Adamantan SIA · Exploration · Data Science · Europe
Physics-based basin modeling fused with machine learning — accelerating hydrocarbon discovery, reducing subsurface risk, and delivering insight at a fraction of traditional cost.
The Problem
Traditional basin modeling takes weeks per scenario — with limited uncertainty analysis and massive manual overhead.
Petroleum exploration relies on computationally expensive deterministic simulations, sparse subsurface data, and expert-intensive manual interpretation. For every scenario run, teams spend weeks building models, leaving little room for uncertainty analysis or rapid iteration.
Adamantan's hybrid Physics + ML platform breaks this bottleneck — combining rigorous physical models with ML-powered surrogates, automated calibration, and Bayesian uncertainty quantification to deliver comprehensive exploration insights in hours, not weeks.
Core Services
From raw data conditioning to actionable exploration decisions — every stage of the subsurface workflow, enhanced by machine learning.
Complete 1D/2D/3D basin analysis: burial history, thermal evolution, HC generation, migration pathways, and accumulation modeling using PetroMod and TemisFlow.
Regional evaluation of sedimentary basins, identification of prospective areas, license block evaluation, play-based risk ranking, and resource potential assessment.
Predictive modeling of TOC, Ro, and HI from wireline logs using ensemble ML and Physics-Informed Neural Networks. Automated source rock characterization and biomarker analysis.
Balanced cross-sections, fault permeability analysis, fracture distribution modeling, tectonic reconstruction, and drilling risk assessment for complex folded regions.
3D terrigenous and carbonate depositional simulations, facies maps, sequence stratigraphy, and reservoir quality prediction using DionisosFlow and Petrel-GPM.
We develop proprietary tools for structural analysis (KINEMATIK), Python/ML pipelines for geoscience workflows, and custom automation software built on innovative methodologies.
Monte Carlo scenario analysis, play-based risk assessment, P10/P50/P90 volumetric estimation. Bayesian inversion and ML surrogate ensembles for 100× speedup in uncertainty analysis.
Multi-source data fusion — seismic, well-log, geochemical, and structural — with automated QC, gap-filling, and anomaly detection to prepare clean inputs for basin models.
Expert audits, methodology consulting, scientific project support, and knowledge transfer workshops — flexible as standalone projects, retainers, or structured training programs.
How We Work
Machine learning is not bolted on — it is embedded at each phase of the exploration lifecycle, from data conditioning through final reporting.
Our Core Methodology
A proprietary spatial framework that brings geological context into every machine learning workflow — enabling statistically robust, geologically meaningful risk assessment at basin scale.
Our flagship methodology addresses a fundamental challenge in basin-scale ML: raw geological data lacks spatial context. Standard machine learning ignores the structural architecture of the basin — treating a data point 200 km from the nearest well the same as one 5 km away. We solve this by imposing a rigorous spatial framework before any ML analysis begins.
Why this matters for ML: Standard geoscience ML treats data points as independent observations. Our block-zone-HUB framework encodes structural position, distance to known data, and geological context as explicit features — giving ML models the spatial intelligence to learn geologically valid patterns, even in data-sparse frontier basins.
Outcome: Basin-wide HC potential maps with per-element risk scores, confidence intervals, and statistically grounded exploration priorities — delivered faster and at lower cost than conventional approaches.
Processing, analysis, and preparation of well logs, seismic, and geochemical data. Basin screening and regional analysis. License block evaluation.
2D modeling of complex folded regions, tectonic evolution & fault timing analysis, fault permeability analysis, validation of structural seismic interpretation.
1D/2D/3D petroleum systems modeling with PetroMod and TemisFlow. Model scaling, local grid refinement, and technical support for model construction.
Source rock characterization (TOC, HI, Tmax, S1/S2), maturity modeling and calibration (vitrinite reflectance, AFTA), biomarker analysis and oil-source correlation.
HC migration pathway modeling (Darcy flow, ray-tracing), trap filling and spill-point analysis, reservoir charge and seal integrity evaluation, stratigraphic forward modeling.
Monte Carlo scenario analysis, play-based risk assessment, probabilistic resource assessment (P10/P50/P90), petroleum system risk element analysis.
Integrated study reports, interactive dashboards and 3D visualization, exploration recommendations, well-location proposals, and knowledge transfer workshops.
Software & Platforms
We work across the leading petroleum systems platforms and develop our own proprietary software where standard tools fall short.
Industries Served
Our geoscience and AI capabilities transfer across any industry requiring subsurface understanding.
Full-cycle exploration support from basin screening and play analysis through prospect evaluation, drilling recommendations, and petroleum systems risk assessment.
Structural geology, basin analysis, and geochemical data science applied to mineral exploration targeting and resource characterization.
Site characterization, reservoir integrity analysis, and uncertainty quantification for CO₂ storage projects — leveraging the same petroleum systems workflows.
Thermal history modeling, structural analysis, and resource assessment for geothermal exploration — physics-based models adapted to heat flow characterization.
Why Adamantan
We don't layer AI on top of existing workflows — we rebuild exploration workflows from the ground up around the physics + ML combination.
Our team spans geologists, geophysicists, geochemists, and data scientists with decades of real exploration experience — not just AI practitioners applying generic methods.
We don't replace physical models with black-box ML. Our surrogates are trained on and constrained by the physics — delivering interpretable, geologically valid results.
KINEMATIK and our in-house tools embody proprietary methodologies developed over years of structural geology research — capabilities not available elsewhere.
We handle the full chain — data conditioning, model building, uncertainty analysis, and final exploration recommendations — as a single integrated workflow.
We work as project-based consultants, on retainer for ongoing exploration programs, or as a training partner running expert workshops for your in-house team.
Ready to Begin?
Let's discuss how our Physics + ML approach can reduce subsurface risk and accelerate your exploration decisions.