical to machine-health in the future.

A further factor adding pain to this set of challenges is the reality that many predictive maintenance solutions come with significant barriers to entry. As well as often causing extensive downtime in the installation phase, many solutions require months or even years of data and bedding-in time before accurate projections are generated and tweaked manually. This approach is highly arduous and prohibitive for large organisations with targets to hit and even organisations with existing predictive maintenance solutions already in place experience challenges in quickly and accurately predicting maintenance cycles.

The approach

At the start of the process, the organisation will work with you to set up the ROI guarantee under the oversight of a tier one reinsurance provider (SCOR), there is no additional cost for this. Once the agreement is in place, the solution begins to achieve rapid impact on your business with an agile approach to predictive maintenance, collecting and analysing existing data and generating deep machine-health insights automatically.

The intuitive AI tool integrates seamlessly into existing infrastructure, without requiring additional investment or external data science talent. A variety of different types of data are captured by the solution to generate meaningful insights. One type is conditioning monitoring information from sensors connected to machines that operate continuously, with another example being maintenance data from enterprise asset management systems.

The solution then analyses the data and automatically identifies anomalies, trends, patterns and predictions relating to a wide range of known and unknown failure modes, using data sources such as vibration, pressure, torque and current. By collecting and continuously analysing condition monitoring data such as these, the system alerts engineers in advance of malfunctions and creates a targeted, predictive and prescriptive approach to maintenance.

Engineers can access these data insights and alerts via an easy-to-use interface on their mobile devices, making it simple for staff on the ground to enhance their daily operations without the solution getting in the way. This ease of use is further enhanced through direct support from the organisation, working alongside users to become proficient in applying the solution to their individual roles.

Developed to be highly scalable, the solution is powered by a secure and regularly independently audited cloud-based architecture, enabling it to monitor and collect data from tens of thousands of critical assets. This provides the user with the opportunity to globally optimise maintenance approaches across an entire organisation. The solution is designed with the ability to seamlessly integrate with existing data collection systems and sensors, or new sensors can be purchased using our network of partners, with accurate results within less than one month from installation.

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