International Journal of Nonlinear Analysis and Applications

A two-stage stochastic programming model for the CCS-EOR planning problem

Document Type : Research Paper

Author

Faculty of Science, Arak University, Arak, Iran

Abstract

Carbon-capture-and-storage (CCS) is an important technology to reduce CO$_2$ emissions. A commercial method to establish the CCS on a large scale is to sequestrate CO$_2$ in depleted oil reservoirs and to combine it with enhanced oil recovery (EOR) operations. In this way, the CO$_2$ emission is reduced, as well as the oil production increases. The joint CCS-EOR planning problem determines the optimum allocation of existing CO$_2$  to depleted reservoirs and the scheduling of the EOR operations. This paper presents a deterministic MIP model which is a modification of an existing model in the literature. Then, this model is extended to a two-stage stochastic model in which the parameters expressing the initial oil yields and the periodic depletion factor of oil yields associated with reservoirs are uncertain, and the uncertainty is realized as soon as the operation of the reservoir is started. Our stochastic model is computationally more efficient than the existing model in the literature, due to the reduction of binary variables, as well as the absence of “non-anticipativity constraints”. Instead, our stochastic model is less realistic. The proposed models are examined over two case studies taken from the literature. The obtained results confirm the higher effectiveness of our stochastic model. 

Keywords

[1] B. Abdoli, F. Hooshmand, and S. Mir Hassani, A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem, Appl. Energy 338 (2023), 1–23.
[2] E. Adu, Y. Jhang, and D. Liu, Current situation of carbon dioxide capture, storage, and enhanced oil recovery in the oil and gas industry, Canad. J. Chem. Engin. 97 (2019), no. 5, 1048–1076.
[3] W. Ampomah, R. Balch, M. Cather, R. Will, D. Gunda, Z. Dai, and M. Soltanian, Optimum design of CO2 storage and oil recovery under geological uncertainty, Appl. Energy 195 (2017), 80–92.
[4] A. Elkamel, H. Hashim, P.L. Douglas, and E. Croiset, Optimization of energy usage for fleet-wide power generating system under carbon mitigation options, AIChE J. 55 (2009), no. 12, 3168–3190.
[5] A. Ettehad, Storage compliance in coupled CO2-EOR and storage, Greenhouse Gases: Sci. Technol. 4 (2014), no. 1, 66–80.
[6] V. Goel and I.E. Grossmann, A class of stochastic programs with decision dependent uncertainty, Math. Program. Ser. B 108 (2006), no. 2-3, 355-–394.
[7] J. Guo, C. Huang, J. Wang, and X. Meng, Integrated operation for the planning of CO2 capture path in CCS–EOR project, J. Petrol. Sci. Engin. 186 (2020), 106720.
[8] Y.J. He, Y. Zhang, Z.F. Ma, N.V. Sahinidis, R.R. Tan, and D.C.Y. Foo, Optimal source-sink matching in carbon capture and storage systems under uncertainty, Ind. Engin. Chem. Res. 53 (2014), no. 2, 778–785.
[9] H. Herzog, Dioxide Capture and Storage, USA: Helm Hepburn, Oxford University Press, 2009.
[10] F. Hooshmand and S.A. MirHassani, Efficient constraint reduction in multistage stochastic programming problems with endogenous uncertainty, Optim. Meth. Software 31 (2016a), no. 2, 359–376.
[11] H.R. Jahangiri and D. Zhang, Ensemble-based co-optimization of carbon dioxide sequestration and enhanced oil recovery, Int. J. Greenhouse Gas Control 8 (2012), 22–33.
[12] J. Jiang, Z. Rui, R. Hazlett, and J. Lu, An integrated technical-economic model for evaluating CO2 enhanced oil recovery development, Appl. Energy 247 (2019), 190–211.
[13] T. Jonsbraten, Optimization models for petroleum field exploitation, PhD thesis, Norwegian School of Economics and Business Administration, 1998.
[14] F. Kamali and Y. Cinar, Co-optimizing enhanced oil recovery and CO2 storage by simultaneous water and CO2 injection, Energy Explor. Exploit. 32 (2014), 281–300.
[15] S. Kashkooli, A. Gandomkar, M. Riazi, and M. Tavallali, Coupled optimization of carbon dioxide sequestration and CO2 enhanced oil recovery, J. Petrol. Sci. Engin. 208 (2022), 109257.
[16] R.S. Middleton, A new optimization approach to energy network modeling: anthropogenic CO2 capture coupled with enhanced oil recovery, Int. J. Energy Res. 37 (2013), no. 14, 1794–1810.
[17] R.S. Middleton and J.M. Bielicki, A scalable infrastructure model for carbon capture and storage: SimCCS, Energy Policy 37 (2009), no. 3, 1052–1060.
[18] R. S. Middleton, M.J. Kuby, and J.M. Bielicki, Generating candidate networks for optimization: The CO2 capture and storage optimization problem, Comput. Envir. Urban Syst. 36 (2012), no. 1, 18–29.
[19] M.A. Safarzadeh and S.M. Motahhari, Co-optimization of carbon dioxide storage and enhanced oil recovery in oil reservoirs using a multi-objective genetic algorithm (NSGA-II), Petrol. Sci. 11 (2014), 460—468.
[20] R.R. Tan, K.B. Aviso, S. Bandyopadhyay, and D.K.S. Ng, Continuous-time optimization model for source-sink matching in carbon capture and storage systems, Ind. Engin. Chem. Res. 51 (2012), no. 30, 10015–10020.
[21] R.R. Tan, K.B. Aviso, S. Bandyopadhyay, and D.K.S. Ng, Optimal source-sink matching in carbon capture and storage systems with time, injection rate, and capacity constraints, Envir. Progress Sustain. Energy 32 (2013), no. 2, 411–416.
[22] J.F.D. Tapia, J.Y. Lee, R.E.H. Ooi, D.C.Y. Foo, and R.R. Tan, Optimal CO2 allocation and scheduling in enhanced oil recovery (EOR) operations, Appl. Energy 184 (2016), 337–345.
[23] J. Tapia, J. Lee, R. Ooi, D. Foo, and R. Tan, Planning and scheduling of CO2 capture, utilization and storage (CCUS) operations as a strip packing problem, Process Safety Envir. Protect. 104 (2016a), 358–372.
[24] J.F.D. Tapia and R.R. Tan, Fuzzy optimization of multi-period carbon capture and storage systems with parametric uncertainties, Process Safety Envir. Protect. 92 (2014), no. 6, 545–554.
[25] X. Wang, K. Veld, P. Marcy, S. Huzurbazar, and V. Alvarado, Economic co-optimization of oil recovery and CO2 sequestration, Appl. Energy 222 (2018), 132–147.
[26] J. You, W. Ampomah, and Q. Sun, Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework, Appl. Energy 279 (2020b), 115695.
[27] J. You, W. Ampomah, Q. Sun, E. J. Kutsienyo, R.S. Balch, Z. Dai, M. Cather, and X. Zhang, Machine learning based co-optimization of carbon dioxide sequestration and oil recovery in CO2-EOR project, J. Cleaner Product. 260 (2020a), 120866.
[28] S. Zhang, L. Liu, L. Zhang, Y. Zhuang, and J. Du, An optimization model for carbon capture utilization and storage supply chain: A case study in Northeastern China, Appl. Energy 231 (2018), 194–206
International Journal of Nonlinear Analysis and Applications
Volume 15, Issue 8
August 2024
Pages 289-300
  • Receive Date: 06 May 2023
  • Revise Date: 10 July 2023
  • Accept Date: 22 July 2023