Assessment of alternative water management infrastructure strategies for natural gas extraction across cost, water use and greenhouse gas emissions

Lead University: Carnegie Mellon University
PI: W. Michael Griffin
Co-PI(s): H. Scott Matthews, CEE/EPP and Paul Fischbeck, Social and Decision Sciences

Shale gas development is water intensive, requiring 2-3 million gallons of water per well, and discharge of “used” water to waterways can be environmentally problematic. Consideration of the cost and environmental effectiveness for various strategies for water use is a critical need for the region. This proposed project, collaborative between CMU, ExxonMobil Research and Engineering (EMRE), and XTO Energy Inc., will create an improved modeling tool for considering alternative uses of water infrastructure associated with unconventional natural gas operations (e.g., shale gas) over it’s life cycle. CMU will build on a rough internal model created by EMRE, and working with XTO Energy (a Pennsylvania (PA) Marcellus operating company), will further develop the modeling framework to include economic costs, water use considerations, such as water treatment technologies, water quality goals required for re-use, disposal or alternative uses, and the greenhouse gas (GHG) emissions of the various strategies. The goal is a robust decision support tool that can be used by companies in the Marcellus region such as XTO Energy to balance environmental considerations with economic factors when managing water use at the extraction site.

We will work with XTO and EMRE to further consider the literature associated with unconventional natural gas associated water use and use this information to add depth and expand both the quality and scope of the model. This will make the model more broadly applicable to the corporate decision making process. The expanded framework will include new opportunities for underground injection, use and re-use strategies, and treatment technologies. The overall decision process will include robust consideration of uncertainties. The resulting model can be used to build organizational awareness of options and support strategic decision making regarding the value of water treatment, reuse and disposal options. The modeling will lead to cost-effective solutions that can be further developed and commercialized to protect local resources and the environment