While planning transmission pipeline expansions, engineers must decide which pipe diameters to use as well as where to locate compressor stations. Engineers must make these design decisions based on the desired capacity of the pipeline, the cost of the pipe used, and the cost of compression. A significant amount of time may be spent running case studies to determine the optimum compressor station location and pipe sizes. This paper presents an automated technique for locating compressor stations throughout the pipeline based on pipeline and compressor constraints. The compressor location data along with pipeline and compressor costs are generated for a series of pipe diameters and pressure scenarios. The resultant design cases are then analyzed to uncover data trends and design alternatives which allow the engineer to make informed pipeline design decisions.


Designing a gas transmission pipeline requires a significant amount of engineering and economic analysis. Not only must a suitable pipeline route be selected, but, based on the design capacity, engineers must decide on pipe diameters, internal pipe coatings, pipeline maximum allowable operating pressures (MAOP), and optimum locations and sizes of compressor stations. Pipeline installation and operating costs depend heavily on these decisions, as each of these comes with an associated cost. The price of pipe installation increases significantly with pipe diameter. If larger MAOPs are desired, pipes must have thicker walls with sufficient hoop strengths to meet the greater pressure demands leading to larger capital expenses. Compression comes with significant operating costs including fuel, maintenance and management costs.

There is a tradeoff between these selected parameters. For a given capacity, if larger pipe diameters or higher MAOPs are chosen, compressors may be located further apart reducing compression costs, but pipe installation costs increase. Conversely, if compressors are placed closer together, pipe capital costs may be reduced by using smaller diameter or thinner walled pipes at the expense of higher compression costs. Large amounts of money can be saved by selecting the optimum set of pipeline parameters. Johnson and Wallooppillai (2006) give an excellent description of the process of designing a pipeline, along with the associated costs, while Arsegianto et al. (2003) and Elchiekh et al. (2013) give descriptions of the costs associated with pipeline construction and operation.

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