Use cases for our AI-driven geoscience software

Liberate your geoscience data
and innovate your workflows with AI

EarthNET lets you utilise all available data to gain a more complete geological understanding and optimise your basis for decision-making related to drilling exploration wells, optimising production, developing CO2 storage sites, and placement of wind farms. Our powerful AI technology enables you to uncover hidden insights in your industrial data, identifying trends, patterns, and correlations that were previously impossible to see.

TOUCH

Data ingestion to the OSDU™ Data Platform

Data ingestion to the OSDU™ Data PlatformMore about this use case

TOUCH

CCS site screening

CCS site screeningMore about this use case

TOUCH

CCS site characterisation and de-risking

CCS site characterisation and de-riskingMore about this use case

TOUCH

Predicting overlooked hydrocarbons

Predicting overlooked hydrocarbonsMore about this use case

TOUCH

Optimising production of hydrocarbons

Optimising production of hydrocarbonsMore about this use case

TOUCH

Offshore wind placement

Offshore wind placementMore about this use case

OUR SOLUTIONS

Driving better business decisions

We are dedicated to helping our clients unlock the full potential of their subsurface data through our innovative suite of tools and services.

Through the EarthNET Suite, we provide advanced data analytics, visualisation, and workflow automation capabilities to help you make sense of your data and derive valuable insights.

In addition, we offer consulting services to help you optimise your workflows and achieve your business objectives, as well as multi-client products to provide you with the data you need to drive your decision-making.

INDUSTRIES

Providing data and AI solutions for oil & gas, CCS and renewable energy companies

Oil and gas companies and companies working with CCS and renewables have one thing in common – they need trustworthy data and information about the geological complexity of the seabed and subsurface. Collecting the data, curating it, making it accessible, and interpreting it accurately and efficiently are common challenges. These are time-consuming and complicated processes and exposed to human errors.