Knowledge Graphs for Oil & Gas

Short answer. A knowledge graph for oil & gas connects country, basin, formation, well, event, sensor, feature, model, risk, advisory, procedure, action and outcome as explicit relationships. It gives drilling AI the geological and operational context it lacks, and makes every recommendation traceable to its evidence.

Knowledge Graphs

What problem does this solve?

Lessons from past wells are spread across daily and end-of-well reports, spreadsheets and people, and are not available to engineers or models at the moment they are needed.

What does the architecture look like?

CountryBasinFormationWellEventSensorFeaturePhysics / ML modelRiskAdvisoryProcedureActionOutcome
Knowledge graph: from country to outcome. Country → Basin → Formation → Well → Event → Sensor → Feature → Physics / ML model → Risk → Advisory → Procedure → Action → Outcome

How does it work?

The ontology

The graph models the chain from country and basin to formation and well, then from events and sensors to features, models, risks and advisories, and finally to procedures, actions and outcomes.

Neo4j or GraphDB

Neo4j suits operational queries such as offset-well lookup; RDF stores such as GraphDB suit standards-based ontologies and inference.

Feeding AI

Graph context is used for model features, cluster assignment in federated learning and GraphRAG retrieval for engineering Q&A.

Closing the loop

Recording which advisory led to which action and outcome turns the graph into an evidence base for improving models and procedures.

Implementation considerations

  • Start with a narrow ontology and grow it from real queries.
  • Entity resolution for well and formation names is essential.
  • Assign data owners for each part of the graph.

Frequently asked questions

What is the first use case for a drilling knowledge graph?

Offset-well lookup: finding similar wells and their events during planning and real-time operations.

Related solutions

Solution

Drilling Knowledge Graph

A drilling knowledge graph links countries, basins, formations, wells, events, sensors, models, risks, advisories, procedures and outcomes.

Solution

Knowledge Graphs

A knowledge graph connects assets, locations, events, sensors, models, procedures and outcomes as explicit relationships.

Solution

AI for Drilling Operations

AI for drilling operations combines real-time rig sensor data, physics models and machine learning to detect hazards such as kicks, lost circulation and stuck pipe early, and to advise drillers with evidence.

More in the Knowledge Graph guide

Knowledge Graphs

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Architecture Guides

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Federated Learning

What Is Federated Learning for Oil & Gas?

Federated learning for oil & gas trains drilling and production models across rigs, operators and countries without moving raw well data.

Explore the Architecture

Talk it through with an engineer who builds these systems.

Explore the Architecture