Products

Q-DOS

The Quality Distributed Optical Sensing
Data Management Platform

 

We have developed the Quality Distributed Optical Sensing (Q-DOS) software platform for our client Sensalytx.

 

Q-DOS is a digital platform for the visualisation, the analysis, and the interpretation of Distributed Optical Sensing (DOS) data, which addresses both Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) data. Thermal and acoustic readings are acquired along the whole length of the fibre optic cables, which behave as continuous sequences of sensors in a distributed monitoring network.
 
The Q-DOS platform has been conceived to visualise, analyse, and interpret DOS datasets from wells, in correlation with other well-relevant E&P datasets such as pressure, lithology, gamma rays, completion, inclination.

 


 

IDQI

 The Intelligent Data Quality Improver

 

Industries are facing challenges in the handling of ever-increasing quantities of data; the upstream Oil and Gas sector in particular is severely affected by this “data deluge” challenge and is at risk of failing to extract enough information from the most potentially valuable assets they actually own, namely exploration and production data.

So far, numerical data cleaning and curation have been considered as synonyms for numerical data quality improvement, but the latter is much more than simply addressing gaps, outliers, noise and bias in numerical datasets typically composed of time series of some kind.

HyperDap has launched the IDQI project as a joint funding initiative between the Company, OGIC and The Data Lab to addresses the issue of digital exploration and production data quality in the Oil and Gas industry. IDQI builds on top of novel data quality improvement knowledge and technologies devised by Hyperdap.

The guidance and expertise of project investigators from Computing Science at University of Aberdeen (UK) in AI, data science and numerical data analysis and interpretation brings a high technical and academic research standard to the project.

 

 Our project sponsors

 

OGIC

Supports and funds innovation in the Oil&Gas industry

 

                       

The Data Lab

Scotland’s innovation centre for data and AI

 

 
Our project collaborator
 
 

University of Aberdeen (UK)

 

Prof. Wamberto Vasconcelos, Computing Science

 Academic investigators

Dr. Ernesto Compatangelo, Computing Science

 


 

GWM

The Graphical Workflow Manager
 

The GWM has been developed to solve a recurrent problem in the industry which constantly produces huge amount of data and require to process it faster and effectively.

The software is a sleek and effective interface to connect process and calculations in one go in articulated workflows. Users can easily create sequences of process components and embed their own calculations in each component, each with standardises input and output interfaces. The GWM allows users to immediately visualise the result of their computational workflow and of each intermediate stage.