VROC - AI for engineers, Case Study exploring the benefits for the Oil and Gas industry

The typical challenge in every plant and installation is that although they generate billions of data points, this is not converted into actionable insights for engineers.

Big data analytics consists of 4 key stages:

1. Collecting the data (data from different machines is stored in a single data warehouse/historian).

2. Cleaning of the data (deduplication – removal of duplicate data)

3. Analysis of the data to give key insights to engineers. Currently most systems offer condition- based monitoring – alerts when the machines behave outside the parameters.

4. Action on the key insights. From our team’s experience, we find that most of our clients and potential clients need to spend some time on 1 and 2 above to generate useful analysis in 3. We developed VROC to help engineers use those insights from the billions of data points.

The VROC platform analyses all the asset data (from all manufacturers – including binary data and process data). VROC AI provides:

1. Predictive analytics with Time to Failure (TTF) and Root Cause Analysis (RCA)

2. Process optimisation

This positively impacts safety, reliability, uptime, efficiency improvement and cost reduction. Many companies provide the software but require the client to engage other service providers to acquire the data from the field, install additional sensors (if required), and integrate other data sources across the value chain. VROC is data to dashboard. This means we bring the information you need straight to your devices with no large CAPEX investments.

VROC will integrate the systems for you and engineer a solution to effectively acquire the data from the field and across the value chain. In addition, VROC also offers asset reliability engineering services to determine actions to mitigate or eliminate equipment failures and process degradation.

VROC does not require a team of data scientists and analysts to interpret, process your data and create your models. Our data team consists of servers and artificial intelligence engines to provide you with the information you need, whenever and wherever you need it.

The insights delivered by the VROC AI platform enable our clients to make better asset and process management decisions.

The benefits include:

1. Increased asset uptime,

2. Increased productivity and revenues, avoiding unnecessary expenditure, and

3. Improved safety outcomes.

A reduction in planned and unplanned downtime and increased production especially applicable where processes cannot fail. VROC informs optimised management decisions to deliver improved financial performance.

Design just-in-time maintenance regimes to minimize downtime and reduce costs. Re-design processes to optimize production. The VROC platform gathers real time and historical data from monitored assets.

Using data points – which typically number in the tens of billions – it automatically builds analytics models using artificial intelligence and machine learning to provide insights and predictions.

Case Study Problem:

• Offshore platform $100M lost production revenue due to a reoccurring compressor failure • One 48-hour shutdown window available to rectify the problem

• Engineers and subject matter experts unsure exactly what the causes of the problem are and had little confidence in their plan to repair the equipment during the shutdown window downtime after the rectification works performed.

Solutions:

• One year of data from the platform provided to VROC • Client engineers performed standard reliability analysis taking 4,000 man-hours to identify the cause of the failure

• VROC processed the 12 months of data in 90 minutes and came to the same conclusions as the reliability engineers, producing the results 2,000x faster without requiring any subject matter expertise.

• VROC real time predictive maintenance implemented on the offshore platform to improve platform reliability and reduce unplanned

Decision-makers can use these insights to optimise operations and reduce cost.

Fact 
“Predictive maintenance program saves between 8% and 12% than a purely Preventative maintenance program” US Dept. of Energy 
 

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