Benefits at a glance

Improve planning with Auto AI

Successful AI prediction, with two days lead time to events

Asset Manager

Client can reduce manhours required to perform frequent filter change outs

No code AI is faster than conventional problem solving approach

Client can avoid well deferment and production delays from unplanned events

The Challenge

VROC’s customer, an offshore oil and gas platform in South East Asia recently implemented OPUS. During the initial team training, the Operation Supervisor framed a challenge that was occurring on the platform, wanting to explore if AI could help predict the events.

The Produced Water Filter (40microns) located downstream of the skimmer, was frequently clogging due to sand production. The event had occurred at an average of five times per month. The sanding clogging events caused well deferment, along with an increase to manhours to repeatedly change out filters, which under normal operation are changed out every two months. The events also have resulted in a decrease of the filter inventory on site.

As a critical gas hub, this platform has been prioritised for the deployment of AI, as it has real time data availability and some pre-existing challenges.

Examples of Produced Water Filter clogging events in oil and gas

The Solution

VROC trained a Time to Failure AI model to learn the patterns and other parameters that lead to the clogging event, so that it can predict in advance the events, and disruptions can be avoided.

The model was trained on the last few months of historical data where the filter was frequently clogged, and the model was put into production at the end of July 2022. Previously the team had no lead time to clogging events. The initial goal was to predict the events 24 hours in advance.

The Results

An AI model was trained to learn underlying patterns leading to each sand clogging event by using some of the available subsea parameters. The model calculates the probability of an event occurring in the future at each point in time.

The models are continually retrained with new data to learn new behaviours and patterns so that the accuracy can continue to improve, and the lead time can be extended.

The models are now consistently alerting the team 1-2 days in advance of a clogging event, and with these reliable insights the team is able to plan interventions.

This successful catch has been selected as VROC’s Catch of the Month. It is a great example of using operational data and AI to assist with day-to-day operational challenges, helping to optimize production.

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