(723c) Long- and Short-Term Optimization Model for Shale Gas Water Management
With the advancement in directional drilling and hydraulic fracturing, shale gas is predicted to provide almost half of the United States natural gas supply in twenty years. Since water use is associated with each step of the drilling and production process, it is an important aspect in shale gas development. The challenge with water management is that a large volume of it is required in a short-period of time. Water sources can be described as interruptible and non-interruptible sources. Whereas withdrawal from a non-interruptible source remains constant, withdrawal from an interruptible source can be affected by seasonal variation in water availability. As a result, even in water-rich regions, water use can be under strain due to the high demand. To this end, impoundments can be installed close to the water sources in order to serve as buffer tanks to store water when there is high freshwater supply in the sources. In order to avoid hauling freshwater to shale gas wellpads through trucks, overland or buried pipeline could be installed to transport freshwater from water sources to the wellpads. While permanent pipeline can be buried underground when the operators set up gas pipelines, overland pipelines are temporary and can be rented. Flowback water can be reused at the next frac through adequate treatment and blending with freshwater. Thus, based on the flowback water composition, different treatment techniques can be adopted.
We present a new mixed-integer linear programming (MILP) model for optimizing daily and long-term decisions in water use through a discrete-time representation of the State-Task Network. The proposed model extends the previous work by the authors (Yang et al., 2004), which dealt only with operations, to include capital investment decisions. Specifically, the objective is to minimize the overall cost including capital cost of impoundment, piping, and treatment facility, and operating cost including freshwater, pumping, and treatment. Given are the potential freshwater source location and withdrawal data, potential impoundment location, wellpad storage, location, and total number of stages, treatment unit capability and location, and the number of frac crews available. The goal is to determine the location and capacity of impoundment, the type of piping, treatment facility locations and removal capability, as well as the frac schedule, and the water sources to obtain freshwater. In addition, we examine the impact truck hauling restriction has on the overall cost and frac schedule. The problem is optimized over a long planning horizon, which increases the computational difficulty for solving the MILP model. A real-world case study in the Utica Shale is optimized to illustrate the application of the proposed formulation.
Yang, L., J. Manno and I.E. Grossmann, “Optimization Models for Shale Gas Water Management,” submitted for publication (2014).