Benefits at a Glance

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US$2.2M Reduction in Production Loss across Two Plants

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$1.4M Savings due to Reduced Downtime

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83% Reduction in Production Loss

The Problem

Late-life assets challenge the balance of maintaining uptime and asset integrity while meeting regulatory compliance and production targets. This challenge needs to be met by the operators and asset owners while managing their OPEX to maintain profitability.

VROC was engaged by an operator to use our AI Platform to remotely monitor the health of their offshore platforms in the North Sea to better understand reliability issues.

During the project period, due to increased restrictions caused by the pandemic, only personnel critical to the performance of the asset could go onsite. This meant that among others, Reliability Engineers were restricted to working remotely, and would normally have no direct visibility of asset performance. Maintenance crew not directly related to requirements offshore were also subject to these restrictions which would have potentially effected asset performance.

The Solution

Within a three-week period, VROC was able to ingest the historical data and start downloading live data from their historian through a secure data connection.

After a week’s training on building models and dashboards and with support from VROC data scientists and engineers, the operator’s key asset performance and reliability engineers were able to deploy AI models and visualisation dashboards on critical assets e.g., compressors, generators, and water injection pumps to monitor their health and performance. These graphic representations of asset health, with the ability to drill down to find contributing factors and root causes for any significant deviations from normal operations, helped in the early identification of future problems.

The Outcome

Over a nine month proof of concept, the client experienced significant benefits and return on investment which are highlighted below.

PERSONNEL
Reliability personnel working remotely due to COVID-19 lockdown were able to track asset health in real time and put in place mitigation strategies to avoid shutdowns.

MAINTENANCE
Maintenance was able to be reorganised to minimise firefighting and transition to a risk based approach based on predicted failures, allowing better shutdown planning

OPTIMISATION
As a result of better visibility of their equipment, the operator was able to focus on optimising individual processes using complex models

OPEX REDUCTION
Direct and immediate cost savings have come from having fewer personnel on board (POB) and reduced maintenance; however, the significant savings have resulted from the mitigation of failures and optimisation of processes.
Plant 1 US$1.8M reduction in production loss, $1.4M savings due to reduced downtime, 83% reduction in production loss
Plant 2 US$431,000 reduction in production loss, 80% reduction in production loss

As these examples illustrate, significant benefits that have been directly derived by the late-life asset operator during one of the most challenging years for the oil and gas industry. The direct correlation with a reduction in OPEX is evident and often immediate with the deployment of the VROC machine learning modelling techniques on our AI platform.

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