Shale gas has, in recent years, emerged as a possible transition fuel that will facilitate the transformation from coal-based energy systems to less CO2intensive energy systems , . However, environmental impacts, especially impact on water resources, have given rise to questions and concerns and thus must be addressed in making decision related to shale development –. Given the cost-benefit tradeoffs , the assessment of shale gas resources has captured the attention of the research community – .
The planning and development of shale gas supply chains involves a number of strategic decisions that must be made to optimize the economic performance of such an investment. Three key aspects must be considered in seeking to optimize the exploitation of shale resources: (1) production schemes, which involve drilling strategy, well design, and well clustering (well-pads); (2) water management, which includes decisions such as fresh water procurement, produced water treatment (plant locations and capacities) and disposal options; and finally (3) gas processing, which encompasses gas treatment facilities decisions (plant locations and capacities), and the design of gas pipeline networks. Certainly, the simultaneous consideration of the aforementioned aspects provides substantial modeling and computation challenges. Some studies have focused mainly in the assessment of shale gas production with little or no detail concerning the gas and water transport and processing infrastructure –, . Others have primarily focused on the production schemes . However, recently, Guerra et al. (2015) developed a comprehensive integrated shale gas supply chain optimization model that takes into account the different options regarding well-pad design, gas and water transport and the processing infrastructure. In its general form, the resulting optimization model is a Mixed-Integer Non-Linear Programming (MINLP) problem. However, a special case can be derived by assuming that the gas composition is constant across the shale reservoir; this simplification reduces the complexity of the model to a Mixed-Integer Linear Programming (MILP) problem. However, the general problem is an MINLP and as shown by the authors, solution of this problem using commercially available MILP/MINLP solvers can take substantial computation times or require the use of specialized solution strategies that exploit the structure and characteristics of the problem.
In this work, we explore different solution approaches for solving the large-scale shale gas supply chain optimization problem. The solution strategies employ three different devices used in the math programming literature: (1) Model reformulation, where the focus is on reducing the complexity of the model while retaining its accuracy, (2) computational enhancements, such as the use of tighter upper and lower bounds, column and row scaling, and specially structured integer cuts and finally (3) convexification and decomposition techniques. The effectiveness of these strategies are investigated using a range of case studies involving formulations constructed using data from Colombia.
Corresponding author: G.V. Reklaitis (firstname.lastname@example.org).
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