LONDON — LYTT has deployed a new fiber-enabled, sensor analytics system for flow profiling of production and injection wells to a large offshore oil field in the Middle East.
The application was designed to interpret point and distributed sensor data from a carbonate reservoir.
According to the company, results demonstrated that fiber-based, real-time monitoring, incorporating machine learning, can deliver improved operational visibility, reservoir management and early risk identification.
Conventional flow profiling involves compiling data using logging tools that measure flow rate and phase at each point in a well. But this process can lead to erosion of wireline sensors from acid stimulation in injection wells and long lead times to generate actionable data.
For the Middle East project, LYTT aimed to identify an alternative method, acquiring two fiber-optic datasets and processing them using its real-time sensor analytics application.
The application is said to have demonstrated accurate injection profiles, with LYTT's model proving to be reliable for interpreting the point acoustic sensor data obtained from the production well.