Pipeline Network
Hydraulic and thermal modeling of the entire pipeline network, accounting for process constraints
Web-based engineering
Pipeline network simulation for the web
Build and refine pipeline network models in a shared, browser-based workspace. Analyze flow, pressure and temperature across the network while accounting for process constraints
Engineering tasks
What is happeningin the network now?
Flow, pressure and temperature distribution accounting for interactions between network objects
- Boundary objects, pipelines and valves in one model
- Simulation of branched and looped networks
- Parameters throughout the network
A coherent operating picture in which every segment is connected to the entire system
Functional architecture
Unified simulation engine
Solve the entire network as a coupled system using mass, momentum and energy balances
Matrix-based network solver
The network is solved as a single system of nonlinear equations
- Loops, bypass lines and branches
- Boundary-condition inversion
- Automatic flow reversal
- Support for parallel computation on CPUs
Browser-based access
Work in your browser with centrally managed updates
Roles and permissions
Collaborate with project-level access control
Model versioning
Track changes and save model versions
Object management
Manage input data, object properties and results in a single table
Flexible workspace
Customizable units of measurement and light and dark themes
Help and training
User guides, technical guidance and interactive courses within the platform
Product interface
Pipeline network simulation
Production network diagram, matrix solver progress and solver messages
Model calibration
A model aligned with actualoperating conditions
Calibration against field pressure and temperature measurements to reduce differences between model predictions and field data
- 01
Fielddata
Pressure and temperature measurements at network elements for a specified date
- 02
Parametersetup
Select calibration parameters and their adjustment ranges
- 03
Runcalibration
Choose an algorithm and start the calibration
- 04
Analyzeresults
Compare model predictions with field measurements and track the error
- 05
Updatedmodel
A refined model for operating analysis and forecasting
- Calibration parameters
- Friction, hydrostatic and heat-transfer coefficients
- Calibration algorithms
- Differential evolution, particle swarm optimization, a genetic algorithm, and the Nelder–Mead and Levenberg–Marquardt methods
Model optimization
Find the best feasibleoperating conditions
Optimize network control settings for a chosen objective while satisfying process constraints
Objective function
What we optimize
- maxOil, condensate or gas production rate
- minChemical injection rate
- minWater or associated gas production rate
Control variables
- Choke diameter / pressure drop
- ESP operating frequency
- Sucker rod pump stroke rate
- Pump / compressor operating frequency
- Chemical injection rate
Feasible region
Constraints we consider
| Hydraulic | Pressure and pressure drop |
|---|---|
| Flow rate | Flow rates for individual elements and the whole network |
| Temperature | Temperatures at network elements |
| Composition | H₂S, CO₂ and other component concentrations |
| Group | Constraints for groups of network elements |
Iterative search: solve the network model, evaluate the objective function, check constraints and update control variables
Quality, speed and convergence
Comparison with reference software
BASIS deviations from reference results
| Parameter | Mean | Maximum |
|---|---|---|
| Pressure · P | 0.48% | 1.05% |
| Flow rate · Q | 0.04% | 0.68% |
| Temperature · T | 0.16% | 0.94% |
Snapshot simulation time
| Objects in the model | BASIS | Reference |
|---|---|---|
| ≈ 100 | 0.9 s | 0.8 s |
| ≈ 1,000 | 21.4 s | 20.6 s |
| ≈ 3,000 | 82.7 s | 78.9 s |
iterations on average for BASIS to converge19 for the reference software