551464 Optimal Allocation of Modular and Transportable Methane Processing Units for Use in Oil and Gas Fields

Thursday, June 6, 2019: 11:42 AM
Republic ABC (Grand Hyatt San Antonio)
R. Cory Allen1,2, Styliani Avraamidou1,2, Douglas L. Allaire3, Mahmoud M. El-Halwagi4 and Efstratios N. Pistikopoulos1,2, (1)Texas A&M Energy Institute, Texas A&M University, College Station, TX, (2)Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, (3)J. Mike Walker ‘66 Department of Mechanical Engineering, Texas A&M University, College Station, (4)The Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX

Optimal Allocation of Modular and Transportable Methane Processing Units for Use in Oil and Gas Fields

R. Cory Allen1,2, Styliani Avraamidou1,2, Douglas L. Allaire3, Mahmoud M. El-Halwagi2, Efstratios N. Pistikopoulos1,2*

  1. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
  2. Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
  3. J. Mike Walker ‘66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA

*stratos@tamu.edu

Keywords: Two Stage Stochastic Programming, Large Scale Optimization, Process Systems Engineering

Production from tight oil and shale gas wells is expected to account for over two-thirds and three-fourths respectively of the total amount of oil and gas produced in the United States by 2050 [1]. These unconventional wells have steep production decline rates [2] and many tight oil fields lack the proper infrastructure to transport all of the associated natural gas to the market. In order to reduce the capital and operational cost of processing facilities, modular and transportable methane processing units have been suggested as they can be reallocated in the field to meet changes in production, or can be used to abate or cease flaring of the stranded associated gas [3, 4, 5, 6].

This work is based on our earlier work on the capacity planning and scheduling optimization framework for modular processing units in a shale gas field [6]. The framework, which is modeled as a stochastic mixed integer linear program, allows for uncertainty with respect to the influent flow rate into each of the processing facilities in field, which are comprised of modular processing units. In an effort to increase the scale of the problems solved in [6], we introduce cutting planes, which exploit the block diagonal structure of the feasible solution region, as well as employ lagrangian relaxations, which reduces the computational burden of the non-anticipatory constraints. This consequently allows for solutions of large scale problems, that often accompany the development of oil and gas fields due to the number of wells being drilled and well as their accompanying production uncertainties to be found.

References:

[1] United States Energy Information Administration. (2018). Annual Energy Outlook 2018: with projections to 2050. Washington, DC.

[2] United States Energy Information Administration. (2018). Drilling Productivity Report November 2018: for key tight oil and shale gas regions. Washington, DC.

[3] Baldea, M., Edgar, T. F., Stanley, B. L., & Kiss, A. A. (2017). Modular manufacturing processes: status, challenges, and opportunities. AIChE Journal, 63(10), 4262-4272.

[4] Yang, M., & You, F. (2018). Modular methanol manufacturing from shale gas: techno‐economic and environmental analyses of conventional large‐scale production versus small‐scale distributed, modular processing. AIChE Journal, 64(2), 495-510.

[5] Tan, S. H., & Barton, P. I. (2016). Optimal dynamic allocation of mobile plants to monetize associated or stranded natural gas, part II: dealing with uncertainty. Energy, 96, 461-467

[6] Allen, R. C., Allaire, D., & El-Halwagi, M. M. (2018). Capacity Planning for Modular and Transportable Infrastructure for Shale Gas Production and Processing. Industrial & Engineering Chemistry Research.


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