Markus Drouven and Diego Cafaro - Center for Advanced Process Decision-making
Department of Chemical Engineering - Carnegie Mellon University, Estados Unidos
Ingeniero Químico de la Universidad Iberoamericana, Méjico y Doctor en Ingeniería Química en el Imperial College, Inglaterra. Actualmente se desempeña como profesor en el Departamento de Ingeniería Química, del cual ha sido Director. Su trabajo se orienta al desarrollo de nuevos modelos de programación matemática y técnicas para una variedad de problemas en la ingeniería de sistemas de procesos como la optimización global y basada en lógica, la planificación de corto, mediano y largo plazo de procesos y la optimización de la utilización de agua en procesos y sistemas de producción de shale gas. Ha dictado seminarios en universidades de los EE.UU. y numerosos países y posee 480 publicaciones en revistas especializadas. Es director del "Center for Advanced Process Decision-making", que cuenta con un total de 20 compañías petroleras, químicas y de ingeniería.
Abstract:
Optimization Models for Optimal Investment, Drilling and Water Management for Shale Gas Development
Shale gas development is transforming the energy landscape in the United States. Advances in production technologies, notably the dual application of horizontal drilling and hydraulic fracturing, allow the extraction of vast deposits of trapped natural gas that, until recently, were uneconomic to produce. The objective of this presentation is to describe mixed-integer programming models that can support upstream operators in making faster and better decisions that ensure low-cost and responsible natural gas production from shale formations.
We first describe a multiperiod mixed-integer nonlinear programming (MINLP) model along with a tailored solution strategy for strategic, quality-sensitive shale gas development planning. The proposed model coordinates planning and design decisions to maximize the net present value of a field-wide development project. By performing a lookback analysis based on data from a shale gas producer in the Appalachian Basin, we find that return-to-pad operations are the key to cost-effective shale gas development strategies. We next address impaired water management challenges in active development areas through a multiperiod mixed-integer linear programming (MILP) model. This model is designed to schedule the sequence of fracturing jobs and coordinate impaired- and freshwater deliveries to minimize water management expenses, while simultaneously maximizing revenues from gas sales. Based on the results of a real-world case study, we conclude that rigorous optimization can support upstream operators in cost-effectively reducing freshwater consumption significantly, while also achieving effective impaired water disposal rates of less than one percent. We also propose a multiperiod MINLP model and a tailor-designed solution strategy for line pressure optimization in shale gas gathering systems. The proposed model determines when prospective wells should be turned in-line, and how the pressure profile within a gathering network needs to be managed to maximize the net present value of a development project. We find that backoff effects associated with turn-in line operations can be mitigated through preventive line pressure manipulations. Finally, we develop deterministic and stochastic MILP models for refracturing planning. These models are designed to determine whether or not a shale well should be restimulated, and when exactly to ref