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Deep Tech

Built for the
Wellbore.

Greenstone's technology stack is purpose-built for oil and gas — not adapted from enterprise software. Every component is designed around the latency, reliability, and precision demands of real-time drilling operations.

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Go / WASMCloudflare WorkersWITSML 2.0WebSocketsReact + Three.jsClaude APIFastAPIZero Trust
Core Platform

DataEngine

Real-time wellbore data ingestion & processing

DataEngine is the calculation truth layer of the GeoMaster platform — a pure-Python WITSML-2 library with 17 typed builders across 16 pipeline stages, a UOM registry, schema-driven parameter validation, minimum-curvature trajectory engine with tie-in support, physics-based kick detection, and an Optuna-based calibration system.

17
Calc builders
v0.7.8
Current version
99.9%
Uptime SLA
Capabilities
WITSML 1.4.1.1 + 2.0 ingestion
Full schema support — LAS 2.0, Petrel format, and CSV also supported
17 calculation builders
16 pipeline stages: trajectory (min-curvature + tie-in), drilling mode classifier, kick detection, MSE, d-exponent, ECD, and more
15 typed schemas
PARAM_SCHEMAS registry with parameter validation — schema-driven thresholds replacing hardcoded values as of v0.6
Physics-based kick detection
Three-stream indicator system: primary (pit gain, MW delta, ECD drop), secondary (rolling-baseline SPP/ROP/torque/d-exp/gas). ML layer in development.
Optuna calibration
Bayesian hyperparameter tuning for builder thresholds — calibrated against real well datasets
AI Core

GeoEngine

Multi-agent AI system for drilling intelligence

GeoEngine is not a chatbot — it is a multi-agent AI system where specialist agents collaborate, each with access to specific tools via MCP servers, coordinated by a central orchestrator. It runs as a dedicated FastAPI service alongside GeoMaster's Django backend, accessed only through internal service-to-service calls.

30d
Intel cache TTL
2
Live agents
MCP
Tool protocol
Capabilities
FieldIntelligenceAgent
Autonomously searches geological sources, synthesises formation, reservoir, drilling context, and geosteering notes — cached per field for 30 days
ChatAgent
Conversational AI grounded in live field intelligence and project state — maintains per-project chat history and accesses well trajectories, formation tops, and log data
Model-agnostic LLM interface
Runs on Claude (Anthropic) or OpenAI — switch providers with a single env var, zero code changes
MCP server architecture
Every tool is an MCP server: DatabaseServer (project data), WebSearchServer (Tavily/Brave), MemoryServer (intelligence cache + history)
Extension system
Linker Optimisation, WITSML monitoring agent, and Mud Logging agents on the roadmap — each as a first-party GeoEngine extension
Infrastructure

WASM Runtime

Browser-native computation at native speed

Critical computation — depth-shift algorithms, synthetic log generation, and curve arithmetic — runs in WebAssembly directly in the browser. This eliminates round-trip latency for the interactive parts of the workflow while keeping sensitive data client-side.

<1ms
Compute latency
Go
Source language
0
Server round-trips for QC
Capabilities
Depth-shift engine
Sub-millisecond lag correction for MWD vs LWD depth mismatch
Synthetic log generation
Real-time Gardner and Castagna synthetics from density logs in the browser
Curve arithmetic
Cross-plot computations (Rwa, Sw, Vclay) run at 60fps during interaction
Zero server round-trips
Interactive QC workflows respond instantly regardless of connection quality
WASM-first design
Computation modules compiled from Go via wasm_exec — deterministic, auditable, and runs entirely in the browser
AI / Training

ML Pipeline

Continuous learning from every well drilled

The ML Pipeline manages training, versioning, and deployment of GeoEngine's models. It runs on Cloudflare Workers AI for inference and feeds back from operator-validated geosteering decisions to continuously improve basin-specific models.

12 basin
Specialised models
Edge
Inference location
SHAP
Explainability
Capabilities
Transfer learning by basin
Global base model fine-tuned on regional data — Permian, North Sea, Middle East, and more
Federated improvement
Operator corrections feed anonymised gradients back into the shared model — no raw data leaves the operator
Model versioning
Every inference is tagged with the model version — full reproducibility for well reports
Edge inference
Models deployed to Cloudflare edge — inference happens close to the user, not in a central data centre
Explainability layer
SHAP-based feature importance — geologists can see why the model made a recommendation
Infrastructure

Built on Cloudflare's Global Edge

Every component runs at the edge — compute, storage, auth, and observability. No centralised servers, no single point of failure.

Edge Network
Cloudflare

300+ PoPs — data and compute close to every rig site globally

Deployment
Workers / Pages

Serverless — no instances to manage, instant global scale

Storage
R2 + D1

Zero egress cost object storage + distributed SQLite at the edge

Realtime
Durable Objects

Stateful WebSocket connections for live well data streaming

Auth
Zero Trust

Cloudflare Access — no VPN, identity-first access control

Observability
Workers Analytics

P95 latency, error rates, and usage — built in, no Datadog bill

Ready to see it in action?

GeoMaster is live and processing real wells today. Request a technical demo with our engineering team.

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