Research Papers: Petroleum Engineering

Effective Prediction and Management of a CO2 Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks

[+] Author and Article Information
Si Le Van

Department of Energy Resources Engineering,
Inha University,
100 Inharo, Nam-gu,
Incheon 22212, South Korea
e-mail: slevansi_1190@inha.edu

Bo Hyun Chon

Department of Energy Resources Engineering,
Inha University,
100 Inharo, Nam-gu,
Incheon 22212, South Korea
e-mail: bochon@inha.ac.kr

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received May 27, 2017; final manuscript received September 15, 2017; published online October 19, 2017. Assoc. Editor: Esmail M. A. Mokheimer.

J. Energy Resour. Technol 140(3), 032906 (Oct 19, 2017) (14 pages) Paper No: JERT-17-1248; doi: 10.1115/1.4038054 History: Received May 27, 2017; Revised September 15, 2017

The injection of CO2 has been in global use for enhanced oil recovery (EOR) as it can improve oil production in mature fields. It also has environmental benefits for reducing greenhouse carbon by permanently sequestrating CO2 (carbon capture and storage (CCS)) in reservoirs. As a part of numerical studies, this work proposed a novel application of an artificial neural network (ANN) to forecast the performance of a water-alternating-CO2 process and effectively manage the injected CO2 in a combined CCS–EOR project. Three targets including oil recovery, net CO2 storage, and cumulative gaseous CO2 production were quantitatively simulated by three separate ANN models for a series of injection frames of 5, 15, 25, and 35 cycles. The concurrent estimations of a sequence of outputs have shown a relevant application in scheduling the injection process based on the progressive profile of the targets. For a specific surface design, an increment of 5.8% oil recovery and 4% net CO2 storage was achieved from 25 cycles to 35 cycles, suggesting ending the injection at 25 cycles. Using the models, distinct optimizations were also computed for oil recovery and net CO2 sequestration in various reservoir conditions. The results expressed a maximum oil recovery from 22% to 30% oil in place (OIP) and around 21,000–29,000 tons of CO2 trapped underground after 35 cycles if the injection began at 60% water saturation. The new approach presented in this study of applying an ANN is obviously effective in forecasting and managing the entire CO2 injection process instead of a single output as presented in previous studies.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.


Perera, M. S. A. , Gamage, R. P. , Rathaweera, T. D. , Ranathunga, A. S. , Koay, A. , and Choi, X. , 2016, “ A Review of CO2-Enhaced Oil Recovery With a Simulated Sensitivity Analysis,” Energies, 9(7), p. 481. [CrossRef]
Sedaghat, M. H. , Ghazanfari, M. H. , Parvazdavani, M. , and Morshedi, S. , 2013, “ Experimental Investigation of Microscopic/Macroscopic Efficiency of Polymer Flooding in Fractured Heavy Oil Five-Spot Systems,” ASME J. Energy Resour. Technol., 135(3), p. 032901. [CrossRef]
Kamath, K. I. , and Yan, S. J. , 1981, “ Enhanced Oil Recovery by Flooding With Dilute Aqueous Chemical Solutions,” ASME J. Energy Resour. Technol., 103(4), pp. 285–290. [CrossRef]
Si, L. V. , and Chon, B. H. , 2016, “ Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application,” Energies, 9(12), p. 1081. [CrossRef]
Zaluski, W. , El-Kaseeh, G. , Lee, S. Y. , Piercey, M. , and Duguid, A. , 2016, “ Monitoring Technology Ranking Methodology for CO2-EOR Sites Using the Weyburn-Midale Field as a Case Study,” Int. J. Greenhouse Gas Control, 54(Part 2), pp. 466–478. [CrossRef]
Al-Ameri, W. A. , Abdulraheem, A. , and Mahmoud, M. , 2016, “ Long-Term Effects of CO2 Sequestration on Rock Mechanical Properties,” ASME J. Energy Resour. Technol., 138(1), p. 012201. [CrossRef]
Zhou, D. , and Yang, D. , 2017, “ Scaling Criteria for Waterflooding and Immiscible CO2 Flooding in Heavy Oil Reservoirs,” ASME J. Energy Resour. Technol., 139(2), p. 022909. [CrossRef]
Nobakht, M. , Moghadam, S. , and Gu, Y. , 2007, “ Effects of Viscous and Capillary Forces on CO2 Enhanced Oil Recovery Under Reservoir Conditions,” Energy Fuel, 21(6), pp. 3469–3476. [CrossRef]
Zanganeh, P. , Ayatollahi, S. , Alamdari, A. , Zolghadr, A. , Dashti, H. , and Kord, S. , 2012, “ Asphaltene Deposition During CO2 Injection and Pressure Depletion: A Visual Study,” Energy Fuel, 26(2), pp. 1412–1419. [CrossRef]
Ping, H. L. , Ping, S. P. , Wei, L. X. , Chao, G. Q. , Sheng, W. C. , and Fangfang, L. , 2015, “ Study on CO2 EOR and Its Geological Sequestration Potential in Oil Field around Yulin City,” J. Pet. Sci. Eng., 134, pp. 199–204. [CrossRef]
Ampomah, W. , Balch, R. S. , Grigg, R. B. , Will, R. , Dai, Z. , and White, M. D. , 2016, “ Farnsworth Field CO2-EOR Project: Performance Case History,” SPE Improved Oil Recovery Conference, Tulsa, OK, Apr. 11–13, SPE Paper No. 179528-MS.
Hoffman, B. T. , and Shoaib, S. , 2013, “ CO2 Flooding to Increase Recovery for Unconventional Liquids-Rich Reservoirs,” ASME J. Energy Resour. Technol., 136(2), p. 022801.
Ren, B. , Ren, S. , Zhang, L. , Chen, G. , and Zhang, H. , 2016, “ Monitoring on CO2 Migration in a Tight Oil Reservoir During CCS-EOR in Jilin Oilfield China,” Energy, 98, pp. 108–121. [CrossRef]
Raza, A. , Rezaee, R. , Gholami, R. , Bing, C. H. , Nagarajan, R. , and Hamid, M. A. , 2016, “ A Screening Criterion for Selection of Suitable CO2 Storage Sites,” J. Nat. Gas Sci. Eng., 28, pp. 317–327. [CrossRef]
Ahmadi, M. A. , Pouladi, B. , and Barghi, T. , 2016, “ Numerical Modeling of CO2 Injection Scenarios in Petroleum Reservoirs: Application to CO2 Sequestration and EOR,” J. Nat. Gas Sci. Eng., 30, pp. 38–49. [CrossRef]
He, L. , Shen, P. , Liao, X. , Li, F. , Gao, Q. , and Wang, Z. , 2016, “ Potential Evaluation of CO2 EOR and Sequestration in Yanchang Oilfield,” J. Energy Inst., 89(2), pp. 215–221. [CrossRef]
Teklu, T. W. , Alameri, W. , Graves, R. , and Kazemi, H. , 2016, “ Low-Salinity Water-Alternating-CO2 EOR,” J. Pet. Sci. Eng., 142, pp. 101–118. [CrossRef]
Yao, Y. , Wang, Z. , Li, G. , Wu, H. , and Wang, J. , 2016, “ Potential of Carbon Dioxide Miscible Injections Into the H-26 Reservoir,” J. Nat. Gas Sci. Eng., 34, pp. 1085–1095. [CrossRef]
Song, Z. , Li, Z. , Wei, Z. , Lai, F. , and Bai, B. , 2014, “ Sensitivity Analysis of Water-Alternating-CO2 Flooding for Enhanced Oil Recovery in High Water Cut Oil Reservoirs,” Comput. Fluids, 99, pp. 93–103. [CrossRef]
Wei, N. , Li, X. , Dahowski, R. T. , and Davidson, C. L. , 2015, “ Economic Evaluation on CO2-EOR of Onshore Oil Fields in China,” Int. J. Greenhouse Gas Control, 37, pp. 170–181. [CrossRef]
Ahmadi, M. A. , Ebadi, M. , and Hosseine, S. M. , 2014, “ Prediction Breakthrough Time of Water Coning in the Fractured Reservoirs by Implementing Low Parameter Support Vector Machine Approach,” Fuel, 117(Part A), pp. 579–589. [CrossRef]
Ahmadi, M. A. , and Ebadi, M. , 2014, “ Evolving Smart Approach for Determination Dew Point Pressure Through Condensate Gas Reservoirs,” Fuel, 117(Part B), pp. 1074–1084. [CrossRef]
Ahmadi, M. A. , Soleimani, R. , Lee, M. , Kashiwao, T. , and Bahadori, A. , 2015, “ Determination of Oil Well Production Performance Using Artificial Neural Network (ANN) Linked to the Particle Swarm Optimization (PSO) Tool,” Petroleum, 1(2), pp. 118–132. [CrossRef]
Ettehadtavakkol, A. , Lake, L. W. , and Bryant, S. L. , 2014, “ CO2-EOR and Storage Design Optimization,” Int. J. Greenhouse Gas Control, 25, pp. 79–92. [CrossRef]
Ahmadi, M. A. , 2012, “ Neural Network Based Unified Particle Swarm Optimization for Prediction of Asphaltene Precipitation,” Fluid Phase Equilib., 314, pp. 46–51. [CrossRef]
Ahmadi, M. A. , Zahedzadeh, M. , Shadizadeh, S. R. , and Abbassi, R. , 2015, “ Connectionist Model for Predicting Minimum Gas Miscibility Pressure: Application to Gas Injection Process,” Fuel, 148, pp. 202–211. [CrossRef]
Pan, F. , McPherson, B. J. , Dai, Z. , Lee, S. Y. , Ampomah, W. , Viswanathan, H. , and Esser, R. , 2016, “ Uncertainty Analysis of Carbon Sequestration in an Active CO2–EOR Field,” Int. J. Greenhouse Gas Control, 51, pp. 18–28. [CrossRef]
Dai, Z. , Viswanathan, H. , Middleton, R. , Pan, F. , Ampomah, W. , Yang, C. , Jia, W. , Lee, S. Y. , Balch, R. , Grigg, R. , and White, M. , 2016, “ CO2 Accounting and Risk Analysis for CO2 Sequestration at Enhanced Oil Recovery Sites,” Environ. Sci. Technol., 50(14), pp. 7546–7554. [CrossRef] [PubMed]
Eshraghi, S. E. , Rasaei, M. R. , and Zendehboudi, S. Z. , 2016, “ Optimization of Miscible CO2 EOR and Storage Using Heuristic Methods Combined With Capacitance/Resistance and Gentil Fractional Flow Models,” J. Nat. Gas Sci. Eng., 32, pp. 304–318. [CrossRef]
Ahmadi, M. A. , 2015, “ Connectionist Approach Estimates Gas–Oil Relative Permeability in Petroleum Reservoirs: Application to Reservoir Simulation,” Fuel, 140, pp. 429–439. [CrossRef]
Ampomah, W. , Balch, R. , Cather, M. , Coss, D. R. , Dai, Z. , Heath, J. , Dewers, T. , and Mozley, P. , 2016, “ Evaluation of CO2 Storage Mechanisms in CO2 Enhanced Oil Recovery Sites: Application to Morrow Sandstone Reservoir,” Energy Fuel, 30(10), pp. 8545–8555. [CrossRef]
Li, L. , Khorsandi, S. , Johns, R. T. , and Dilmore, R. M. , 2015, “ CO2 Enhanced Oil Recovery and Storage Using a Gravity–Enhanced Process,” Int. J. Greenhouse Gas Control, 42, pp. 502–515. [CrossRef]
Moortgat, J. , Firoozabadi, A. , Li, Z. , and Esposito, R. , 2010, “ A Detailed Experimental and Numerical Study of Gravitational Effects on CO2 Enhanced Recovery,” SPE Annual Technical Conference and Exhibition, Florence, Italy, Sept. 19–22, SPE Paper No. SPE-135563-MS. https://www.onepetro.org/conference-paper/SPE-135563-MS
Computer Modelling Group Ltd., 2016, “ WINPROP User Guide: Phase–Behaviour & Fluid Property Program,” Computer Modelling Group Ltd., Calgary, AB, Canada.
Orr, F. M. , Dindoruk, B. , and Johns, R. T. , 1995, “ Theory of Multicomponent Gas/Oil Displacements,” Ind. Eng. Chem. Res., 34(8), pp. 2661–2669. [CrossRef]
Orr, F. M. , and Silva, M. K. , 1987, “ Effect of Oil Composition on Minimum Miscibility Pressure—Part 2: Correlation,” SPE J., 2(4), pp. 479–491.
Ahmadi, K. , and Johns, R. T. , 2011, “ Multiple–Mixing–Cell Method for MMP Calculations,” SPE J., 16(4), pp. 733–742. [CrossRef]
Haghtalab, A. , and Moghaddam, A. K. , 2016, “ Prediction of Minimum Miscibility Pressure Using the UNIFAC Group Contribution Activity Coefficient Model and the LCVM Mixing Rule,” Ind. Eng. Chem. Res., 55(10), pp. 2840–2851. [CrossRef]
Damico, J. R. , Monson, C. C. , Frailey, S. , Lasemi, Y. , Webb, N. D. , Grigsby, N. , Yang, F. , and Berger, P. , 2014, “ Strategies for Advancing CO2 EOR in the Illinois Basin, USA,” Energy Procedia, 63, pp. 7694–7708. [CrossRef]
Dai, Z. , Middleton, R. , Viswanathan, H. , Rahn, J. F. , Bauman, J. , Pawar, R. , Lee, S. Y. , and McPherson, B. , 2014, “ An Integrated Framework for Optimizing CO2 Sequestration and Enhanced Oil Recovery,” Environ. Sci. Technol. Lett., 1(1), pp. 49–54. [CrossRef]
Holt, T. , Lindeberg, E. , and Berg, D. W. , 2009, “ EOR and CO2 Disposal—Economic and Capacity Potential in the North Sea,” Energy Procedia, 1(1), pp. 4159–4166. [CrossRef]
Zhao, X. , and Liao, X. , 2012, “ Evaluation Method of CO2 Sequestration and Enhanced Oil Recovery in an Oil Reservoir, as Applied to the Changqing Oil Fields, China,” Energy Fuel, 26(8), pp. 5350–5354. [CrossRef]
Attavitkamthorn, V. , Vilcaez, J. , and Sato, K. , 2013, “ Integrated CCS Aspect Into CO2 EOR Project Under Wide Range of Reservoir Properties and Operating Conditions,” Energy Procedia, 37, pp. 6901–6908. [CrossRef]
Gong, Y. , and Gu, Y. , 2015, “ Miscible CO2 Simultaneous Water–and–Gas (CO2–SWAG) Injection in the Bakken Formation,” Energy Fuel, 29(9), pp. 5655–5665. [CrossRef]
Mishra, S. , Hawkins, J. , Barclay, T. H. , and Harley, M. , 2014, “ Estimating CO2–EOR Potential and Co-Sequestration Capacity in Ohio's Depleted Oil Fields,” Energy Procedia, 63, pp. 7785–7795. [CrossRef]


Grahic Jump Location
Fig. 2

Laboratory data and regression results simulated in WINPROP for a specific crude oil: (a) oil specific gravity—differential liberation test (DLT), (b)oil viscosity—DLT, (c) gas-to-oil ratio—DLT, (d) saturation pressure—ST, and (e) swelling factor—ST

Grahic Jump Location
Fig. 1

Flow capacity of phases in the considered rock system used in the simulation: (a) water–oil relative permeabilities, (b) gas–liquid relative permeabilities, and (c) three-phase relative permeability

Grahic Jump Location
Fig. 3

Schematic represents the structure of network models, whereas Y stands for oil recovery, CO2 production or net CO2 storage

Grahic Jump Location
Fig. 4

Performances of a specific simulation case: (a) oil recovery factor and oil rate, (b) CO2 production and net CO2 sequestration, and (c) mole fraction profile of CO2 at 5.5 cycles

Grahic Jump Location
Fig. 5

Distribution in values obtained from the numerical samples: (a) oil recovery factor, (b) CO2 production, and (c) net CO2 sequestration

Grahic Jump Location
Fig. 6

Performances obtained from training the essential objectives: (a) oil recovery factor, (b) CO2 production, and (c) net CO2 storage

Grahic Jump Location
Fig. 7

Plots of comparison between the numerical samples and recomputed targets using the generated networks: (a) oil recovery factor, (b) cumulative CO2 production, and (c) and net CO2 storage

Grahic Jump Location
Fig. 9

Relationships between the critical targets on the surface operation parameters at 15 and 35 cycles: (a) oil recovery, (b) net CO2 storage, and (c) cumulative CO2 production

Grahic Jump Location
Fig. 10

Effects of well spacing and fluid injection ratio on the targets at 35 cycles: (a) oil recovery factor, (b) cumulative CO2 production, and (c) net CO2 sequestration

Grahic Jump Location
Fig. 11

Progression of RF, net CO2 storage and CO2 production (recycled) at two cases of Sw, where Kv/Kh = 0.5, T = 60, WAG ratio = 1 and well distance = 300 m

Grahic Jump Location
Fig. 8

Dependences of oil recovery factor and carbon sequestration on reservoir factors achieved using the ANN models: (a) Sw and Kv/Kh versus RF and (b) Sw and Kv/Kh versus net CO2 storage



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In