(28d) Shale Gas Supply Chain Network Design and Operation Incorporating Rigorous Well Simulations

In recent years, technological advances have provided the necessary tools for exploitation of new sources of fossil fuels in the United States. Particularly, the development of horizontal drilling and hydraulic fracturing technologies has turned the shale gas into one of the most rapidly growing businesses in the energy sector. As a result, shale gas production as a percentage of total dry gas production has continuously increased in the last few years. While shale gas production represented only 5% of total U.S. dry gas production in 2004, now it is contributing to approximately 56% of the total dry gas production.¹ Given this enormous potential, the focus has shifted from recognizing available natural gas resources toward coordinating the necessary operations to allocate these resources into production and delivery of subsequent products to different types of markets.

Although previous works have presented different approaches to tackle the optimal design and operation of the shale gas supply chain^2,3, a major gap still exists regarding to the incorporation of practical unconventional shale gas drilling and stimulation strategies and market volatility in proposed optimization frameworks. In order to develop the most profitable strategy for maximizing production from a reservoir, the optimal number of infill wells, well footage and number of stages per well must be established. Since sharp decline rates are inevitable during the first few months to a year of a production, a method of rejuvenation becomes necessary. Hydraulic re-fracturing presents a cost effective method for optimizing shale gas production from declining wells because it avoids the higher costs involved in drilling and developing new wells. Another important aspect that deserves attention in selecting the optimal drilling strategy is land acquisition which can have a significant impact on the costs, especially at the beginning of a shale gas project. On the other hand, market volatility also arises other important issues related to the fluctuation of productsâ?? prices. Considering pricesâ?? randomness can allow the determination of a more efficient and flexible supply chain. Moreover, the presence of different types of markets including internal and external can also introduce flexibility in the supply chain to appease unfavorable internal market conditions.

In this study, a framework is proposed to determine the optimal design and operation of a shale gas supply chain network. The aforementioned aspects are incorporated into the optimization strategy through development of a complex mathematical formulation. An unconventional reservoir model is built using CMGâ??s GEM, in order to obtain realistic decline profiles for the specified producing wells under consideration. Reservoir properties within the Marcellus Shale play are used in our proposed case study. Based on the results of the simulation, the dual effect of number of fractures stages and lateral length on well productivity is incorporated into the optimization strategy. Furthermore, the inherent randomness of market prices is treated through the utilization of a scenario-based stochastic approach. A binomial option pricing model is utilized to approximate the stochastic process involved in the determination of future final products prices. Finally, external markets are incorporated into the model in order to provide the possibility of exporting certain products to counteract unfavorable prices conditions of internal markets.

References

1. EIA. The growth of U.S. natural gas: an uncertain outlook for U.S. and world supply in the EIA Energy Conference. Washington, DC: U.S. Energy Information Administration, 2015.

2. Cafaro DC, Grossmann IE. Strategic planning, design, and development of the shale gas supply chain network. AIChE J. 2014;60: 2122â??2142.

3. Gao J, You F. Shale gas supply chain design and operations toward better economic and life cycle environmental performance: MINLP model and global optimization algorithm. ACS Sustain Chem Eng. 2015;3:1282â??1291.