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In our AI labs, we're innovating to revolutionize subsurface geology, aiming to build an actionable, results-driven AI to unlock Earth's resources and optimize drilling success. Dive into our groundbreaking research and business insights here.

From 40-Fold to 4,000-Fold: How Data Density Is Reshaping Geophysics and Machine Learning

Seismic data fold has jumped from ~40 to over 4,000 in two decades — a hundred-fold increase that is fundamentally changing what machine learning can do in subsurface science, shifting the human role from execution to lateral thinking, and opening frontiers beyond Earth.

Basin-Targeted Seismic Foundation Models: Why Region-Specific Pretraining Wins

A family of masked-autoencoder foundation models pretrained on ~30 TB of Norwegian Continental Shelf seismic data outperforms generic vision models and a globally pretrained seismic baseline across four interpretation benchmarks — with the 2.5D multi-view variant emerging as the practical default.

The 2026 Reset: Why Subsurface AI Is Now a Survival Layer, Not a Pilot

The 2026 oil and gas downturn is a permanent recalibration of the human-to-machine ratio. Operators who digitalised before the workforce cuts are already pulling ahead — and the gap is structural, not cyclical.

Seismic Foundation Models: The CDO's New Edge

Operators spend $2.4B/year on subsurface studies that take weeks. Seismic foundation models compress that to hours — and put the CDO in charge.

Agentic CCS: Months of Site Screening in Minutes

A five-agent orchestrated loop screens 10 synthetic North Sea / Gulf Coast wells against CCS reservoir-caprock criteria in under 2 seconds.

Why the Next Energy Majors Will Be AI Infrastructure Operators

Most energy AI pilots never reach production — and the reason isn't the data science, it's that nobody owns the compute. The operators that own their HPC layer, their data, and their models will define the next decade of upstream competition.

GeoBFDT: A Transformer for Automated Bedding and Fracture Detection in Borehole Image Logs

A research note on GeoBFDT, a DETR-style transformer trained on FMI and CMI logs from 14 Omani wells, that detects fractures and bedding planes end-to-end — predicting depth, dip, and azimuth without mask supervision.

Oil & Gas in 2026: From AI Customer to AI Builder

In 2026, oil and gas stopped buying AI off the shelf and started building it — sovereign LLMs trained on decades of subsurface data, agentic stacks replacing upstream workflows, and modular data centers next to refineries. The same industry is now the power supply for the hyperscaler buildout. Two flows of capital, one tape.

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.

Gas-to-Grid-to-GPU: Oil & Gas as AI Infrastructure

How natural gas producers can pivot from fuel supplier to full-stack AI infrastructure operator — owning the power, the data center, and the revenue stream.

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.

Optimisers and learning-rate schedulers: a practical deep dive

Why the choice of optimiser (SGD, Adam, RMSprop) and the shape of your learning-rate schedule (step decay, exponential, cyclic) often matter more than another 1% of model capacity. A webinar recap, with the live optimiser-trajectory plot we built to make these trade-offs visible.

Seismic facies analysis with deep domain adaptation — IEEE TGRS 2022

Published in IEEE Transactions on Geoscience and Remote Sensing (60, 1–16, 2022, Art no. 4508116). EarthAdaptNet + CORAL — a deep-learning architecture that classifies seismic facies in a target basin using only labels from a source basin. Pixel accuracy >84% on F3, ~99% peak class accuracy on Penobscot transfer.

Digitization of raster logs — VeerNet, a U-Net + transformer hybrid

The full peer-reviewed treatment of VeerNet — the U-Net-inspired transformer-augmented architecture EarthScan uses for raster well-log digitisation. Covers the synthetic-curve generation pipeline, the Texas RRC real-data set, the loss-function ablation across Dice, Tversky, Lovász, Focal, and SCE, and the comparison with the Neuralog SCTR commercial baseline.

VeerNet — a transformer-residual hybrid for raster well-log digitisation

VeerNet — published in MDPI Journal of Imaging — is the deep-learning architecture EarthScan built specifically for the raster-log digitisation problem: turning scanned paper well-logs into queryable digital curves. Hybrid CNN-residual encoder + transformer + upsampling decoder. F1 = 35%, IoU = 30% on a 10K-image benchmark.

Automated FMI-log correlation via unsupervised CNN segmentation

Borehole image (FMI) logs reveal fractures and breakouts that conventional logs cannot — but annotating them is laborious and inconsistent. This work shows that unsupervised CNN segmentation (Kim et al., 2020) detects bedding planes and fracture sinusoids without any labelled training data. Presented at EAGE Workshop on Borehole Geology in Asia Pacific.

EarthAdaptNet on F3 + Penobscot: a domain-adaptation case study

Companion to the EarthAdaptNet architecture paper — this post applies EAN to two real industry seismic datasets (F3 block, Netherlands; Penobscot, Canada) and introduces the deep domain-adaptation (DDA) variant for the unlabelled-target case. Lays the groundwork for the IEEE TGRS 2022 paper.

Oil & gas needs MLOps, not just ML

Oil and gas generates terabytes of operational data daily and spends $3.3 trillion in annual revenue — yet ML projects in the industry routinely die between training and production. The bottleneck isn't the models. It's MLOps. Originally published on HackerNoon.

Deep learning in geoscience and subsurface: a practitioner's guide

A practitioner-oriented walkthrough of where deep learning earns its keep in subsurface workflows — seismic imaging, reservoir characterisation, fault detection — and where the literature still over-promises. Webinar recap with the key papers and code links.

EarthAdaptNet: a domain-adaptive encoder-decoder for seismic imaging

The architectural detail behind EarthAdaptNet (EAN) — the encoder-decoder network EarthScan applies to semantic segmentation of seismic images. Companion to the geothermal case-study post; this one focuses on the building blocks (Residual Blocks, Transposed Residual Blocks, U-Net-inspired skip connections) and the per-class accuracies on the Roosevelt benchmark.

AI for geothermal: applying seismic-facies segmentation to a clean-energy reservoir

How the same deep-learning toolkit oil & gas operators have been using to interpret seismic data can be redirected at a clean-energy problem: locating geothermal reservoirs. Case study from the Roosevelt Hot Springs Geothermal Area, Utah, using EarthScan's EarthAdaptNet encoder-decoder architecture.

Data-centric AI development: from Big Data to Good Data

Andrew Ng's reframing — that the next 90% of AI's value will come from improving the data, not the model — changes how production teams build pipelines. A webinar recap covering data ingestion, the data pipeline, MLOps, and why AugMix is one of the most underrated augmentation methods.

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