Capacity Planning for Modular and Transportable Infrastructure for Shale Gas Production and Processing

被引:36
作者
Allen, R. Cory [1 ,2 ,3 ]
Allaire, Douglas [1 ]
El-Halwagi, Mahmoud M. [2 ]
机构
[1] Texas A&M Univ, Dept Mech Engn, College Stn, TX 77840 USA
[2] Texas A&M Univ, Artie McFerrin Dept Chem Engn, College Stn, TX 77843 USA
[3] Texas A&M Univ, Texas A&M Energy Inst, College Stn, TX 77843 USA
关键词
OPTIMAL DYNAMIC ALLOCATION; STRANDED NATURAL-GAS; PROCESS SYSTEMS; MOBILE PLANTS; OPTIMIZATION; MONETIZATION; WATER;
D O I
10.1021/acs.iecr.8b04255
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Shale gas wells typically have steep production decline curves in the first few years of operation. Therefore, if such reduction in production is not accounted for, much of the supporting infrastructure within the shale gas field owned by the exploration and production (E&P) company will be grossly oversized after only a few years of production. Instead of the conventional approach of utilizing spatially fixed processing facilities, this work proposes the use of modular and transportable processing plants. This in turn allows the processing facilities to be composed of multiple modular plants operating in parallel. These modular plants can be reallocated within the field to other processing facilities by the E&P company to combat the uncertainty in production that comes with developing a shale gas field. A superstructure is developed to aid in formulating the capacity planning and allocation problem as a multi-stage stochastic program with uncertain production forecasts. We incorporate a novel recourse function that allows the operator of the E&P company to quantify the effect of postponing the processing of the influent to a later time due to insufficient processing capacity. The proposed approach and solution technique are illustrated through a case study. For a set of randomly generated scenarios, the modular and transportable system shows major cost and operational benefits over the traditional permanent plants with fixed capacities.
引用
收藏
页码:5887 / 5897
页数:11
相关论文
共 37 条
[21]   NOVEL OPTIMIZATION APPROACH OF STOCHASTIC PLANNING-MODELS [J].
IERAPETRITOU, MG ;
PISTIKOPOULOS, EN .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1994, 33 (08) :1930-1942
[22]   Multi-Stage Adjustable Robust Optimization for Process Scheduling Under Uncertainty [J].
Lappas, Nikolaos H. ;
Gounaris, Chrysanthos E. .
AICHE JOURNAL, 2016, 62 (05) :1646-1667
[23]   Net Present Value Analysis of Modular Chemical Production Plants [J].
Lier, Stefan ;
Gruenewald, Marcus .
CHEMICAL ENGINEERING & TECHNOLOGY, 2011, 34 (05) :809-816
[24]  
Mattar L, 2008, SOC PETR ENG SHAL GA
[25]   Optimal Design of Water Distribution Networks with Incorporation of Uncertainties and Energy Nexus [J].
Mukherjee R. ;
González-Bravo R. ;
Nápoles-Rivera F. ;
Linke P. ;
Ponce-Ortega J.M. ;
M. El-Halwagi M. .
Process Integration and Optimization for Sustainability, 2017, 1 (04) :275-292
[26]   Simultaneous Energy and Water Optimisation in Shale Exploration [J].
Oke, Doris ;
Majozi, Thokozani ;
Mukherjee, Rajib ;
Sengupta, Debalina ;
El-Halwagi, Mahmoud M. .
PROCESSES, 2018, 6 (07)
[27]   Design, simulation and techno-economic analysis of two processes for the conversion of shale gas to ethylene [J].
Ortiz-Espinoza, Andrea P. ;
Noureldin, Mohamed M. B. ;
El-Halwagi, Mahmoud M. ;
Jimenez-Gutierrez, Arturo .
COMPUTERS & CHEMICAL ENGINEERING, 2017, 107 :237-246
[28]   Exploring the Benefits of Modular Renewable-Powered Ammonia Production: A Supply Chain Optimization Study [J].
Palys, Matthew J. ;
Allman, Andrew ;
Daoutidis, Prodromos .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2019, 58 (15) :5898-5908
[29]   Techno-Economic Assessment of Benzene Production from Shale Gas [J].
Perez-Uresti, Salvador I. ;
Adrian-Mendiola, Jorge M. ;
El-Halwagi, Mahmoud M. ;
Jimenez-Gutierrez, Arturo .
PROCESSES, 2017, 5 (03)
[30]  
Shapiro A., 2009, LECT STOCHASTIC PROG