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Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
Published on: June 12, 2016
Shale gas wastewater management under uncertainty
Xiaodong Zhang1, Alexander Y Sun2, Ian J Duncan2
1EES-16, Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78713, USA.
This study introduces an optimization framework to manage wastewater from hydraulic fracturing (HF), balancing cost and uncertainty. The UO-FPW model helps plan treatment and disposal strategies for flowback and produced water (FP water).
Area of Science:
- Environmental Engineering
- Water Resource Management
- Operations Research
Background:
- Hydraulic fracturing (HF) generates significant wastewater (FP water) with toxic chemicals and high TDS.
- Wastewater management is a critical environmental and public health challenge in shale gas production.
- Existing treatment/disposal options include underground injection, treatment plants, and reuse.
Purpose of the Study:
- To develop an optimization framework for evaluating wastewater treatment/disposal options during HF.
- To create a model (UO-FPW) that accounts for cost-effectiveness and system uncertainty.
- To aid in planning FP water management practices and treatment facility capacity.
Main Methods:
- Development of the UO-FPW optimization model.
- Incorporation of fuzzy membership functions and probability density functions to handle uncertainty.
- Application to a hypothetical case study to demonstrate practical use.
Main Results:
- The model reflects tradeoffs between minimizing costs and ensuring system reliability.
- It addresses the risk of violating constraints and meeting FP water treatment/disposal requirements.
- Demonstrates the model's applicability in practical decision-making.
Conclusions:
- The UO-FPW model provides a tool for optimizing FP water management strategies.
- Decision-makers can adjust strategies by refining feasibility and probability levels.
- The model can be integrated into decision support systems for shale oil/gas management.
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