The LYTT LYVE™ industrial IoT platform combines intelligent feature extraction, unique pattern recognition algorithms and an intuitive user interface to deliver unprecedented operational visibility that empowers customers to drive portfolio-wide optimization.
We turn your sensor data into connected business insights within seconds, and send them anywhere in the world via a web-based, customized dashboard.
End-to-end, sensor fusion analytics platform that enhances operational decision-making.
Streaming, storing, and processing terabytes of data used to be expensive and time consuming. Not anymore. Our edge computing solution intelligently extracts the key features from sources like distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) data streams within seconds, reducing the volume of data by 1,000 times without compromising quality.
Our cloud-based data management tools make it quicker and easier to process large volumes of data, such as DAS and DTS. Our systems can also be integrated with other contextual data sets to show you the bigger picture. And since data is streamed straight to the cloud, you can access our tools and carry out real-time analysis from any location worldwide with an internet connection.
This is where the magic happens! Machine learning is the process of teaching computers to use data to make accurate predictions. At LYTT, we use a unique combination of machine learning and physics models to continuously transform raw sensor and time series data into actionable insights in a matter of seconds.
spotLYTT™ is our user-friendly data visualization dashboard. It takes the data gathered by our patented, field-proven analytics tools and turns it into contextualized insights via a single, intuitive dashboard. It gives you the freedom to visualize and analyze relevant data in real-time for faster, more informed decision making.
We know how precious your data is. That’s why our cloud-based platform comes with industry-leading security features, giving you the flexibility to grant access to multiple users anywhere in the world, safely.
Intelligently extracting data from complex sensor types and transforming it into contextualized insights has proven exceedingly difficult. LYTT has cracked the sensor fusion challenge and delivers a single, consistent operational view - keeping insights concise and connected.
Our pattern recognition and visualization tools draw on machine learning analytics, similar to those used in music recognition applications, to identify and extract relevant features from terabytes of data.
Just like a song has an individual, unique pattern, different fluids– such as water, oil and gas – have unique acoustic fingerprints. But in complex, high-value businesses, it’s not enough to simply ‘see’ the fingerprint, operators need to know why they’re seeing it in order to make informed choices.
That’s where our tried-and-tested hybrid analytics approach comes in.
LYTT combines the power of acoustic pattern recognition technology used in the music industry with AI-powered models to deliver significant value to the oil and gas sector and beyond.
We use physics models to teach our models the impact a set of known variables will have on the data. This helps identify hidden data patterns created by fingerprints under different conditions. In other words, we don’t just ‘see’ the patterns, we understand ‘why’ we’re seeing them.
It is unlike anything else on the market today.
As thought leaders, LYTT has published numerous papers highlighting our subject matter expertise and demonstrating our innovative and proven technology in action.
This paper demonstrates an innovative approach to processing Distributed Fiber Optic Sensing (DFOS) deployed to a gas condensate well.
Paper number: SPE-205435-MS
The paper describes the case of a critical injection North Sea well that developed sustained casing pressure in the B-annulus and required an investigation to understand the leak origin.
Paper Number: OTC-30930-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
In the case study outlined in this paper, the oil production rate was increased by 25% using a predictive pressure optimization workflow to minimize sand production in a horizontal, sand prone well.
Paper Number: SPWLA-2021-0001
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
The paper describes a case study of a well where persistent sustained casing pressure (SCP) in the A-annulus resulted in the well being shut in for a period of almost three years.
Paper Number: SPE-204450-MS
Within a comprehensive overview of the evolution of downhole surveillance in the customer's field, DAS sand detection is discussed as a new, real-time downhole sand monitoring technology.
Paper Number: SPWLA-2018-V59N4A6
DAS sand detection surveillance in the customer's field delivers substantial production and safety benefits.
Paper Number: SPE-188991-MS
This paper describes the application of fiber optic technology to identify and differentiate multiple downhole well integrity events such as tubing leaks, casing leaks, flow behind casing and overburden integrity.
Paper Number: SPE-203447-MS
Learn how LYTT’s unique technology is turning in-well fiber optic sensing data into actionable insights, making well production safer, faster and more efficient.
Distributed acoustic sensing (DAS) technology essentially turns a fiber optic cable into a long microphone, making it possible to track different sounds, such as oil, gas or sand moving in a well from the reservoir all the way to the surface.
However, DAS produces an extraordinary amount of data. For example, a single hydrocarbon well can generate 100 million data points every second. That’s like streaming 1,000 full movies every hour. Until now, it has been difficult for operators to convert this data into useful insights in a timely manner. But our data analytics solutions are changing all that.
Distributed temperature sensing (DTS) uses fiber optic cable to continuously record temperature. DTS is particularly useful in certain operational environments where temperature can affect operational decisions.
DTS has proven challenging to operators, as it has traditionally taken them a long time to interpret. Our data analytics tools and solutions make light work of this, collecting, processing and analysing DTS in real time.