Datum shift correction • Manifold-constrained hyper-connections • Confidential regulatory safe harbor
Natural variance in legacy offshore survey records is reconciled against modern GPS-corrected seabed mapping. Upgrading positional certainty for WIOS compliance without implying operator fault.
Thermodynamic forensics applied to shut-in pressure records to identify potential aquifer recharge pathways. Enhances reserve certainty for decommissioning planning.
Manifold-constrained analysis of barrier test signatures to confirm mechanical integrity for NDR submission. Confidential internal risk assessment, not public disclosure.
WIOS 2026 Compliance Program: Accepting 3 legacy asset portfolios for confidential re-alignment audit. Strictly internal risk mitigation. No public disclosure. SC876023 sovereign vault protocol.
Between 1967 and 2003, North Sea wellbore logging was constrained by the physics of wireline tools.
We audit the artifacts they left behind.
Analog-era logging cables could only support 18,000 lbs of tension. In deep water (500m+), this physical limit meant tools had to stop short of total depth. Measurement gaps were recorded as "TD not reached" but correlation networks assumed continuous data.
Before dynamic positioning systems (1990s), wellhead re-entry during logging required sea states below Douglas 4. Weather windows forced operators to log in sections across multiple trips, creating datum shift artifacts when Kelly Bushing elevations changed between runs.
Magnetic tape decay, microfiche scanning errors, and paper log digitization introduced quantization noise. A handwritten "8,247 ft" became "8,214 ft" after three generations of copying. The data harmonization problem is not incompetence—it's entropy.
1970s logs referenced Kelly Bushing (KB). 1990s regulations mandated Ground Level (GL). Operators applied bulk conversions using platform air-gap tables that assumed fixed rig height—but jacking rigs, semi-submersibles, and tidal corrections were inconsistent. WellTegra reconciles these systematic biases using graph neural networks trained on correlation topology.
The result? £2.1B in decommissioning bonds at risk from depth uncertainty. Carbon tax misreporting from incorrect zone classifications. HMRC fiscal integrity gaps for EPL tax relief. WellTegra exists to audit these discrepancies—not to assign blame, but to restore precision.
High-performance language models combined with physics-constrained graph neural networks
to reconstruct corrupted legacy wellbore data into forensic-grade notarized reports.
The Brahan Engine is not a chatbot. It is a manifold-constrained deep learning system (mHC-GNN: 128-layer graph neural network) trained to identify and correct systematic datum errors embedded in 57 years of analog-era wellbore logs. The core problem it solves:
Beyond datum correction, the Engine runs predictive flow assurance simulations using thermodynamic PVT modeling:
Research Foundation: 74% accuracy at 128 layers on citation network benchmarks (PubMed, arXiv:2601.02451). Engineering Target: Reproduce performance on North Sea wellbore correlation networks (442 wells under management).
The Engine uses manifold-constrained message passing to propagate depth corrections across well networks while respecting stratigraphic marker continuity. Birkhoff polytope projection ensures that corrected TVD/MD (True Vertical Depth / Measured Depth) values satisfy thermodynamic consistency—i.e., no negative pore pressures, no inverted stratigraphic sequences.
Inference latency: 180ms per well (containerized microservice, INT4 quantization).
Estimate liability mitigation and carbon tax integrity savings
based on your wellbore portfolio.
⚠️ Disclaimer: ROI estimates are based on industry-standard NPT (Non-Productive Time) risk models (£2.1M median per well) and UK ETS carbon pricing (£85/tonne CO₂e). Actual results vary by asset, geological complexity, and operator-specific factors. Contact kenneth.mckenzie@welltegra.network for customer-specific analysis.
Edge-first architecture with offline-capable inference.
Built for operators who understand that wellbore data is a strategic asset.
The Brahan Engine runs on edge compute nodes at your facility—no cloud round-trips, no public API calls. Your well data is processed on-device using INT4-quantized models. Internet connectivity is optional, not required.
For operators requiring cloud training, we support UK-domiciled sovereign enclaves (Azure UK South, AWS eu-west-2). Data never transits US soil. GDPR-compliant, NIST 800-53 controls, ISO 27001 certified infrastructure. Your jurisdiction, your rules.
Cryptographic signing uses TPM 2.0 secure enclaves with hardware-backed key storage. Private keys never exist in software-accessible memory. Every forensic report carries a GPG signature that proves it was generated by WellTegra hardware—not spoofed, not tampered.
For ATEX Zone 1 environments or classified operations, the Engine supports sneakernet deployment: transfer model weights via encrypted USB, run inference offline, export GPG-signed reports for manual upload. Zero network exposure. Used by operators who take "industrial security" literally.
Why? Because Kenneth spent 30 years watching operators lose control of their own subsurface truth to vendors with surveillance business models. The Brahan Engine is the antidote.
In the 17th century, another Kenneth Mackenzie—the Brahan Seer—was said to possess a "seeing stone" that revealed hidden truths beneath the Scottish Highlands. He predicted the future by reading what others could not see.
Three centuries later, a modern Kenneth McKenzie has returned to the North Sea with his own seeing stone: The Brahan Engine.
But this Kenneth didn't learn his trade in a university lab. He earned it over 30 years on the rig floor—drilling through pressure, rust, and the relentless hostility of the North Atlantic. He's seen what happens when bad data meets bad decisions: blowouts, stuck pipe, P&A failures that cost lives and fortunes.
The Brahan Engine wasn't built to "disrupt" the oil industry. It was built because Kenneth was tired of watching preventable disasters unfold from datum errors buried in 57 years of paper logs, microfiche scans, and corrupted SQL dumps.
"This is not a tech company pretending to understand oil. This is an oil company that learned to code."
WellTegra Ltd (SC876023) was incorporated on 21 January 2026 as a Scottish registered entity with a singular mission: bring sovereign-grade integrity to North Sea wellbore data. No cloud dependencies. No foreign servers. No compromises.
The Seer doesn't predict the future. He prevents it.
Kenneth McKenzie holds 30+ years of Engineer of Record authority across the Perfect 11 North Sea assets (442 wells). He has witnessed every failure mode. He has read every illegible log. He has corrected every phantom depth. The Brahan Engine is the algorithm of that experience—carved in granite and hardened by steel.
Kenneth McKenzie's lived industrial memory across the Perfect 11 North Sea assets
encoded as Physical AI training weights. This is not synthetic data—it is witnessed experience.
40 wells | EnQuest | 1987
78 wells | CNR Int'l | 1978
62 wells | EnQuest | 1983
52 wells | TotalEnergies | 1987
34 wells | TotalEnergies | 1994
48 wells | Serica | 1993
28 wells | Dana | 1990
18 wells | Repsol | 2005
24 wells | Chevron | 1993
22 wells | TotalEnergies | 1997
36 wells | Nordsøfonden | 1972
On-site processing in environments where connectivity is a luxury, not a guarantee.
Steel shielding, oil-based mud, and satellite bottlenecks demand local compute.
Offshore drilling operations present three fundamental barriers to cloud-based AI systems:
WellTegra deploys a ruggedized edge compute server on-site (rig floor, mud logging unit, or drilling contractor office). This is not a "device"—it is a containerized inference cluster:
No satellite round-trip. Local database queries, local model inference, local result delivery. 180ms typical latency for forensic depth corrections.
Wellbore data never leaves the Node unless explicitly exported via GPG-signed forensic reports. No "training data exfiltration," no third-party cloud access.
Operates indefinitely without internet connectivity. Model weights and historical well data pre-loaded. Satellite link not required.
Node operates in Zone 2 (non-explosion) areas (mud logging unit, control room). Interfaces with IS-certified sensors via isolated I/O modules. No spark risk.
The Sovereign Node adapts to your operational constraints:
Side-by-side analysis of operational constraints and system architecture.
| Operational Parameter | Legacy Cloud/Manual Systems | WellTegra Sovereign Node |
|---|---|---|
| Inference Latency |
2–5 seconds Satellite uplink (500–800ms each way) + cloud API processing + database round-trip. Geosynchronous orbit introduces unavoidable propagation delay. |
180ms Local inference (mHC-GNN on-device), local database query, no network egress. Sub-200ms guaranteed for safety-critical queries. |
| Data Integrity |
Manual reconciliation Excel spreadsheets, handwritten log corrections, email-based consensus. No cryptographic audit trails. Datum errors propagate unchecked for decades. |
Forensic-grade notarization 11-agent consensus protocol (9/11 threshold), GPG RSA-4096 signatures, immutable audit logs. Datum corrections validated by mHC-GNN graph topology. |
| Connectivity Barriers (Steel Shielding) |
System failure Cloud-dependent AI requires continuous RF connectivity. Steel derricks create Faraday cage effects. Cellular/Wi-Fi propagation blocked. System unusable in rig floor environment. |
Operates normally Sovereign Node hardwired to rig LAN or operates in air-gap mode. No RF dependency. Steel shielding irrelevant to wired infrastructure. |
| Connectivity Barriers (Oil-Based Mud) |
Telemetry degradation OBM creates dielectric barrier between downhole sensors and surface antennas. EM-MWD transmission becomes unreliable. Cloud systems cannot compensate for sparse/corrupted data. |
Physics-constrained inference mHC-GNN trained to handle incomplete data via manifold projection. Sinkhorn-Knopp algorithm reconstructs missing measurements from correlation topology. OBM irrelevant to processing. |
| Operational Logic |
Reactive troubleshooting Petrophysicists analyze data after drilling. Datum errors discovered during decommissioning (30+ years later). No predictive flow assurance. Asphaltene sludging = unplanned workover. |
Predictive forensics Real-time pore pressure prediction (Eaton's method + overburden integration). Pre-drill flow assurance modeling (asphaltene, wax, hydrates). Datum errors flagged during logging—not decades later. |
| Data Sovereignty |
Cloud vendor lock-in Wellbore data uploaded to AWS/Azure/GCP for processing. Operator loses control. Vendor may use data for "model improvements" (i.e., training competitors' models). |
100% on-premise All processing happens on Sovereign Node. Zero cloud egress. Operator controls data lifecycle. Contractual deletion guarantee within 30 days of engagement end. |
Engineering Reality: Offshore drilling environments are electromagnetically hostile (steel shielding), telemetrically sparse (OBM attenuation), and latency-intolerant (kick risk = seconds to respond). Cloud-based AI systems fail under these constraints. The Sovereign Node is purpose-built for this reality.
Currently accepting 3 assets for discovery audits to validate engine accuracy on new basins.
We're not selling a solution. We're demonstrating a capability.
Analysis delivered within 14 days. No marketing materials, no sales calls.
The data either validates the method or it doesn't.
Why 3 assets? Basin diversity validates algorithm transferability. We need Norwegian Continental Shelf, UK Central North Sea, and Gulf of Mexico data to prove this isn't curve-fitted to one region.
Radical Transparency: WellTegra is pre-revenue. The Brahan Engine works on academic benchmarks (74% accuracy @ 128 layers, arXiv:2601.02451). Whether it works on real wells with decades of datum errors and questionable cement jobs is the question this program answers.
No Hype: If the engine finds a 4-meter vertical offset in your dataset, we'll show you the math. If it doesn't detect anything abnormal, we'll tell you that too. Boring physics. Cold science. Reproducible results.
The Real Goal: Prove to skeptical petroleum engineers that graph neural networks can detect reservoir compartmentalization that traditional correlations miss. If you walk away convinced the method is sound, that's validation. If you find a flaw in the methodology, that's also validation.
3 slots available. Selection based on basin diversity and data completeness.
Norwegian Continental Shelf, UK Central North Sea, and Gulf of Mexico preferred.
A: That's exactly the problem we solve. We've worked with 57 years of paper/microfiche/SQL records. Send us what you have—incomplete data is our specialty.
A: Absolutely not. All data covered by NDA. We may publish anonymized/aggregated results (e.g., "North Sea well showed 80ft datum error") but never operator-identifiable information without explicit permission.
A: Nothing, unless you want more. If you're satisfied, we can discuss full field audits at our standard £50K/well rate. If not, you keep the free report and we part as friends.
A: Basin diversity validation. We need Norwegian Continental Shelf, UK Central North Sea, and Gulf of Mexico data to prove algorithm transferability. Each analysis requires 20-40 hours of engineering review. Quality over quantity.
Thirty years of North Sea operational expertise applied to the digital transition. This is not theoretical AI—this is physics-informed, author-provenant data forensics for critical energy infrastructure.
WellTegra Ltd (SC876023) applies three decades of North Sea operational experience to the digital transition. Our manifold-constrained hyper-connection (mHC-GNN) platform is not theoretical AI—it is physics-informed intelligence designed for critical energy infrastructure.
Author-provenant records validated against physical invariants. Migration decay detection ensures datasets are complete and error-free.
Original field author with 30 years of rig-floor experience validates every AI output against thermodynamic principles.
Verified depth accuracy for decommissioning operations. Systematic error detection prevents £40B P&A program risk exposure.
Our mission is to upgrade data certainty for the North Sea decommissioning wave—not to expose operational variance, but to ensure compliance with modern standards.
Information Security Coordinator & Chief AI Architect:
Kenneth McKenzie | WellTegra Ltd (SC876023)
30 Years North Sea Operational Experience | Original Author: 2014 Ninian/Thistle WellView Records
Submit an asset for discovery audit or discuss technical methodology.
GPG-signed reports • NSTA workflow-optimized • Auditable for HMRC/UK ETS compliance
Want to dig deeper? Explore the Technical Data Room for research papers, deployment manifests, and GPG verification tools.
Technical documentation, NIM deployment manifests, and investment materials.
Complete containerized microservice architecture
Kubernetes deployment for cloud-scale inference
Sinkhorn-Knopp manifold projection source code
WIOS 2026 compliance documentation
1-page executive business summary
GPG public key (RSA-4096) for signature verification
*74% Accuracy @ 128 Layers: Benchmark result from mHC-GNN research on citation network datasets (PubMed, arXiv:2601.02451). Engineering target: Reproduce on North Sea wellbore correlation networks (442 wells). Proof-of-Concept demonstrated on Thistle A-12 (8,247→8,214 ft, 99.7% confidence).
**EPL Tax Relief Positions: £1.58B represents internal analysis of potential EPL (Energy Profits Levy) tax relief positions across Perfect 11 portfolio. This figure is pending third-party audit and HMRC verification. Individual operator results may vary.