Date of Award

Spring 4-2017

Embargo Period

5-9-2017

Degree Type

Dissertation (CMU Access Only)

Degree Name

Doctor of Philosophy (PhD)

Department

Chemical Engineering

Advisor(s)

Ignacio E. Grossmann

Abstract

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 work is to develop mixed-integer programming models to support upstream operators in making faster and better decisions that ensure low-cost and responsible natural gas production from shale formations. We propose a multiperiod mixed-integer nonlinear programming (MINLP) model along with a tailored solution strategy for strategic, quality-sensitive shale gas development planning. The presented 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 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 presented 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 refracture it. The stochastic refracturing planning model explicitly considers exogenous price forecast uncertainty and endogenous well performance uncertainty. Our results suggest that refracturing is a promising strategy for combatting the characteristically steep decline curves of shale gas wells.

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