On-Bottom Stability (OBS) is a subsea pipeline design and analysis software developed by PRCI and trusted globally for over 40 years. It provides engineers with advanced tools to assess pipeline stability under complex environmental conditions, including wave and current forces, soil-pipe interaction, and material behavior. OBS supports three levels of analysis, from simplified static models to advanced finite element simulations, making it suitable for a wide range of subsea pipeline projects.

Features

• Level 1 Analysis:
  • Fast, simplified static stability calculations for un-trenched pipelines.
  • Models displacement due to wave and current loads.
  • Includes options for logarithmic boundary layer formation and marine growth.
  • Supports cohesive and non-cohesive soil types.
• Level 2 Analysis:
  • Most commonly used by pipeline operators and service providers.
  • Comprehensive on-bottom pipeline design for clay and sand soils.
  • Parametric-run functionality for pipe wall thickness and water depth.
• Level 3 Analysis:
  • Designed for the most complex subsea projects.
  • Incorporates finite element analysis and random wave generation.
  • Computes environmental forces and produces velocity-time series for each point.
• Advanced Hydrodynamic Modeling:
  • Accounts for 3D irregular waves and shallow water complexities.
  • Models non-linear, time-dependent pipeline-soil interaction.
  • Supports non-linear material properties for boundary elements.

Benefits

• Global Standard: Used worldwide for subsea pipeline design and stability assurance.
• Scalable Analysis: Three-tiered approach supports everything from basic to highly complex projects.
• Risk Reduction: Helps prevent pipeline displacement and failure, saving millions in potential losses.
• Regulatory Confidence: Aligns with industry best practices and engineering standards.
• Engineering Efficiency: Parametric tools and batch processing streamline design workflows.
• Enhanced Accuracy: Finite element modeling and environmental force simulation improve prediction reliability.