Predictive analysis services
Data in the right hands can predict and shape the future. Our data scientists use predictive analytics to make sense of the huge volumes of data generated by our customers’ offshore systems so that we can identify hidden threats far in advance and reveal opportunities to improve performance.
A service, not software.
X-PAS™ is a predictive analysis service developed specifically to meet the needs of oil and gas operating companies and energy businesses. The service can be applied across a whole asset or specific production systems, such as the gas train, oil train, water, power generation and other utilities.
Data science + predictive technologies + oil and gas domain expertise.
The service brings together a unique blend of people and technology. Our team includes a number of exceptional data scientists, analysts, engineers and subject matter experts who use a range of predictive technologies to turn our customers' operational data into meaningful, actionable insights.
Get ahead of issues before they become problems.
We build bespoke predictive models that allow our team to continuously analyse our customers' data and provide them with operational insights and predictions, so they know what is likely to happen and when.
Operate safer, cleaner and more efficiently.
Our X-PAS™ predictive analysis service helps our customers to:
- Cut costs by reducing maintenance activity and manning levels; reducing existing monitoring services; and by reducing EU ETS costs
- Increase revenue by avoiding system trips and production losses; improving the uptime and start-up of production systems; and by solving problems and dynamically optimising the production process
- Meet compliance by fostering safer, more efficient operations; reducing emissions and environmental impact; and meeting industry regulations and standards
A proven track record
As an industry first and in recognition of our growing track record, our X-PAS™ predictive analysis service won the Oil & Gas UK award for Business Innovation and was shortlisted as a finalist in the Energy Institute Awards.