Multiproduct pipelines permit to transport large volumes of a wide range of refined petroleum products from major supply sources to distribution centers near market areas. Batches of refined products and grades are pumped back-to-back in the same pipeline, often without any separation device between batches. The sequence and lengths of such pumping runs should be carefully selected in order to meet market demands at the promised dates while satisfying many pipeline operational constraints. This paper deals with the scheduling of a multiproduct pipeline system receiving a number of liquid products from a single refinery source to distribute them among several depots. A novel MILP continuous mathematical formulation that neither uses time discretization nor division of the pipeline into a number of single-product packs is presented. By developing a more rigorous problem representation, the quality of the pipeline schedule is significantly improved. Moreover, a severe reduction in binary variables and CPU time with regards to previous approaches is also achieved. To illustrate the proposed approach, a pair of real-world case studies was solved. Both involve the scheduling of a single pipeline carrying four oil derivatives from an oil refinery to five distribution depots. Higher pumping costs at daily peak periods were also considered. Compared with previous work, better solutions were found at much lower computational time.

The long-term planning of the shale gas supply chain is a relevant problem that has not been addressed before in the literature. This paper presents a mixed-integer nonlinear programming (MINLP) model to optimally determine the number of wells to drill at every location, the size of gas processing plants, the section and length of pipelines for gathering raw gas and delivering processed gas and by-products, the power of gas compressors, and the amount of freshwater required from reservoirs for drilling and hydraulic fracturing so as to maximize the economics of the project. Since the proposed model is a large-scale non-convex MINLP, we develop a decomposition approach based on successively refining a piecewise linear approximation of the objective function. Results on realistic instances show the importance of heavier hydrocarbons to the economics of the project, as well as the optimal usage of the infrastructure by properly planning the drilling strategy.

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