Drilling Knowledge Graph: Connecting Wells, Formations, Events and Advice
Short answer. A drilling knowledge graph links countries, basins, formations, wells, events, sensors, models, risks, advisories, procedures and outcomes. AgenticNex AI builds it on Neo4j or GraphDB so drilling AI can use offset-well context and every advisory traces back to evidence.
What is the business problem?
Lessons from past wells sit in daily drilling reports, end-of-well reports and engineers' memory, and are rarely available when a similar situation occurs.
Why can conventional approaches be insufficient?
- Reports are unstructured and siloed by well and operator.
- Similarity search on text misses geological context.
- Advisories are not linked to the outcomes they produced.
What is our engineering approach?
- Model the drilling ontology from country to outcome.
- Extract entities and events from reports and real-time data.
- Link ML models and their features to the channels they use.
- Close the loop by recording actions and outcomes.
What does the architecture look like?
Which technology capabilities are involved?
- Neo4j / GraphDB
- Drilling ontology
- NLP extraction from DDRs
- Graph similarity for offset wells
- GraphRAG for engineering Q&A
Example use cases
- Offset-well lookup during planning
- Context for real-time hazard models
- Engineering Q&A over historical reports
Expected business and technical outcomes
- Past lessons available at the moment of need
- Auditable chain from evidence to advice
- [Insert verified project result]
Relevant case studies
Frequently asked questions
What data goes into a drilling knowledge graph?
Well headers, formations, trajectories, daily and end-of-well reports, events, sensor channels, models, procedures and outcomes.
Related insights
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