377394 Optimal Dynamic Allocation of Mobile Plants to Monetize Associated or Stranded Natural Gas Under Uncertainty

Thursday, November 20, 2014: 8:49 AM
401 - 402 (Hilton Atlanta)
Siah Hong Tan, Dept. of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA and Paul I. Barton, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA

Associated or stranded natural gas presents a particular challenge to monetize due to its low volume and lack of supporting infrastructure. Recent proposals for deploying mobile, modular plants, such as those which perform gas-to-liquids (GTL) conversion or liquefied natural gas (LNG) on a small scale have been identified as possible attractive routes to gas monetization. However, such modular technologies are yet unproven in the marketplace. In addition, uncertainty about supply, demand and price conditions greatly influence the outcome of investment decisions in the oil and gas industry. To address these issues, we propose a multi-stage stochastic programming framework which determines the most optimal allocation decisions for a decision-maker intending to make use of mobile plants to monetize associated or stranded gas under uncertainty. We then demonstrate how this framework can be applied to a real-world case study of the Bakken shale play in North Dakota, where large amounts of associated gas are currently being flared. The development of this framework paves the way for decision-makers to screen and evaluate unproven modular technologies systematically and also provides a robust strategy for profit.

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See more of this Session: Supply Chain Optimization
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