What is a Digital Twin?
A digital twin is a dynamic virtual representation of a physical asset, process, or system that is continuously updated with real-time data. Unlike a static simulation model that is run once and shelved, a digital twin lives alongside its physical counterpart, evolving as conditions change.
Digital Twin vs Simulation Model
Traditional Simulation
- • Built once, updated periodically (months/years)
- • Run offline by specialists
- • Snapshot of a point in time
- • Used for strategic planning
Digital Twin
- • Continuously updated with real-time data
- • Accessible to operations teams via dashboards
- • Reflects current state of the physical asset
- • Used for both real-time operations and planning
Levels of Digital Twin Maturity
Descriptive Twin
A 3D visualisation or data model that represents the asset's current state. Shows what is happening but does not predict or prescribe.
Diagnostic Twin
Integrates analytics to explain why something is happening - root cause analysis, anomaly detection, performance deviation alerts.
Predictive Twin
Uses ML and physics models to forecast future states - predicting equipment failures, production decline, or reservoir behaviour weeks ahead.
Prescriptive Twin
Recommends optimal actions - adjust choke settings, schedule maintenance, modify injection rates - based on simulation of multiple scenarios.
Industry Examples
BP: Uses APEX digital twin technology to model its global production network - reservoirs, wells, and surface facilities - enabling engineers to run "what-if" scenarios and optimise production across the entire portfolio.
Aker BP: Partnered with Cognite to build a data-driven digital twin of their North Sea assets, integrating 3D models, equipment data, and maintenance history into a single platform accessible to all disciplines.
Equinor: Uses digital twins of offshore platforms for remote diagnostics, reducing the need for helicopter trips and enabling onshore experts to troubleshoot equipment issues in real time.