Optimization of Natural Gas Distribution in Pipeline Networks
Open Access
- Author:
- Pruvot, Antoine
- Graduate Program:
- Petroleum and Natural Gas Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- May 11, 2015
- Committee Members:
- Dr Luis Ayala, Thesis Advisor/Co-Advisor
Chiara Lo Prete, Thesis Advisor/Co-Advisor
Andrew Nathan Kleit, Thesis Advisor/Co-Advisor - Keywords:
- pipeline
pipe
economic surplus
natural gas
weymouth
flow
optimization
distribution
network - Abstract:
- In natural gas pipeline transportation systems, network operators play a crucial role. Through compression power and pipeline geometry, they master the physics of the systems, allowing them to control the flow of gas between two points. Their decisions impact the entire production chain, from the suppliers to the consumers. Consequently, the management of pipeline systems requires an in-depth analysis of the influence of each decision. Each pressure change in the system may seriously impact the flow of natural gas, deeply modifying the revenue of the entire production and how it is divided between the different actors of the market. It is fundamental to understand how to master the system in order to control the money generated. From an economic point of view, natural gas pipeline production, transportation and sale creates wealth divided between the different actors in the sector: the profit of the producer, the consumer welfare and a combination of both for the network operator. This social wealth, should be maximized in order to generate the most benefit from the network for society. In order to do so, it is necessary to understand how much gas is flowing through each pipeline. If pressure values are fixed on an arbitrary basis, the dispatch of natural gas in the network will not be optimized. The loss of social wealth generated can be considerable given the important volumes transported through pipeline those days. In the market of natural gas transportation, if the pressure at the nodes is wrongly chosen, it could be disastrous for a company. How could any producing/transporting company avoid wasting this significant amount of money? What are the solutions available for the natural gas pipeline engineers to dispatch natural gas in order to maximize the social wealth generated? This issue can be stated in the corresponding two situations: • For the construction of a new pipeline network, how should the geometry of the different pipes be chosen in order to transport natural gas in an optimal way? • For an existing pipeline network, how should the pressure drops be chosen to maximize the social wealth of the producing/transporting company? The goal of this study is to provide network operators with the parameters to answer those situations. By fixing the pressure values at the nodes of the system, it is possible to maximize the economic value generated by the natural gas transportation and sales. Additionally, running the simulation on different natural gas network configurations = inform the company on how to choose the ideal geometry factors of each branch of pipeline. Midthun et al. (2009) suggested two different methods to address this problem. The first one, the Independent Static Flow (ISF) method is a straightforward way to find a solution. Neglecting the physics of natural gas, this method assumes that every pipe of the system is running at maximum capacity. The method is very easy to use and implement. Nevertheless, the solution provided is unrealistic: as the physics of natural gas is not respected, it is impossible to practically apply the method on a real network. Hence, this method can only be used to give an idea of how to regulate the flows, and an operator could only try to guess the pressure values at the nodes that could help to get closer this ideal situation on his network. The loss of economic value of natural gas from the arbitrary choices of the operator is a concern. Additionally, the solution arbitrary applied by the operator will generate far less social wealth than the ideal solution given through ISF Method due to the application of the physics of natural gas transportation. To address this issue, the second method proposed by Midthun et al. (2009), the Taylor Development Method, relies on an approximation of the underlying physics to solve for the optimal solution. In order to improve the relevance of the results to the constraints of the pipeline network, Midthun et al. decided to modify the nonlinear constraints of the system, .However, the accuracy of this approach has a price: the more accurate the solution, the more computationally difficult the optimization becomes. Figure 1: The fragile optimum for the Taylor Development Method Figure 1 illustrates this complex choice. Thus, the user remains struggled in a compromise to find the right equilibrium between quality of the result and time (and so money) of computation. The situation is even worse for large network, as the number of constraining equations greatly increases for each additional pipeline on a network. This compromise between size of network/quality of results on one hand and computational feasibility on the other hand cannot be satisfying. Today, natural gas companies have to deal with networks of several hundred of pipes. An accurate solution would be too hard to solve for, and decreasing the accuracy expectations may cause a large waste of social wealth. In order to avoid this loss, this paper is suggesting another method, based on Ayala et. al.’s (2013) Linear-Pressure Analog Method. Instead of adding extra constraining equations to take account for the nonlinearities of natural gas physics, it is possible to simplify the system. Assuming a linear relationship between natural gas flow rate with respect to pressure drop, the system become smaller and easier to solve. In other words, physics of natural gas is assumed to be similar to the one of laminar liquid flows. From here, a correction is applied to the solution found, taking account for the nonlinearities inherent in real natural gas behavior. The process is iterated until convergence is reached. This method is both feasible and accurate with limited computational demands. Consequently, with any standard computer, a production/transportation company can obtain the ideal and realistic dispatch of natural gas in its network, and optimize the economic value generated by its natural gas transportation.