In the current data-driven environment, the ability to identify patterns is essential across industries. Acoustic analysis, driven by the increasing data from Distributed Fiber Optic Sensing (DFOS), is one such area.
Central to DFOS is Distributed Acoustic Sensing (DAS), which has its challenges. Those familiar with DAS understand its main issue: the volume of data. A single DAS instrument produces nearly a terabyte of acoustic data per hour.
Even after reducing this volume by around 40% using lossless compression, managing the data remains a challenge. The answer isn't in further compression but in extracting the essential statistical features that describe the full acoustic spectrum.
LYTT offers a solution in selective feature extraction. We extract relevant features from large DAS datasets in real-time. Using field and lab data, as well as additional well sensor information, we utilize software in high-performance 'edge' servers, or 'real-time processing units' (RTPUs). These units are located at well sites, working with DAS and DTS (Distributed Temperature Sensing) systems.
Think of it this way: just as music genres have a distinctive rhythm or beat, common operations have recognizable audio signatures. Drawing from field data and lab experiments, we've identified these signatures.
Our patented data processing approach reduces the risk of drowning in data, ensuring the integrity of real-time data. As a result, operators can analyze and make decisions efficiently, with a significant reduction in DAS data volume.
Our system's strength is its foundation on over five years of continuous well production data and a comprehensive library of lab data. By filtering out unnecessary 'noise,' we provide accurate real-time well condition monitoring.
Our algorithm integrates DTS data, additional well sensor data, and lab data. This combination offers valuable real-time insights.
In essence, LYTT's technology allows industries to bypass the overwhelming data from fiber optic sensors, focusing solely on the most pertinent analytics. These insights are generated quickly, aiding operational decisions across various sectors, not just oil and gas.
Our feature extraction method is proven and operates consistently across energy assets. Its application isn't limited to the oil and gas sector; it's also used in other industrial processes where acoustic sensor insights are essential.
But there's more. Feature extraction is a fundamental component of LYTT's cloud-based AI platform. As we extract essential features at the edge, machine learning comes into play, revealing insights that guide our clients' decision-making.
In a world overwhelmed by data, LYTT stands out as a beacon of efficient data extraction, showcasing the transformative power of innovation in reshaping industries.
