Natural gas represents the cleanest fossil fuel owing to its low level of the greenhouse gas emissions (compared with the rest of fossil fuels). For this reason, in conjunction to the recently discovered reserves in terms of unconventional gas as well as the enormous technological advances in drilling and in hydraulic fracturing have led a “revolution†for the primary energy production, which is economic and environmentally attractive. In this context, the Energy Information Administration (EIA) has published attractive data for the shale gas reserves around the world. Thus, shale gas has changed the outlook in terms of energy production. However, new concerns and challenges have also emerged for this industry such as the large amounts of water required for hydraulic fracturing phase, the large volumes of polluting effluents that are discharged to the environment (flowback fluid) etc. An important logistic task involved in the above-mentioned concerns is related to the scheduling due to an inconvenient plan to carry out the completion phase in the wells can cause great economic and environmental losses; in other words, if the scheduling for the hydraulic fracturing is not optimized, the obtained scheme will involve higher costs and a larger amount of required water. Hence, this work proposes a new optimization framework in order to determine the optimal scheduling for the hydraulic fracturing process, achieving economic and environmental benefits. It should be noted that this task involves highly non-convex models whereas the rest of the mathematical approach conducts to a convex model. Therefore, the overall methodology is composed by deterministic (able for the convex part) and metaheuristic (necessary to deal with the non-convex model) optimization.
The chosen software for the deterministic optimization part was general algebraic modeling system (GAMS) and the metaheuristic optimization algorithm is the improved multi-objective differential evolution coded in visual basic for applications (VBA). Likewise, a linking strategy is proposed through VBA code to execute the GAMS solver. The new solution strategy consists in manipulating the data that serve as parameters in the problem to be solved in the GAMS platform and evaluating the performance of the optimal values of the objective functions in the multi-objective differential evolution algorithm with updated values for the involved parameters. In this sense, this work proposes an optimization approach to the design of a water network for gas shale production considering the recycle and reuse of water to reduce the fresh water consumption and the decrease the discharge of polluting effluents where it is included a storage system and a sophisticated treatment system.
