Abstract
Various mathematical models have so far been developed for assessing flow efficiency in oil and gas pipelines. However, no such models for assessing flow efficiency have been developed for petroleum products pipelines. The main objectives of this study is to assess and predict the effect of deposits and corrosion on flow efficiency in petroleum products pipelines in addition to developing an optimal pigging frequency model to reduce the effects. To achieve this, a novel advanced analytical method for analysing turbulent flow in petroleum products pipelines along with existing mathematical models, regression, statistical tools and artificial neural networks were applied. In-line inspection data (ILI) Supervisory Control and Data Acquisition (SCADA), and pigging data obtained from a main operational transmission petroleum products pipeline located in Kenya were used in the study. Due to the numeric variables involved, a quantitative approach was preferred for the analysis.The main findings obtained from the study were that deposits and corrosion negatively affect flow efficiency in petroleum products pipelines. In this study, about 20% of flow efficiency was lost due to deposits while 0.3% of flow efficiency was lost due to the presence of corrosion pits in the pipeline. An optimal pigging frequency model was hereby recommended to reduce the effects. In addition, it was found that application of machine learning that is, artificial neural networks resulted to actual flow efficiencies in the pipelines with an error of 2%.
The key contributions were a novel advanced analytical method for assessing turbulent flow rate, and three mathematical models for assessing the effects of deposits, corrosion, and pigging frequency on flow efficiency in petroleum products pipelines. In addition, a flow efficiency application chart for analysing flow efficiency in petroleum products pipelines and corresponding flow efficiency application spreadsheets (FEAS) were developed in the study. These would provide a fast and economical means of assessing and improving flow efficiency in the pipelines. The current cost of in-line inspection in petroleum products pipelines is $33,000/mile due to high cost of equipment and labor involved while that of Supervisory Control and Data Acquisition is $100,000/system . This study will provide alternative economical solutions to the current challenges affecting flow efficiency in petroleum products pipelines in addition to enhancing safety.
The main challenge in oil and gas industry is to maintain adequate flow efficiency in the pipelines due to various factors such as the presence of deposits and corrosion in the pipelines. The flow efficiency application chart and the flow efficiency application spreadsheets developed in the study will largely help to inform this problem as well as reduce potential catastrophes such as fires and leakages rampant in petroleum products pipelines. The findings and contributions will go a long way to aid pipeline operators and engineers maintain high integrity in the maintenance, functions, and operations of the pipelines attracting higher profits for petroleum products pipeline companies and a higher revenue for the economy.
Date of Award | 2024 |
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Original language | English |
Sponsors | Kenyan Pipeline Company Limited |
Supervisor | Paul Davies (Supervisor), John Kinuthia (Supervisor) & Catherine Ngila (Supervisor) |
Keywords
- Pipelines
- Integrity
- Deposits
- Corrosion
- Efficiency
- Regression
- Models
- Statistics
- Simulations
- ILI
- SCADA
- ANN
- Petroleum
- Products