Pipeline Flow Modeling for Slack Regions
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Solution Overview
Problem
Conventional pipeline flow models fail to accurately predict flow conditions in slack regions, leading to non-physical results, computational instability, and compromised leak detection and batch tracking capabilities due to the decoupling of pressure from frictional loss and the formation of vapor phases within pipelines transporting volatile hydrocarbons.
Innovation Solution
A method that iteratively determines the conditions of state in pipeline cells by customizing equations based on flow modes, using specific area equations for tight, slack, or minimum area flow modes, enabling stable modeling of transient operations and improved leak detection and batch tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pipeline flow models are used to model tight flow regions, then the physics is well understood and equations of conservation can be applied, but the models fail to accurately predict flow conditions in slack regions leading to non-physical results
Solution Approach 1:
The pipeline is divided into multiple computational cells, and the model selectively applies different equations to each cell based on its flow mode (tight or slack). This segmentation allows the system to maintain high accuracy for tight flow regions while adapting to slack flow conditions, resolving the contradiction between reliability for tight flow and adaptability for slack flow.
Solution Approach 2:
Different mathematical equations and modeling approaches are applied to different segments of the pipeline based on local flow conditions. Tight flow regions use conventional conservation equations, while slack flow regions use specialized equations that account for vapor phase formation and pressure decoupling. This local quality approach enables the model to be accurate for each specific flow mode while maintaining overall system versatility.
2Use of energy by moving object
If the pipeline head is allowed to drop below peak elevations to reduce pumping costs, then energy efficiency improves, but slack flow occurs causing computational instability and compromised leak detection
Solution Approach 1:
The model dynamically adjusts the mathematical equations used in each computational cell based on real-time flow conditions. When the pipeline head drops below peak elevations and slack flow develops, the model automatically switches to appropriate slack flow equations that maintain computational stability. This dynamic adaptation allows energy-efficient operation below peak elevations without sacrificing modeling reliability.
Solution Approach 2:
The model changes key parameters such as pressure, density, and flow regime classifications based on local conditions in each computational cell. By detecting when head drops below elevation and transitioning to slack flow parameter sets, the model maintains stability even when operating at lower, more energy-efficient pumping levels. This parameter adaptation resolves the contradiction between energy use and modeling stability.
3Productivity
If lighter components evaporate to form vapor phase within the pipeline, then the volatility of the product is expressed, but the mass of vapor phase becomes negligible and pressure decouples from frictional loss
Solution Approach 1:
The model introduces an intermediary approach by using iterative solving methods that account for the negligible vapor phase mass while maintaining the relationship between pressure and frictional loss through the liquid phase. The vapor phase is treated as a separate entity that influences pressure distribution but does not dominate the mass balance, allowing the model to capture evaporation effects without losing the critical pressure-friction relationship needed for leak detection.
Solution Approach 2:
The flow model treats the two-phase system as a composite where the liquid phase carries the bulk mass and momentum while the vapor phase occupies the remaining space and influences pressure. This composite approach allows the model to account for evaporation and vapor formation while maintaining accurate tracking of pressure changes relative to frictional losses in the liquid-dominated flow, preserving leak detection capability.
4Reliability
If iterative determination of cell conditions with customized equations is used, then accurate and stable modeling of transitions between flow modes is achieved, but computational complexity increases
Solution Approach 1:
The complex modeling task is segmented into discrete computational cells, each handled independently with equations customized to its specific flow mode. This segmentation breaks down the overall computational complexity into manageable pieces while maintaining high reliability through mode-appropriate equations. The iterative process operates on this segmented structure, improving stability without overwhelming computational complexity.
Solution Approach 2:
The model applies customized equations selectively only to computational cells where needed rather than throughout the entire pipeline. Most cells may use standard equations, while only those experiencing slack flow or transitions receive the more complex customized treatment. This partial application of complex equations maintains computational efficiency while achieving the stability and accuracy benefits where required.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides accurate and stable modeling of pipeline flow, including transitions between tight and slack flow modes, enhancing leak detection and batch tracking capabilities while maintaining pipeline head within safe operating pressures.
Implementation Method 1
The lighter (lower molecular weight) components (e.g., pentane and hexane) will, at a pressure below the vapor pressure, evaporate to form a vapor phase within the pipeline. Evaporation of lighter components of volatile products is promoted by low pressures (below the vapor pressure) and high temperatures.
Implementation Method 2
In OCF, the liquid phase flows downstream through the pipeline under the influence of gravity and under generally constant pressure.
Data Source
AI summary
A method of modeling a segment of a pipeline transporting a product comprising defining within the segment a plurality of discrete cells, each disposed between knots, preparing a system of equations relating the conservations of mass, momentum and energy for each cell along with equations for the liquid phase flow area of cells with tight, slack and minimum area flow modes, providing data relating to the product and the location and elevation of the cells, sensing a plurality of conditions within known cells, solving the system of equations, initiating a re-stepping process by re-assessing the flow modes of each cell and re-setting flow modes for cells with unstable flow modes, and resolving the system of equations using stable flow modes. An embodiment of the method includes excepting one or more cells from the re-stepping portion where a recurrent pattern of flow mode change is detected.