CAse study: Q-dos
Dataset visualisation, analysis and interpretation using Artificial Intelligence
We developed the Quality Distributed Optical Sensing (Q-DOS) software platform for our client Sensalytx for use by a confidential major international energy company to visualise and analyse a large oil & gas well Distributed Optical Sensing (DOS) data set. The Q-DOS software platform helped to characterise production efficiency and the changing down-hole conditions by visualising and analysing historical and up to date data.
The Q-DOS digital platform enables visualisation, analysis, and interpretation of DOS data, which addresses both Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) values. Thermal and acoustic readings are acquired along the whole length of the fibre optic cables, often over several kilometres, which behave as continuous sequences of sensors in a distributed monitoring network. Q-DOS allowed correlation with other well-relevant datasets such as pressure, lithology, gamma rays, completion and inclination.
The fundamental approach of Q-DOS is applicable to optical fibre sensing applications in many other industries and in a variety of different contexts.
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