Pipeline Pigging Oil Flow Analysis - ANSYS Fluent CFD Training

Project Overview

This computational fluid dynamics investigation examines pigging operations within oil pipeline systems using ANSYS Fluent software. The simulation incorporates a Pipeline Inspection Gauge (pig) positioned within the pipeline structure to analyze flow dynamics and operational impacts.

Pipeline Pigging Technology

Pipeline Inspection Gauges serve multiple critical functions including geometric and fluid parameter monitoring, pipeline cleaning operations, and creating physical separation barriers between different fluid types. Pigging operations encompass the deployment, control, and navigation of these devices through pipeline networks for maintenance and inspection purposes.

Engineering Challenge

The presence of pigs within fluid flow pathways creates flow obstruction, resulting in significant pressure differentials across the device. This pressure drop phenomenon represents a key operational concern requiring detailed analysis to optimize pigging efficiency while minimizing flow disruption.

Simulation Objectives and Scope

Primary Investigation Goals

The simulation focuses on analyzing fluid behavior surrounding the pig body within the pipeline environment, with particular emphasis on pressure differential characterization across the upstream and downstream pig surfaces.

Operational Parameter Analysis

The study examines two distinct flow scenarios featuring oil inlet velocities of 0.9 m/s and 1.9 m/s, enabling comparative analysis of velocity-dependent pigging effects on system performance.

Multiphase Flow Modeling

A Volume of Fluid (VOF) multiphase approach defines the gas-oil and petroleum material interactions within the pipeline system, providing comprehensive representation of real-world pigging conditions.

Geometric Configuration and Computational Grid

Two-Dimensional Model Development

The pipeline geometry was constructed using Design Modeler software, incorporating a simplified stationary pig configuration within a representative pipeline cross-section.

Computational Mesh Characteristics

The computational grid was developed using ANSYS Meshing software with an unstructured mesh configuration containing 5,789 elements. The mesh quality was optimized specifically for capturing pig-fluid interaction phenomena and ensuring accurate flow field resolution around the pig geometry.

CFD Simulation Configuration

Fundamental Modeling Assumptions

The simulation employs a pressure-based solver implementation for incompressible flow analysis with a transient approach for temporal pressure drop analysis. Gravitational effects are neglected to provide simplified flow analysis focusing on the primary pigging phenomena.

Turbulence and Multiphase Modeling Framework

The viscous modeling utilizes the k-epsilon standard turbulence model with standard near-wall treatment for boundary layer resolution. The multiphase flow is handled through the VOF method incorporating two Eulerian phases with implicit formulation and sharp interface modeling to accurately capture the gas-oil and petroleum phase interactions.

Boundary Condition Specifications

The pipeline inlet is configured as a velocity inlet with variable velocity settings of 0.9 m/s and 1.9 m/s depending on the simulation case, with petroleum volume fraction set to unity and gas-oil volume fraction at zero. The pipeline outlet employs a pressure outlet boundary condition with zero gauge pressure. Both the pipeline walls and pig surfaces are treated as stationary walls with no-slip boundary conditions to accurately represent the physical constraints.

Numerical Solution Methods

The pressure-velocity coupling utilizes the SIMPLE algorithm for solution convergence. Pressure discretization employs the PRESTO scheme for enhanced accuracy in complex geometries. Momentum equations are solved using second-order upwind discretization, while volume fraction transport uses the compressive scheme to maintain sharp interfaces. Turbulent kinetic energy and dissipation rate equations employ first-order upwind discretization for numerical stability.

Temporal Configuration

The simulation runs for a total duration of 90 seconds with a time step of 0.03 seconds, resulting in 3,000 total time steps. This temporal resolution provides adequate capture of transient pressure drop development and flow field evolution around the pig geometry.

Initial Conditions

Standard initialization is applied throughout the computational domain with zero gauge pressure, zero x-velocity component, and y-velocity corresponding to the respective inlet conditions for each simulation case. The petroleum volume fraction is initialized to zero throughout the domain, allowing the inlet boundary condition to drive the phase distribution.

Results and Engineering Analysis

Flow Field Characterization

The simulation generates comprehensive two-dimensional contour visualizations depicting pressure distribution, velocity fields, and phase volume fractions for both defined fluid phases. All contour results represent final time-step conditions at 90 seconds, providing complete characterization of the developed flow field around the pig geometry.

Pressure Drop Analysis

The investigation reveals pressure differential patterns across the pig geometry, providing quantitative data on flow obstruction effects at different operational velocities. These results enable optimization of pigging operations while maintaining acceptable pressure losses and demonstrate the relationship between flow velocity and pressure drop magnitude.

Velocity-Dependent Performance

Comparative analysis between 0.9 m/s and 1.9 m/s inlet conditions demonstrates the relationship between flow velocity and pigging efficiency, offering insights for operational parameter selection in real-world applications. The higher velocity case shows increased pressure differentials and more pronounced flow disturbances around the pig body.

Engineering Applications

The simulation results provide valuable data for pipeline operators regarding pigging operation planning, pressure drop predictions, and system performance optimization during inspection and maintenance activities. The temporal analysis capability allows for understanding of transient effects during pig deployment and retrieval operations.