Real-time sensor analytics for reliable reservoir monitoring

Hayley Everett
Lytt's fibre-enabled analytics application for flow profiling. Image via Lytt.

Lytt, an end-to-end sensor fusion analytics platform provider, has demonstrated the effectiveness of its innovative, fibre-enabled analytics application for flow profiling applied to production and injection wells in one of the largest offshore oil fields in the world.

The company’s new sensor analytics application was deployed to interpret point and distributed sensor data collected from a carbonate reservoir in the Middle East. The results highlight that fibre-based, real-time monitoring solutions which use the latest advances in machine learning (ML) can unlock greater operational visibility, improving reservoir management and providing early risk identification.

Çağrı Cerrahoğlu, Product Lead – Analytics, Lytt, said: “We are proud to have demonstrated our innovative flow profiling application – a first for the Middle East. The results from our latest project prove that Lytt can address systemic O&G operational challenges through advanced, ML applications. Lytt is committed to continuous innovation to deliver enhanced operational visibility and drive added value across the industry.”

Flow profiling is of paramount importance for accurate and reliable reservoir monitoring in production and injection wells to enable continued oil production. At present, flow profiling involves collecting data using traditional logging tools that measure flow rate and phase at each point in a well. This is a time-intensive process that comes with a host of challenges, including the erosion of wireline sensors from acid stimulation in injection wells and lengthy lead times to produce actionable data.

As part of this project, Lytt aimed to identify an alternative method that mitigates these challenges and avoids the need for a full suite of production logging tools. To do so, the company acquired two fibre optic data sets and processed them using its real-time sensor analytics application.

After an assessment of its performance, the Lytt application demonstrated accurate injection profiles, and the company’s ML model proved reliable for interpreting the point acoustic sensor data obtained from the production well.

According to the firm, it has targeted a critical industry need to reduce financial losses incurred from unscheduled production interruptions and asset damage. The development of a reliable ML model for production monitoring also marks an important milestone in the technological advancement of the industry, the company said.

The details of the technical paper, ‘Real-time Applications of Sensor Analytics for Production and Injection Profiling’ was presented at ADIPEC 2022, 31 October – 3 November. 

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