Continuous AI for explorers
Revolutionizing the oil and gas industry with AI-powered exploration and analysis.
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At Earthscan we provide innovative solutions for oil and gas companies to locate subsurface reserves 10X faster and precisely. Our cutting-edge technology allows for more accurate and efficient exploration, saving our clients both time and money.
The tool
Less effort
bigger outcomes
Subsurface asset operations
Unleashing AI from Labs to Production MetaDigital
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Discover how we help clients
to get the very last drop
With our cutting edge deep learning techniques oil companies now can accurately locate oil deposits in less time and with great precision both subsurface as well as off-shore
With our tool, the manual hours put in by Geoscientists reduce drastically, thereby increasing the productivity of the team.
Our state-of-the-art AI algorithm and advanced visualization tools help Geoscientists and engineers to identify production zones with improved confidence.
High-precision, robust, scalable algorithms are extensively trained on complex data carefully labeled by geoscience. The variability introduced in the models is convenient for identifying new insights.
Our in-house user-friendly, end-to-end continuous AI solutions help your team to bring new subsurface data and get insights 10X faster than our competitors.
Leverage the power of AI for decision-making across a wide range of applications and use cases, from subsurface reservoir estimation to predictive maintenance to risk assessment.
Team
Our Innovative Minds
Meet who is behind our game changing tool
Research & insights
The latest from our team
Beyond the wellbore: an AI feasibility note on lignin and bio-inspired materials
Most of EarthScan's published work sits in the wellbore. This research note covers a parallel thread — applying deep learning to the second-most-abundant biopolymer on Earth, the structurally complex feedstock that has been chronically under-utilised in the energy transition.
Hamiltonian Monte Carlo for reservoir characterisation
A webinar recap of Dr. Shib Sankar Ganguli's Bayesian-multivariate Hamiltonian Monte Carlo approach for estimating total organic carbon in shale reservoirs — and why uncertainty quantification matters more than point estimates when characterising unconventional plays.
AI in geology: transforming noisy curves into instant insights
How transformer-based deep learning — the same architecture family that powers ChatGPT and Vision Transformer — applies to one of the upstream industry's most stubborn problems: turning decades of scanned raster well-log images into clean, queryable digital curves. A condensed companion to the VeerNet AI whitepaper.
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Continuous AI for explorers
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