Platform

LYTT’s Industrial AI Platform

Generate. Accelerate. Monetize.

PLATFORM OVERVIEW

PLATFORM FEATURES

Platform Core

Central to LYTT’s standard platform offering, our platform core: stores historical and real-time data collected from IoT devices and high-fidelity sensors, integrates data from 3rd-party and EDGE data sources and allows users to search, retrieve, and visualise data sets. Our platform core also integrates with LYTT sensor fusion and model deployment features is highly scalable to meet changing demands, and resilient, which ensures high availability. Deploy across multiple clouds and on-prem.

Sensor fusion analytics

Combine data from multiple sensors and sources to provide a more accurate and complete understanding of your systems and processes. Utilize sensor fusion to enable highly accurate and reliable model predictions and unlock more powerful insights.

Third-party model deployment

LYTT's platform allows customers to deploy their existing, pre-trained models on top of the Platform Core. Realize cost savings and decrease time to market by utilizing the LYTT Platform’s existing model deployment capabilities.

Third-party model development

Complete the data science life cycle on top of the LYTT's AI platform. Access training data, build and deploy models within LYTT's integrated data science environment. The fastest way to move from initial data exploration to deploying production-ready models with actionable insights for high-fidelity sensors.

Third-party software integration

Enables users to extract more value from their IoT data by integrating software applications or services developed by third-party vendors into LYTT's AI platform. This includes applications and services related to data analysis, visualization, digital twins, and asset automation.

Self-learning models

Improve model accuracy and strengthen performance with continual learning. Automatically feed manually labelled data back into your machine learning pipeline to retrain models and improve predictive accuracy.

PLATFORM COMPONENTS

LYTT EDGE

LYTT’s 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

spotLYTT™ visualization

spotLYTT™ is LYTT Platform’s bespoke 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.

Connector library

Choose from a range of pre-built connectors to quickly and easily connect IoT applications and devices with LYTT's AI platform. Integrate with a wide range of external software systems for data analysis and visualization. Feed data and insights from the platform into digital twins and asset automation systems.

LYTT Platform data pipelines

Real-time processing engine. Respond to events in real-time. Route sensors from multiple different sources to the appropriate models.

PAPERS

As thought leaders, LYTT has published numerous papers highlighting our subject matter expertise and demonstrating our innovative and proven technology in action.

Flow diagnostics in high rate gas condensate well using distributed fiber-optic sensing and its validation with conventional production log

This paper demonstrates an innovative approach to processing Distributed Fiber Optic Sensing (DFOS) deployed to a gas condensate well.

Paper number: SPE-205435-MS

Well integrity diagnostics using acoustic event classification on distributed acoustic sensing data

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

Real-time water injection monitoring with distributed fiber optics using physics-informed machine learning

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

Production optimization of sanding horizontal wells using a DAS sand monitoring system

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

Production optimization using a 24/7 distributed fiber optic DFO sensing based multiphase inflow profiling capability

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

Using distributed fiber optic sensing to recover well integrity and restore production

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

20 years of downhole surveillance history

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

Application of distributed acoustic sensing DAS technology in identification and remediation of sand producing zones in OHGP completion

DAS sand detection surveillance in the customer's field delivers substantial production and safety benefits.

Paper Number: SPE-188991-MS

Downhole sand ingress detection using fibre-optic distributed acoustic sensors

A new DAS solution identifies real-time downhole sand ingress locations across a reservoir.

Paper Number: SPE-183329-MS

Well integrity flow detection using novel acoustic pattern recognition algorithms

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

Raising recovery rates in real-time using in-well fiber optic sensing

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.

SEE ALL PAPERS

FIBER OPTIC COMPANION TECHNOLOGIES

Distributed acoustic sensing

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

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.