GGL provides data and AI solutions designed to transform disconnected operational information into governed, actionable intelligence.

Our approach is to create a governed data foundation across operational, subsurface, maintenance, engineering and commercial systems, then deploy AI, analytics and automation against clearly defined operational outcomes.

Information can be connected from systems such as:

SCADA | Historians | IoT | SAP | Maximo | Oracle | Seismic | Well Logs | Reservoir Models | Inspection Data | Engineering Documents

Once unified, this information can be used to improve operational decision-making across the energy and industrial value chain.

The existing GGL AI proposition identifies applications across exploration and drilling, production and reservoir, asset integrity, reliability and maintenance, refining and processing, HSE, engineering documents, and supply chain and finance.

Key Applications

Exploration & Drilling:
Prospect ranking, well-placement support, geohazard prediction and subsurface-data integration.

Production & Reservoir:
Production optimisation, reservoir surveillance, forecasting and well-performance monitoring.

Asset Integrity:
Risk-Based Inspection, Remaining Useful Life assessment, corrosion and integrity analytics.

Reliability & Maintenance:
Predictive maintenance, equipment failure prediction and spare-parts optimisation.

Refining & Processing:
Process optimisation, energy optimisation, throughput improvement and margin enhancement.

HSE & Environmental:
Anomaly detection, visual monitoring, spill detection, emissions monitoring and operational risk reduction.

Engineering & Document AI:
Search and interrogate technical drawings, reports, manuals, inspection records and engineering documents with source traceability.

Supply Chain & Commercial:
Inventory forecasting, procurement analytics, invoice verification, contract intelligence and workflow automation.

GGL AI Journey

CONNECT → UNIFY → UNDERSTAND → PREDICT → ACT

The objective is to convert data into measurable operational outcomes:

Increase Production | Improve Asset Reliability | Reduce Downtime | Optimise Energy & Refining Performance |
Automate Engineering Workflows | Reduce Operational Risk