Pipeline Integrity Management System
Software solution designed for monitoring pipeline system condition regarding corrosion-erosion wear and centralized storage of maintenance documentation
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Issues
- Two-dimensional and three-dimensional displaying of pipelines with geographic referencing based on project and as-built documentation, showing the dynamics of spatial changes in pipeline position
- Automated monitoring and analysis of the technical condition of pipeline network elements
- Interactive mapping of equipment passports and other necessary information
- Timely provision of current and analytical information on corrosion-erosion wear and the remaining life of pipeline network elements
- Planning and management of pipeline maintenance and repair operations
Who is intented to use the system?
Maintenance engineers
- Recording thickness measurement results from ultrasonic diagnostics and intelligent “smart” pipeline pigging
- Viewing and interpreting information on current and projected corrosion and erosion wear rates, as well as remaining wall thickness along the entire length of the pipelines
- Making decisions on maintenance and repair activities
Top management
- Essential information on the condition of the pipeline system, planning, and conducting repairs
- Using the interactive intelligent system for analytics, presentations, and reporting
Features
Mapping of infrastructure objects
3D visualization
Chart creation
Report generation
Event planning
Hive schemes
Mapping of infrastructure objects
Laboratory studies
Entry and viewing of diagnostic data
Innovations
Planned results of the predictive module in particular and the developed intelligent system as a whole possess scientific novelty and are of great interest for scientific publications and for writing Ph.D. and doctoral dissertations in the field of technical sciences.
- 4 models (1 analytical and 3 machine learning models)
- More than 100 model architecture modifications
- More than 4,000 models for each well at the research stage
- More than 10,000 hours of model training
- Up to 25,000 feature vectors for each well
