This paper demonstrates the advantages and replicability of utilizing Distributed Fiber Optic Sensing (DFOS) combined with LYTT's customary machine learning, signal processing and interpretation tools in downhole oil and gas operations.
Paper Number: SPWLA-2022-0016
This paper describes a Middle East case study in which the challenge of big data generated by distributed fiber optic well monitoring systems was addressed through the use of LYTT’s sensing and analytics platform. The platform conducted intelligent feature extraction and enabled data to be streamed, processed, stored and visualized in real-time.
Paper Number: SPE-207848-MS
This paper demonstrates an innovative approach to processing Distributed Fiber Optic Sensing (DFOS) deployed to a gas condensate well.
Paper number: SPE-205435-MS
In the case study outlined in this paper, an innovative cloud-based injection monitoring application is deployed that uses DAS and DTS and overlays physics-informed machine learning models to deliver real-time visibility.
Paper Number: OTC-30982-MS
This paper summarizes the main findings from the first successful deployment of a continuous multiphase inflow profiling application that uses innovative signal processing techniques and machine learning that leverage distributed fiber optic data to identify the phase and rate of the inflow along the wellbore during production
Paper Number: SPE-201543-MS