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dc.contributor.authorSyah, Rahmadspa
dc.contributor.authorMehdi Alizadeh, Seyedspa
dc.contributor.authorNurgalieva, Karinaspa
dc.contributor.authorGrimaldo Guerrero, John Williamspa
dc.contributor.authorNasution, Mahyuddin K. M.spa
dc.contributor.authorDavarpanah, Afshinspa
dc.contributor.authorRamdan, Dadanspa
dc.contributor.authorMetwally, Ahmed S. M.spa
dc.date.accessioned2022-01-19T20:34:29Z
dc.date.available2022-01-19T20:34:29Z
dc.date.issued2021-09-20
dc.identifier.issn2071-1050spa
dc.identifier.urihttps://hdl.handle.net/11323/8984spa
dc.description.abstractSupercritical carbon dioxide injection in tight reservoirs is an efficient and prominent enhanced gas recovery method, as it can be more mobilized in low-permeable reservoirs due to its molecular size. This paper aimed to perform a set of laboratory experiments to evaluate the impacts of permeability and water saturation on enhanced gas recovery, carbon dioxide storage capacity, and carbon dioxide content during supercritical carbon dioxide injection. It is observed that supercritical carbon dioxide provides a higher gas recovery increase after the gas depletion drive mechanism is carried out in low permeable core samples. This corresponds to the feasible mobilization of the supercritical carbon dioxide phase through smaller pores. The maximum gas recovery increase for core samples with 0.1 mD is about 22.5%, while gas recovery increase has lower values with the increase in permeability. It is about 19.8%, 15.3%, 12.1%, and 10.9% for core samples with 0.22, 0.36, 0.54, and 0.78 mD permeability, respectively. Moreover, higher water saturations would be a crucial factor in the gas recovery enhancement, especially in the final pore volume injection, as it can increase the supercritical carbon dioxide dissolving in water, leading to more displacement efficiency. The minimum carbon dioxide storage for 0.1 mD core samples is about 50%, while it is about 38% for tight core samples with the permeability of 0.78 mD. By decreasing water saturation from 0.65 to 0.15, less volume of supercritical carbon dioxide is involved in water, and therefore, carbon dioxide storage capacity increases. This is indicative of a proper gas displacement front in lower water saturation and higher gas recovery factor. The findings of this study can help for a better understanding of the gas production mechanism and crucial parameters that affect gas recovery from tight reservoirs.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.sourceSustainabilityspa
dc.subjectDisplacement efficiencyspa
dc.subjectNatural gas recoveryspa
dc.subjectPermeabilityspa
dc.subjectWater saturationspa
dc.subjectAdsorption densityspa
dc.titleA laboratory approach to measure enhanced gas recovery from a tight gas reservoir during supercritical carbon dioxide injectionspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.mdpi.com/2071-1050/13/21/11606spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.3390/su132111606spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references1. Jassim, T.L. The Influence of Oil Prices, Licensing and Production on the Economic Development: An Empirical Investi-gation of Iraq Economy. AgBioForum 2021, 23, 1–11.spa
dc.relation.references2. Esan, B.; Hassan, A. Nexus between Carbon Dioxide Emission, Energy Consumption and Economic Growth in Nigeria. Int. J. Sustain. Energy Environ. Res. 2020, 9, 46–55. [CrossRef]spa
dc.relation.references3. Adle, A.A.; Akdemir, Ö. Achieving competitive advantage in technology based industry: How developing intellectual capital matters. Int. J. Ebus. Egovern. Stud. 2019, 11, 89–103. [CrossRef]spa
dc.relation.references4. Li, A.; Mu, X.; Zhao, X.; Xu, J.; Khayatnezhad, M.; Lalehzari, R. Developing the non-dimensional framework for water distribution formulation to evaluate sprinkler irrigation. Irrig. Drain. 2021, 70, 659–667. [CrossRef]spa
dc.relation.references5. Zhou, Z.; Qin, J.; Xiang, X.; Tan, Y.; Liu, Q.; Xiong, N.N. News Text Topic Clustering Optimized Method Based on TF-IDF Algorithm on Spark. Comput. Mater. Contin. 2020, 62, 217–231. [CrossRef]spa
dc.relation.references6. Tian, L.; Li, J.; Zhang, L.; Sun, Y.; Yang, S. TCPW BR: A Wireless Congestion Control Scheme Base on RTT. Comput. Mater. Contin. 2020, 62, 233–244. [CrossRef]spa
dc.relation.references7. Syah, R.; Alizadeh, S.M.; Nasution, M.K.; Kashkouli, M.N.I.; Elveny, M.; Khan, A. Carbon dioxide-based enhanced oil recovery methods to evaluate tight oil reservoirs productivity: A laboratory perspective coupled with geo-sequestration feature. Energy Rep. 2021, 7, 4697–4704. [CrossRef]spa
dc.relation.references8. Xu, Z.; Liang, W.; Li, K.C.; Xu, J.; Jin, H. A blockchain-based roadside unit-assisted authentication and key agreement protocol for internet of vehicles. J. Parallel Distrib. Comput. 2021, 149, 29–39. [CrossRef]spa
dc.relation.references9. Wang, W.; Yang, Y.; Li, J.; Hu, Y.; Luo, Y.; Wang, X. Woodland labeling in chenzhou, China, via deep learning approach. Int. J. Comput. Intell. Syst. 2020, 13, 1393–1403. [CrossRef]spa
dc.relation.references10. Duan, Y.; Liu, Y.; Chen, Z.; Liu, D.; Yu, E.; Zhang, X.; Fu, H.; Fu, J.; Zhang, J.; Du, H. Amorphous molybdenum sulfide nanocatalysts simultaneously realizing efficient upgrading of residue and synergistic synthesis of 2D MoS2 nanosheets/carbon hierarchical structures. Green Chem. 2020, 22, 44–53. [CrossRef]spa
dc.relation.references11. He, L.; Chen, Y.; Zhao, H.; Tian, P.; Xue, Y.; Chen, L. Game-based analysis of energy-water nexus for identifying environmental impacts during Shale gas operations under stochastic input. Sci. Total Environ. 2018, 627, 1585–1601. [CrossRef]spa
dc.relation.references12. Da’Ie, A.B. Developing mathematical models for global solar radiation intensity estimation at Shakardara, Kabul. Int. J. Innov. Res. Sci. Stud. 2021, 4, 133–138. [CrossRef]spa
dc.relation.references13. Li, W.; Xu, H.; Li, H.; Yang, Y.; Sharma, P.K.; Wang, J.; Singh, S. Complexity and algorithms for superposed data uploading problem in networks with smart devices. IEEE Internet Things J. 2019, 7, 5882–5891. [CrossRef]spa
dc.relation.references14. Gu, K.; Jia, W.; Zhang, J. Identity-based multi-proxy signature scheme in the standard model. Fundam. Informaticae 2017, 150, 179–210. [CrossRef]spa
dc.relation.references15. Gu, K.; Wang, Y.; Wen, S. Traceable Threshold Proxy Signature. J. Inf. Sci. Eng. 2017, 33, 63–79.spa
dc.relation.references16. Davarpanah, A.; Mirshekari, B. Experimental Investigation and Mathematical Modeling of Gas Diffusivity by Carbon Dioxide and Methane Kinetic Adsorption. Ind. Eng. Chem. Res. 2019, 58, 12392–12400. [CrossRef]spa
dc.relation.references17. Mosbah, A.; Ali, M.A.M.; Aljubari, I.H.; Talib, Z.M.; Sherief, S.R. Migrants in the High-Tech and Engineering Sectors: An Emerging Research Area. In Proceedings of the 2018 IEEE Conference on Systems, Process and Control (ICSPC), Melaka, Malaysia, 14–15 December 2018; pp. 234–237.spa
dc.relation.references18. Ahmadi, R.; Sarvestani, M.R.J.; Taghavizad; Rahim, N. Evaluating Adsorption of Proline Amino Acid on the Surface of Fullerene (C60) and Carbon Nanocone by Density Functional Theory. Chem. Methodol. 2020, 4, 68–79. [CrossRef]spa
dc.relation.references19. Benson, S.; Cook, P.; Anderson, J.; Bachu, S.; Nimir, H.B.; Basu, B.; Bradshaw, J.; Deguchi, G. Chapter 5: Underground geolog-ical storage. In IPCC Special Report on Carbon Dioxide Capture and Storage; IPCC: Geneva, Switzerland, 2005.spa
dc.relation.references20. Brodny, J. Storage of carbon dioxide in liquidated mining headings of abandoned coal mines. In Proceedings of the 17th International Multidisciplinary Scientific GeoConference SGEM2017, Science and Technologies in Geology, Exploration and Mining; Stef92 Technology, Albena, Bulgaria, 29 June–5 July 2017.spa
dc.relation.references21. Zhang, J.; Wu, C.; Yang, D.; Chen, Y.; Meng, X.; Xu, L.; Guo, M. HSCS: A hybrid shared cache scheduling scheme for multiprogrammed workloads. Front. Comput. Sci. 2018, 12, 1090–1104. [CrossRef]spa
dc.relation.references22. Zhang, J.; Sun, J.; Wang, J.; Yue, X.-G. Visual object tracking based on residual network and cascaded correlation filters. J. Ambient. Intell. Humaniz. Comput. 2021, 12, 8427–8440. [CrossRef]spa
dc.relation.references23. Ehyaei, M.A.; Ahmadi, A.; Rosen, M.A.; Davarpanah, A. Thermodynamic Optimization of a Geothermal Power Plant with a Genetic Algorithm in Two Stages. Processes 2020, 8, 1277. [CrossRef]spa
dc.relation.references24. He, L.; Chen, Y.; Li, J. A three-level framework for balancing the tradeoffs among the energy, water, and air-emission implications within the life-cycle shale gas supply chains. Resour. Conserv. Recycl. 2018, 133, 206–228. [CrossRef]spa
dc.relation.references25. Cheng, X.; He, L.; Lu, H.; Chen, Y.; Ren, L. Optimal water resources management and system benefit for the Marcellus shale-gas reservoir in Pennsylvania and West Virginia. J. Hydrol. 2016, 540, 412–422. [CrossRef]spa
dc.relation.references26. Lee, B.; Lee, Y. Distinction Between Real Faces and Photos by Analysis of Face Data. Intell. Autom. Soft Comput. 2020, 26, 133–139. [CrossRef]spa
dc.relation.references27. Ezekiel, J.; Ebigbo, A.; Adams, B.M.; Saar, M.O. Combining natural gas recovery and CO2-based geothermal energy extraction for electric power generation. Appl. Energy 2020, 269, 115012. [CrossRef]spa
dc.relation.references28. Sun, H.; Yao, J.; Gao, S.-H.; Fan, D.-Y.; Wang, C.-C.; Sun, Z.-X. Numerical study of CO2 enhanced natural gas recovery and sequestration in shale gas reservoirs. Int. J. Greenh. Gas Control. 2013, 19, 406–419. [CrossRef]spa
dc.relation.references29. Mokhatab, S.; Poe, W.A.; Mak, J.Y. Natural Gas Liquids Recovery. In Handbook of Natural Gas Transmission and Processing; Gulf Professional Publishing: Houston, TX, USA, 2019; pp. 361–393. [CrossRef]spa
dc.relation.references30. Wang, J.; Ryan, D.; Szabries, M.; Jaeger, P. A Study for Using CO2 To Enhance Natural Gas Recovery from Tight Reservoirs. Energy Fuels 2019, 33, 3821–3827. [CrossRef]spa
dc.relation.references31. Zhou, H.; Davarpanah, A. Hybrid Chemical Enhanced Oil Recovery Techniques. Symmetry 2020, 12, 1086. [CrossRef]spa
dc.relation.references32. Gu, K.; Jia, W.; Jiang, C. Efficient identity-based proxy signature in the standard model. Comput. J. 2015, 58, 792–807. [CrossRef]spa
dc.relation.references33. Chen, Y.; He, L.; Li, J.; Zhang, S. Multi-criteria design of shale-gas-water supply chains and production systems towards optimal life cycle economics and greenhouse gas emissions under uncertainty. Comput. Chem. Eng. 2018, 109, 216–235. [CrossRef]spa
dc.relation.references34. Kim, M.; Kim, J.; Shin, M. Word Embedding Based Knowledge Representation with Extracting Relationship Between Scientific Terminologies. Intell. Autom. Soft Comput. 2019, 26, 141–147. [CrossRef]spa
dc.relation.references35. Guo, X.; Liu, J.; Dai, L.; Liu, Q.; Fang, D.; Wei, A.; Wang, J. Friction-wear failure mechanism of tubing strings used in high-pressure, high-temperature and high-yield gas wells. Wear 2021, 468, 203576. [CrossRef]spa
dc.relation.references36. Li, Y.; Macdonald, D.D.; Yang, J.; Qiu, J.; Wang, S. Point defect model for the corrosion of steels in supercritical water: Part I, film growth kinetics. Corros. Sci. 2020, 163, 108280. [CrossRef]spa
dc.relation.references37. Deng, J.; Chen, J.; Wang, D. Mechanism Design and Mechanical Analysis of Multi-Suction Sliding Cleaning Robot Used in Glass Curtain Wall. Comput. Syst. Sci. Eng. 2019, 34, 201–206. [CrossRef]spa
dc.relation.references38. Zhang, D.; Chen, X.; Li, F.; Sangaiah, A.K.; Ding, X. Seam-Carved Image Tampering Detection Based on the Cooccurrence of Adjacent LBPs. Secur. Commun. Netw. 2020, 2020, 8830310. [CrossRef]spa
dc.relation.references39. Duan, M.; Li, K.; Ouyang, A.; Win, K.N.; Li, K.; Tian, Q. EGroupNet. ACM Trans. Multimed. Comput. Commun. Appl. 2020, 16, 1–23. [CrossRef]spa
dc.relation.references40. Zhang, J.; Yang, K.; Xiang, L.; Luo, Y.; Xiong, B.; Tang, Q. A Self-Adaptive Regression-Based Multivariate Data Compression Scheme with Error Bound in Wireless Sensor Networks. Int. J. Distrib. Sens. Netw. 2013, 9, 913497. [CrossRef]spa
dc.relation.references41. Zhang, X.; Sun, X.; Lv, T.; Weng, L.; Chi, M.; Shi, J.; Zhang, S. Preparation of PI porous fiber membrane for recovering oil-paper insulation structure. J. Mater. Sci. Mater. Electron. 2020, 31, 13344–13351. [CrossRef]spa
dc.relation.references42. Fan, C.; Li, H.; Qin, Q.; He, S.; Zhong, C. Geological conditions and exploration potential of shale gas reservoir in Wufeng and Longmaxi Formation of southeastern Sichuan Basin, China. J. Pet. Sci. Eng. 2020, 191, 107138. [CrossRef]spa
dc.relation.references43. Xue, C.; You, J.; Zhang, H.; Xiong, S.; Yin, T.; Huang, Q. Capacity of myofibrillar protein to adsorb characteristic fishy-odor compounds: Effects of concentration, temperature, ionic strength, pH and yeast glucan addition. Food Chem. 2021, 363, 130304. [CrossRef]spa
dc.relation.references44. Yang, W.; Li, K.; Li, K. A Pipeline Computing Method of SpTV for Three-Order Tensors on CPU and GPU. ACM Trans. Knowl. Discov. Data 2019, 13, 1–27. [CrossRef]spa
dc.relation.references45. Davarpanah, A.; Shirmohammadi, R.; Mirshekari, B.; Aslani, A. Analysis of hydraulic fracturing techniques: Hybrid fuzzy approaches. Arab. J. Geosci. 2019, 12, 402. [CrossRef]spa
dc.relation.references46. Zou, C.; Yang, Z.; He, D.; Wei, Y.; Li, J.; Jia, A.; Chen, J.; Zhao, Q.; Li, Y.; Li, J.; et al. Theory, technology and prospects of conventional and unconventional natural gas. Pet. Explor. Dev. 2018, 45, 604–618. [CrossRef]spa
dc.relation.references47. Wang, L.; Tian, Y.; Yu, X.; Wang, C.; Yao, B.; Wang, S.; Winterfeld, P.H.; Wang, X.; Yang, Z.; Wang, Y.; et al. Advances in improved/enhanced oil recovery technologies for tight and shale reservoirs. Fuel 2017, 210, 425–445. [CrossRef]spa
dc.relation.references48. Li, L.; Tan, J.; Wood, D.; Zhao, Z.; Becker, D.; Lyu, Q.; Shu, B.; Chen, H. A review of the current status of induced seismicity monitoring for hydraulic fracturing in unconventional tight oil and gas reservoirs. Fuel 2019, 242, 195–210. [CrossRef]spa
dc.relation.references49. Zhou, X.; Li, K.; Yang, Z.; Xiao, G.; Li, K. Progressive Approaches for Pareto Optimal Groups Computation. IEEE Trans. Knowl. Data Eng. 2019, 31, 521–534. [CrossRef]spa
dc.relation.references50. Davarpanah, A.; Mirshekari, B.; Behbahani, T.J.; Hemmati, M. Integrated production logging tools approach for convenient experimental individual layer permeability measurements in a multi-layered fractured reservoir. J. Pet. Explor. Prod. Technol. 2018, 8, 743–751. [CrossRef]spa
dc.relation.references51. Gao, H.; Li, H.A. Pore structure characterization, permeability evaluation and enhanced gas recovery techniques of tight gas sandstones. J. Nat. Gas Sci. Eng. 2016, 28, 536–547. [CrossRef]spa
dc.relation.references52. Zar˛ebska, K.; Ceglarska-Stefa ´nska, G. The change in effective stress associated with swelling during carbon dioxide sequestration on natural gas recovery. Int. J. Coal Geol. 2008, 74, 167–174. [CrossRef]spa
dc.relation.references53. Liu, K.; Yu, Z.; Saeedi, A.; Esteban, L. Effects of Permeability, Heterogeneity and Gravity on Supercritical CO2 Displacing Gas Under Reservoir Conditions. In Proceedings of the SPE Asia Pacific Enhanced Oil Recovery Conference, Kuala Lumpur, Malaysia, 11–13 August 2015.spa
dc.relation.references54. Mei, J.; Li, K.; Tong, Z.; Li, Q.; Li, K. Profit Maximization for Cloud Brokers in Cloud Computing. IEEE Trans. Parallel Distrib. Syst. 2019, 30, 190–203. [CrossRef]spa
dc.relation.references55. Tang, Q.; Yang, K.; Li, P.; Zhang, J.; Luo, Y.; Xiong, B. An energy efficient MCDS construction algorithm for wireless sensor networks. EURASIP J. Wirel. Commun. Netw. 2012, 2012, 1–15. [CrossRef]spa
dc.relation.references56. Wei, B.; Zhang, X.; Wu, R.; Zou, P.; Gao, K.; Xu, X.; Pu, W.; Wood, C. Pore-scale monitoring of CO2 and N2 flooding processes in a tight formation under reservoir conditions using nuclear magnetic resonance (NMR): A case study. Fuel 2019, 246, 34–41. [CrossRef]spa
dc.relation.references57. Tian, X.; Cheng, L.; Cao, R.; Zhang, M.; Guo, Q.; Wang, Y.; Zhang, J.; Cui, Y. Potential evaluation of CO2 storage and enhanced oil recovery of tight oil reservoir in the Ordos Basin, China. J. Environ. Biol. 2015, 36, 789–797. [PubMed]spa
dc.relation.references58. Orozco, D.; Fragoso, A.; Selvan, K.; Noble, G.; Aguilera, R. Eagle Ford Huff ‘n’ Puff Gas-Injection Pilot: Comparison of Reservoir-Simulation, Material Balance, and Real Performance of the Pilot Well. SPE Reserv. Eval. Eng. 2020, 23, 247–260. [CrossRef]spa
dc.relation.references59. Esfandyari, H.; Moghani, A.; Esmaeilzadeh, F.; Davarpanah, A. A Laboratory Approach to Measure Carbonate Rocks’ Adsorption Density by Surfactant and Polymer. Math. Probl. Eng. 2021, 2021, 5539245. [CrossRef]spa
dc.relation.references60. Deng, Z.; Liu, C.; Zhu, Z. Inter-hours rolling scheduling of behind-the-meter storage operating systems using electricity price forecasting based on deep convolutional neural network. Int. J. Electr. Power Energy Syst. 2021, 125, 106499. [CrossRef]spa
dc.relation.references61. Deng, Z.; Wang, B.; Xu, Y.; Xu, T.; Liu, C.; Zhu, Z. Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting. IEEE Access 2019, 7, 88058–88071. [CrossRef]spa
dc.relation.references62. Hamza, A.; Hussein, I.A.; Al-Marri, M.J.; Mahmoud, M.; Shawabkeh, R. Impact of clays on CO2 adsorption and enhanced gas recovery in sandstone reservoirs. Int. J. Greenh. Gas Control 2021, 106, 103286. [CrossRef]spa
dc.relation.references63. Teklu, T.W.; Li, X.; Zhou, Z.; Alharthy, N.; Wang, L.; Abass, H. Low-salinity water and surfactants for hydraulic fracturing and EOR of shales. J. Pet. Sci. Eng. 2018, 162, 367–377. [CrossRef]spa
dc.relation.references64. Unconventional Gas Production from Hydraulically Fractured Well-An Application of Direct Search Based Optimization Algorithm. Int. J. Recent Technol. Eng. 2019, 8, 2726–2737. [CrossRef]spa
dc.relation.references65. Roshani, M.; Phan, G.T.; Ali, P.J.M.; Roshani, G.H.; Hanus, R.; Duong, T.; Corniani, E.; Nazemi, E.; Kalmoun, E.M. Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline’s scale layer thickness. Alex. Eng. J. 2021, 60, 1955–1966. [CrossRef]spa
dc.relation.references66. Roshani, M.; Phan, G.; Roshani, G.H.; Hanus, R.; Nazemi, B.; Corniani, E.; Nazemi, E. Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows. Measurement 2021, 168, 108427. [CrossRef]spa
dc.relation.references67. Chen, Y.; Li, K.; Yang, W.; Xiao, G.; Xie, X.; Li, T. Performance-Aware Model for Sparse Matrix-Matrix Multiplication on the Sunway TaihuLight Supercomputer. IEEE Trans. Parallel Distrib. Syst. 2019, 30, 923–938. [CrossRef]spa
dc.relation.references68. Jia, B.; Tsau, J.-S.; Barati, R. Role of molecular diffusion in heterogeneous, naturally fractured shale reservoirs during CO2 huff-n-puff. J. Pet. Sci. Eng. 2018, 164, 31–42. [CrossRef]spa
dc.relation.references69. Wang, L.; Yu, W. Gas Huff and Puff Process in Eagle Ford Shale: Recovery Mechanism Study and Optimization. In Proceedings of the SPE Oklahoma City Oil and Gas Symposium, Oklahoma City, OK, USA, 8–12 April 2019.spa
dc.relation.references70. Roshani, M.; Phan, G.; Faraj, R.H.; Phan, N.-H.; Roshani, G.H.; Nazemi, B.; Corniani, E.; Nazemi, E. Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products. Nucl. Eng. Technol. 2021, 53, 1277–1283. [CrossRef]spa
dc.relation.references71. Roshani, M.; Sattari, M.A.; Ali, P.J.M.; Roshani, G.H.; Nazemi, B.; Corniani, E.; Nazemi, E. Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter. Flow Meas. Instrum. 2020, 75, 101804. [CrossRef]spa
dc.relation.references72. Karami, A.; Roshani, G.H.; Khazaei, A.; Nazemi, E.; Fallahi, M. Investigation of different sources in order to optimize the nuclear metering system of gas–oil–water annular flows. Neural Comput. Appl. 2018, 32, 3619–3631. [CrossRef]spa
dc.relation.references73. Chen, J.; Li, K.; Bilal, K.; Zhou, X.; Li, K.; Yu, P.S. A Bi-layered Parallel Training Architecture for Large-Scale Convolutional Neural Networks. IEEE Trans. Parallel Distrib. Syst. 2019, 30, 965–976. [CrossRef]spa
dc.relation.references74. Thanh, H.V.; Sugai, Y.; Nguele, R.; Sasaki, K. Robust optimization of CO2 sequestration through a water alternating gas process under geological uncertainties in Cuu Long Basin, Vietnam. J. Nat. Gas Sci. Eng. 2020, 76, 103208. [CrossRef]spa
dc.relation.references75. AlRassas, A.M.; Ren, S.; Sun, R.; Thanh, H.V.; Guan, Z. CO2 storage capacity estimation under geological uncertainty using 3-D geological modeling of unconventional reservoir rocks in Shahejie Formation, block Nv32, China. J. Pet. Explor. Prod. Technol. 2021, 11, 2327–2345. [CrossRef]spa
dc.relation.references76. Thanh, H.V.; Sugai, Y.; Sasaki, K. Impact of a new geological modelling method on the enhancement of the CO2 storage assessment of E sequence of Nam Vang field, offshore Vietnam. Energy Sources Part A Recover. Util. Environ. Eff. 2019, 42, 1499–1512. [CrossRef]spa
dc.relation.references77. Santiago, C.; Kantzas, A. Investigating the effects of gas type and operation mode in enhanced gas recovery in unconventional reservoirs. J. Nat. Gas Sci. Eng. 2018, 50, 282–292. [CrossRef]spa
dc.relation.references78. Dai, Z.; Viswanathan, H.; Middleton, R.; Pan, F.; Ampomah, W.; Yang, C.; Jia, W.; Xiao, T.; Lee, S.-Y.; McPherson, B.; et al. CO2 Accounting and Risk Analysis for CO2 Sequestration at Enhanced Oil Recovery Sites. Environ. Sci. Technol. 2016, 50, 7546–7554. [CrossRef]spa
dc.relation.references79. Song, Y.; Zhang, D.; Tang, Q.; Tang, S.; Yang, K. Local and nonlocal constraints for compressed sensing video and multi-view image recovery. Neurocomputing 2020, 406, 34–48. [CrossRef]spa
dc.relation.references80. Pal, S.; Mushtaq, M.; Banat, F.; Al Sumaiti, A.M. Review of surfactant-assisted chemical enhanced oil recovery for carbonate reservoirs: Challenges and future perspectives. Pet. Sci. 2018, 15, 77–102. [CrossRef]spa
dc.relation.references81. Ahmadi, M.; Chen, Z. Challenges and future of chemical assisted heavy oil recovery processes. Adv. Colloid Interface Sci. 2020, 275, 102081. [CrossRef]spa
dc.relation.references82. Zhou, S.; Qiu, J. Enhanced SSD with interactive multi-scale attention features for object detection. Multimed. Tools Appl. 2021, 80, 11539–11556. [CrossRef]spa
dc.relation.references83. Tang, Q.; Wang, K.; Yang, K.; Luo, Y.-S. Congestion-Balanced and Welfare-Maximized Charging Strategies for Electric Vehicles. IEEE Trans. Parallel Distrib. Syst. 2020, 31, 2882–2895. [CrossRef]spa
dc.relation.references84. Davarpanah, A. A feasible visual investigation for associative foam polymer injectivity performances in the oil recovery enhancement. Eur. Polym. J. 2018, 105, 405–411. [CrossRef]spa
dc.relation.references85. Davarpanah, A. Parametric Study of Polymer-Nanoparticles-Assisted Injectivity Performance for Axisymmetric Two-Phase Flow in EOR Processes. Nanomaterials 2020, 10, 1818. [CrossRef]spa
dc.relation.references86. Kvamme, B. Feasibility of simultaneous CO2 storage and CH4 production from natural gas hydrate using mixtures of CO2 and N. Can. J. Chem. 2015, 93, 897–905. [CrossRef]spa
dc.relation.references87. Cranganu, C. In-situ thermal stimulation of gas hydrates. J. Pet. Sci. Eng. 2009, 65, 76–80. [CrossRef]spa
dc.relation.references88. Yang, H.; Xu, Z.; Fan, M.; Gupta, R.; Slimane, R.B.; E Bland, A.; Wright, I. Progress in carbon dioxide separation and capture: A review. J. Environ. Sci. 2008, 20, 14–27. [CrossRef]spa
dc.relation.references89. Song, Y.; Li, J.; Chen, X.; Zhang, D.; Tang, Q.; Yang, K. An efficient tensor completion method via truncated nuclear norm. J. Vis. Commun. Image Represent. 2020, 70, 102791. [CrossRef]spa
dc.relation.references90. Wang, J.; Chen, W.; Ren, Y.; Alfarraj, O.; Wang, L. Blockchain based data storage mechanism in cyber physical system. J. Internet Technol. 2021, 21, 1681–1689. [CrossRef]spa
dc.relation.references91. Wang, Z.-W.; Wang, T.-J.; Wang, Z.-W.; Jin, Y. Organic modification of nano-SiO2 particles in supercritical CO2 . J. Supercrit. Fluids 2006, 37, 125–130. [CrossRef]spa
dc.relation.references92. Wang, X.; Luan, Z.; Li, K.; Li, L.; Tang, T. Progress in Application of Aerogels as Adsorbents for Gas Purification. Cailiao Daobao/Mater. Rev. 2018, 32, 2214–2222. [CrossRef]spa
dc.relation.references93. Wang, J.; Wu, W.; Liao, Z.; Jung, Y.W.; Kim, J.U. An enhanced PROMOT algorithm with D2D and robust for mobile edge computing. J. Internet Technol. 2020, 21, 1437–1445. [CrossRef]spa
dc.relation.references94. Liu, F.; Ellett, K.; Xiao, Y.; Rupp, J.A. Assessing the feasibility of CO2 storage in the New Albany Shale (Devonian–Mississippian) with potential enhanced gas recovery using reservoir simulation. Int. J. Greenh. Gas Control 2013, 17, 111–126. [CrossRef]spa
dc.relation.references95. Zuloaga, P.; Yu, W.; Miao, J.; Sepehrnoori, K. Performance evaluation of CO2 Huff-n-Puff and continuous CO2 injection in tight oil reservoirs. Energy 2017, 134, 181–192. [CrossRef]spa
dc.relation.references96. Huang, J.; Jin, T.; Barrufet, M.; Killough, J. Evaluation of CO2 injection into shale gas reservoirs considering dispersed distribution of kerogen. Appl. Energy 2020, 260, 114285. [CrossRef]spa
dc.relation.references97. Zhang, D.; Wang, S.; Li, F.; Tian, S.; Wang, J.; Ding, X.; Gong, R. An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function. Wirel. Commun. Mob. Comput. 2020, 2020, 1–11. [CrossRef]spa
dc.relation.references98. Mazarei, M.; Davarpanah, A.; Ebadati, A.; Mirshekari, B. The feasibility analysis of underground gas storage during an integration of improved condensate recovery processes. J. Pet. Explor. Prod. Technol. 2018, 9, 397–408. [CrossRef]spa
dc.relation.references99. Davarpanah, A.; Mirshekari, B. Experimental study of CO2 solubility on the oil recovery enhancement of heavy oil reservoirs. J. Therm. Anal. Calorim. 2020, 139, 1161–1169. [CrossRef]spa
dc.relation.references100. Tang, Q.; Wang, K.; Song, Y.; Li, F.; Park, J.H. Waiting Time Minimized Charging and Discharging Strategy Based on Mobile Edge Computing Supported by Software-Defined Network. IEEE Internet Things J. 2020, 7, 6088–6101. [CrossRef]spa
dc.relation.references101. Wang, M.; Deng, C.; Chen, H.; Wang, X.; Liu, B.; Sun, C.; Chen, G.; El-Halwagi, M.M. An analytical investigation on the energy efficiency of integration of natural gas hydrate exploitation with H2 production (by in situ CH4 reforming) and CO2 sequestration. Energy Convers. Manag. 2020, 216, 112959. [CrossRef]spa
dc.relation.references102. Lee, H.; Triviño, M.L.T.; Hwang, S.; Kwon, S.H.; Lee, S.G.; Moon, J.H.; Yoo, J.; Gil Seo, J. In Situ Observation of Carbon Dioxide Capture on Pseudo-Liquid Eutectic Mixture-Promoted Magnesium Oxide. ACS Appl. Mater. Interfaces 2018, 10, 2414–2422. [CrossRef]spa
dc.relation.references103. Hu, X.; Xie, J.; Cai, W.C.; Wang, R.; Davarpanah, A. Thermodynamic effects of cycling carbon dioxide injectivity in shale reservoirs. J. Pet. Sci. Eng. 2020, 195, 107717. [CrossRef]spa
dc.relation.references104. Sun, Y.; Du, Z.; Sun, L.; Pan, Y. Phase behavior of SCCO2 sequestration and enhanced natural gas recovery. J. Pet. Explor. Prod. Technol. 2017, 7, 1085–1093. [CrossRef]spa
dc.relation.references105. Weissman, S. Diffusion Coefficients for CO2–CH4 . J. Chem. Phys. 1971, 54, 1881–1883. [CrossRef]spa
dc.relation.references106. Davarpanah, A.; Mazarei, M.; Mirshekari, B. A simulation study to enhance the gas production rate by nitrogen replacement in the underground gas storage performance. Energy Rep. 2019, 5, 431–435. [CrossRef]spa
dc.relation.references107. Ebadati, A.; Akbari, E.; Davarpanah, A. An experimental study of alternative hot water alternating gas injection in a fractured model. Energy Explor. Exploit. 2018, 37, 945–959. [CrossRef]spa
dc.relation.references108. Kim, T.H.; Cho, J.; Lee, K.S. Evaluation of CO2 injection in shale gas reservoirs with multi-component transport and geomechanical effects. Appl. Energy 2017, 190, 1195–1206. [CrossRef]spa
dc.relation.references109. Oldenburg, C.; Law, D.-S.; Le Gallo, Y.; White, S. Mixing of CO2 and CH4 in Gas ReservoirsCode Comparison Studies. In Proceedings of the Greenhouse Gas Control Technologies—6th International Conference, Kyoto, Japan, 1–4 October 2002; pp. 443–448.spa
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