Carbon Storage Modeling Approaches Strengthening Subsurface Assessment Reliability for Long Term Geologic Containment

References

[1] Aalsalem, M. Y., Khan, W. Z., Gharibi, W., Khan, M. K., & Arshad, Q. (2018). Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges. Journal of network and computer applications, 113, 87-97.

[2] Agista, M. N., Guo, K., & Yu, Z. (2018). A state-of-the-art review of nanoparticles application in petroleum with a focus on enhanced oil recovery. Applied sciences, 8(6), 871.

[3] Akomea-Agyin, K., & Asante, M. (2019). Analysis of security vulnerabilities in wired equivalent privacy (WEP). International Research Journal of Engineering and Technology, 6(1), 529-536.

[4] Akpan, U. U., Adekoya, K. O., Awe, E. T., Garba, N., Oguncoker, G. D., & Ojo, S. G. (2017). Mini-STRs screening of 12 relatives of Hausa origin in northern Nigeria. Nigerian Journal of Basic and Applied Sciences, 25(1), 48-57.

[5] Akpan, U. U., Awe, T. E., & Idowu, D. (2019). Types and frequency of fingerprint minutiae in individuals of Igbo and Yoruba ethnic groups of Nigeria. Ruhuna Journal of Science, 10(1).

[6] Alfarge, D., Wei, M., & Bai, B. (2017, April). IOR methods in unconventional reservoirs of North America: Comprehensive review. In SPE Western Regional Meeting (p. D031S008R005). SPE.

[7] Al-Qahtani, K. Y., & Elkamel, A. (2011). Planning and integration of refinery and petrochemical operations. John Wiley & Sons.

[8] Amirian, E., Dejam, M., & Chen, Z. (2018). Performance forecasting for polymer flooding in heavy oil reservoirs. Fuel, 216, 83-100.

[9] Amirian, E., Fedutenko, E., Yang, C., Chen, Z., & Nghiem, L. (2018). Artificial neural network modeling and forecasting of oil reservoir performance. Applications of Data Management and Analysis: Case Studies in Social Networks and Beyond, 43-67.

[10] Anderson, S. T. (2017). Risk, liability, and economic issues with long-term CO2 storage a review. Natural Resources Research, 26(1), 89-112.

[11] Asante, M., & Akomea-Agyin, K. (2019). Analysis of security vulnerabilities in wifi-protected access pre-shared key.

[12] Aslanyan, A., Gilfanov, A., Gulyaev, D., Krichevsky, V., & Timerbaev, M. (2017, October). Dynamic reservoir-pressure maintenance system study in carbonate reservoir with complicated pore structure by production analysis, production logging and well-testing. In SPE Russian Petroleum Technology Conference (p. D023S008R003). SPE.

[13] Awe, E. T. (2017). Hybridization of snout mouth deformed and normal mouth African catfish Clarias gariepinus. Animal Research International, 14(3), 2804-2808.

[14] Awe, E. T., & Akpan, U. U. (2017). Cytological study of Allium cepa and Allium sativum.

[15] Awe, E. T., Akpan, U. U., & Adekoya, K. O. (2017). Evaluation of two MiniSTR loci mutation events in five Father-Mother-Child trios of Yoruba origin. Nigerian Journal of Biotechnology, 33, 120-124.

[16] Balaji, K., Rabiei, M., Suicmez, V., Canbaz, C. H., Agharzeyva, Z., Tek, S., ... & Temizel, C. (2018, June). Status of data-driven methods and their applications in oil and gas industry. In SPE Europec featured at EAGE Conference and Exhibition? (p. D031S005R007). SPE.

[17] Bello, O., Yang, D., Lazarus, S., Wang, X. S., & Denney, T. (2017, May). Next generation downhole big data platform for dynamic data-driven well and reservoir management. In SPE Reservoir Characterisation and Simulation Conference and Exhibition (p. D031S014R002). SPE.

[18] Benyeogor, O., Jambol, D., Amah, O., Obiga, D., Awe, S., & Erinle, A. (2019, August). Pressure relief management philosophy for MPD operations on surface stack HPHT exploration wells. In SPE Nigeria Annual International Conference and Exhibition (p. D033S014R005). SPE.

[19] Biniwale, S., Nitura, J., Sifuentes, W., Ramdzani, R., Talabi, O. A., Seruji, N. E., ... & Koundarkar, O. (2016, November). Integrated Live Asset Modeling-A Necessity to Unlock Oil in Mature Fields Achieving True Integrated Operations IO. In International Petroleum Technology Conference (p. D031S048R002). IPTC.

[20] Brentan, B., Meirelles, G., Luvizotto Jr, E., & Izquierdo, J. (2018). Joint operation of pressure-reducing valves and pumps for improving the efficiency of water distribution systems. Journal of Water Resources Planning and Management, 144(9), 04018055.

[21] Brown, J. B., Salehi, A., Benhallam, W., & Matringe, S. F. (2017, April). Using data-driven technologies to accelerate the field development planning process for mature field rejuvenation. In SPE Western Regional Meeting (p. D041S012R001). SPE.

[22] Caineng, Z. O. U., Yunhong, D. I. N. G., Yongjun, L. U., Xiangui, L., Jianjun, C., Xin, W., ... & Yutian, L. U. O. (2017). Concept, technology and practice of “man-made reservoirs” development. Petroleum Exploration and Development, 44(1), 146-158.

[23] Castro, A., Laksana, S., Abdussalam, G., Allotai, O., Shaiebi, M., Alkhemri, T., ... & Omar, F. (2012, November). Integrated Reservoir Modeling On Heterogeneous Ordovician Sandstone Of IR-Fields, Murzuq Basin Libya, Helps Optimizing Field Development Plan And Reduce Uncertainty. In Abu Dhabi International Petroleum Exhibition and Conference (pp. SPE-161197). SPE.

[24] Davarpanah, A., Mirshekari, B., Jafari Behbahani, T., & Hemmati, M. (2018). Integrated production logging tools approach for convenient experimental individual layer permeability measurements in a multi-layered fractured reservoir. Journal of Petroleum Exploration and Production Technology, 8(3), 743-751.

[25] Denney, D. (2011). Modeling, History Matching, Forecasting, and Analysis of Shale-Reservoir Performance With Artificial Intelligence. Journal of Petroleum Technology, 63(09), 60-63.

[26] Druetta, P., Tesi, P., De Persis, C., & Picchioni, F. (2016). Methods in oil recovery processes and reservoir simulation. Advances in Chemical Engineering and Science, 399-435.

[27] Eli, A., Aboaja, U., & Ajayi, A. (2013, August). Integrated production System Modeling (IPSM) as an opportunity Realization and optimization tool for improved asset management. In SPE Nigeria Annual International Conference and Exhibition (pp. SPE-167557). SPE.

[28] Esmaili, S., & Mohaghegh, S. D. (2016). Full field reservoir modeling of shale assets using advanced data-driven analytics. Geoscience Frontiers, 7(1), 11-20.

[29] Fayaed, S. S., El-Shafie, A., & Jaafar, O. (2013). Reservoir-system simulation and optimization techniques. Stochastic environmental research and risk assessment, 27(7), 1751-1772.

[30] Freifeld, B. M., Oldenburg, C. M., Jordan, P., Pan, L., Perfect, S., Morris, J., ... & Rose, K. (2016). Well Integrity for Natural Gas Storage in Depleted Reservoirs and Aquifers (No. LBNL-1006165). Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States).

[31] Ghassemzadeh, S., & Charkhi, A. H. (2016). Optimization of integrated production system using advanced proxy based models: A new approach. Journal of Natural Gas Science and Engineering, 35, 89-96.

[32] Gherardi, F., Audigane, P., & Gaucher, E. C. (2012). Predicting long-term geochemical alteration of wellbore cement in a generic geological CO2 confinement site: Tackling a difficult reactive transport modeling challenge. Journal of Hydrology, 420, 340-359.

[33] Gopa, K., Yamov, S., Naugolnov, M., Perets, D., & Simonov, M. (2018, October). Cognitive analytical system based on data-driven approach for mature reservoir management. In SPE Russian Petroleum Technology Conference (p. D013S002R003). SPE.

[34] Goudarzi, A., Delshad, M., & Sepehrnoori, K. (2013, February). A critical assessment of several reservoir simulators for modeling chemical enhanced oil recovery processes. In SPE Reservoir Simulation Conference (pp. SPE-163578). SPE.

[35] Hafez, H., Al Mansoori, Y., Bahamaish, J., Saputelli, L., Escorcia, A., Sousa, S., ... & Mijares, G. (2018, November). Large Scale Subsurface and Surface Integrated Asset Modeling-An Effective Outcome Driven Approach. In Abu Dhabi International Petroleum Exhibition and Conference (p. D031S087R002). SPE.

[36] He, M., Luis, S., Rita, S., Ana, G., Euripedes Jr, V., & Zhang, N. (2011). Risk assessment of CO2 injection processes and storage in carboniferous formations: a review. Journal of Rock Mechanics and Geotechnical Engineering, 3(1), 39-56.

[37] Hoda, M. F., Hoffmann, A., Majzoub Dahouk, M., Kuntadi, A., Astutik, W., Juell, A., & Whitson, C. H. (2017, May). Successful Implementations of Integrated Asset Modeling. In SPE Middle East Intelligent Oil and Gas Symposium (pp. SPE-187471). SPE.

[38] Holanda, R. W. D., Gildin, E., Jensen, J. L., Lake, L. W., & Kabir, C. S. (2018). A state-of-the-art literature review on capacitance resistance models for reservoir characterization and performance forecasting. Energies, 11(12), 3368.

[39] Hou, J., Zhou, K., Zhang, X. S., Kang, X. D., & Xie, H. (2015). A review of closed-loop reservoir management. Petroleum Science, 12(1), 114-128.

[40] Islam, M. R., Hossain, M. E., Mousavizadegan, S. H., Mustafiz, S., & Abou-Kassem, J. H. (2016). Advanced petroleum reservoir simulation: Towards developing reservoir emulators. John Wiley & Sons.

[41] Jiang, J., & Younis, R. M. (2016). Hybrid coupled discrete-fracture/matrix and multicontinuum models for unconventional-reservoir simulation. SPE Journal, 21(03), 1009-1027.

[42] Jiang, X., Hassan, W. A. A., & Gluyas, J. (2013). Modelling and monitoring of geological carbon storage: A perspective on cross-validation. Applied energy, 112, 784-792.

[43] Kamari, A., Nikookar, M., Sahranavard, L., & Mohammadi, A. H. (2014). Efficient screening of enhanced oil recovery methods and predictive economic analysis. Neural Computing and Applications, 25(3), 815-824.

[44] Kang, B., & Choe, J. (2017). Initial model selection for efficient history matching of channel reservoirs using Ensemble Smoother. Journal of Petroleum Science and Engineering, 152, 294-308.

[45] Kang, P. S., Lim, J. S., & Huh, C. (2016). Screening criteria and considerations of offshore enhanced oil recovery. Energies, 9(1), 44.

[46] Katterbauer, K., Arango, S., Sun, S., & Hoteit, I. (2015). Multi-data reservoir history matching for enhanced reservoir forecasting and uncertainty quantification. Journal of Petroleum Science and Engineering, 128, 160-176.

[47] Khor, C. S., Elkamel, A., & Shah, N. (2017). Optimization methods for petroleum fields development and production systems: a review. Optimization and Engineering, 18(4), 907-941.

[48] Kovscek, A. R. (2012). Emerging challenges and potential futures for thermally enhanced oil recovery. Journal of Petroleum Science and Engineering, 98, 130-143.

[49] Kurtoglu, B. (2013). Integrated reservoir characterization and modeling in support of enhanced oil recovery for Bakken. Colorado School of Mines.

[50] Lehnert, M., Linhart, A., & Röglinger, M. (2016). Value-based process project portfolio management: integrated planning of BPM capability development and process improvement. Business Research, 9(2), 377-419.

[51] Li, Q., & Liu, G. (2016). Risk assessment of the geological storage of CO2: A review. Geologic carbon sequestration: Understanding reservoir behavior, 249-284.

[52] Lifton, V. A. (2016). Microfluidics: an enabling screening technology for enhanced oil recovery (EOR). Lab on a Chip, 16(10), 1777-1796.

[53] Liu, D., & Sun, J. (2017). The control theory and application for well pattern optimization of heterogeneous sandstone reservoirs (pp. 334-363). Berlin/Heidelberg, Germany: Springer.

[54] Mabo, T., Swar, B., & Aghili, S. (2018, March). A vulnerability study of Mhealth chronic disease management (CDM) applications (apps). In World Conference on Information Systems and Technologies (pp. 587-598). Cham: Springer International Publishing.

[55] Manceau, J. C., Hatzignatiou, D. G., De Lary, L., Jensen, N. B., & Réveillère, A. (2014). Mitigation and remediation technologies and practices in case of undesired migration of CO2 from a geological storage unit Current status. International Journal of Greenhouse Gas Control, 22, 272-290.

[56] Mijnarends, R., Frolov, A., Grishko, F., Kryanev, S., Mikhaylenko, E., Nizamutdinov, E., ... & Lvov, A. (2015, September). Advanced data-driven performance analysis for mature waterfloods. In SPE Annual Technical Conference and Exhibition? (p. D021S011R008). SPE.

[57] Mohaghegh, S. D., Gaskari, R., Maysami, M., & Khazaeni, Y. (2014, October). Data-driven reservoir management of a giant mature oilfield in the Middle East. In SPE Annual Technical Conference and Exhibition? (pp. SPE-170660). SPE.

[58] Muggeridge, A., Cockin, A., Webb, K., Frampton, H., Collins, I., Moulds, T., & Salino, P. (2014). Recovery rates, enhanced oil recovery and technological limits. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372(2006).

[59] Myer, L. (2011). Global status of geologic CO2 storage technology development. United States carbon sequestration council report July, 2011.

[60] Namhata, A., Oladyshkin, S., Dilmore, R. M., Zhang, L., & Nakles, D. V. (2016). Probabilistic assessment of above zone pressure predictions at a geologic carbon storage site. Scientific reports, 6(1), 39536.

[61] Nazarov, R., Zalama, P., Hernandez, M., & Rivas, C. (2014, June). Integrated Asset Modeling in Mature Offshore Fields: Challenges and Successes. In SPE Trinidad and Tobago Section Energy Resources Conference (p. D031S013R002). SPE.

[62] Nwankwor, E. (2014). A unified metaheuristic and system-theoretic framework for petroleum reservoir management (Doctoral dissertation, University of Liverpool).

[63] Ogundipe, F., Sampson, E., Bakare, O. I., Oketola, O., & Folorunso, A. (2019). Digital Transformation and its Role in Advancing the Sustainable Development Goals (SDGs). transformation, 19, 48.

[64] Olajire, A. A. (2014). Review of ASP EOR (alkaline surfactant polymer enhanced oil recovery) technology in the petroleum industry: Prospects and challenges. Energy, 77, 963-982.

[65] Oliveira, T., Thomas, M. and Espadanal, M., 2014. Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & management, 51(5), pp.497-510.

[66] Oni, O., Adeshina, Y. T., Iloeje, K. F., & Olatunji, O. O. (2018). Artificial Intelligence Model Fairness Auditor For Loan Systems. Journal ID, 8993, 1162.

[67] Osabuohien, F. O. (2017). Review of the environmental impact of polymer degradation. Communication in Physical Sciences, 2(1).

[68] Osabuohien, F. O. (2019). Green Analytical Methods for Monitoring APIs and Metabolites in Nigerian Wastewater: A Pilot Environmental Risk Study. Communication In Physical Sciences, 4(2), 174-186.

[69] Owulade, O. A., Isi, L. R., Okereke, M., Sofoluwe, O., Isaac, G., Olugbemi, T., & Essien, N. A. (2019). Review of Reliability Engineering Techniques to Optimize Performance and Risk Management in Energy Infrastructure. Burns, 18.

[70] Pal, S., Mushtaq, M., Banat, F., & Al Sumaiti, A. M. (2018). Review of surfactant-assisted chemical enhanced oil recovery for carbonate reservoirs: challenges and future perspectives. Petroleum Science, 15(1), 77-102.

[71] Parada, C. H., & Ertekin, T. (2012, March). A new screening tool for improved oil recovery methods using artificial neural networks. In SPE Western Regional Meeting (pp. SPE-153321). SPE.

[72] Pathak, V., Palaschak, C., Martinez, O., Hudson, J., Ita, J., Saaf, F., ... & Segundo, J. (2016, September). Solving the challenges of short and long-term production forecasting and uncertainty using a fully-coupled implicit integrated production modelling system. In SPE Annual Technical Conference and Exhibition? (p. D021S022R008). SPE.

[73] Pérez, F., Tillero, E., Pérez, E., Niño, P., Rojas, J., Araujo, J., ... & Piña, M. (2012, May). An Innovative Integrated Asset Modeling for an Offshore-Onshore Field Development. Tomoporo Field Case. In SPE International Production and Operations Conference and Exhibition (pp. SPE-157556). SPE.

[74] Pope, G. A. (2011). Recent developments and remaining challenges of enhanced oil recovery. Journal of Petroleum Technology, 63(07), 65-68.

[75] Reddicharla, N., Meqbali, N. A., AlSelaiti, I. H., & Singh, S. (2017, November). Best Practices for Successful Implementation of Integrated Asset Model Based Well and Reservoir Workflow Automation-A Practical Learning Experience from Mature Brown Fields. In Abu Dhabi International Petroleum Exhibition and Conference (p. D031S077R005). SPE.

[76] Riazi, S. H., Zargar, G., Baharimoghadam, M., Moslemi, B., & Darani, E. S. (2016). Fractured reservoir history matching improved based on artificial intelligent. Petroleum, 2(4), 344-360.

[77] Ringrose, P., & Bentley, M. (2016). Reservoir model design (Vol. 2). Berlin: Springer.

[78] Rodosta, T., & Ackiewicz, M. (2014). US DOE/NETL Core R&D program for carbon storage technology development. Energy Procedia, 63, 6368-6378.

[79] Rodosta, T., Bromhal, G., & Damiani, D. (2018, October). US DOE/NETL Carbon Storage Program: Addressing Key Science and Technology Challenges. In 14th Greenhouse Gas Control Technologies Conference Melbourne (pp. 21-26).

[80] Rui, Z., Lu, J., Zhang, Z., Guo, R., Ling, K., Zhang, R., & Patil, S. (2017). A quantitative oil and gas reservoir evaluation system for development. Journal of Natural Gas Science and Engineering, 42, 31-39.

[81] Rwechungura, R., Dadashpour, M., & Kleppe, J. (2011, September). Advanced history matching techniques reviewed. In SPE Middle East Oil and Gas Show and Conference (pp. SPE-142497). SPE.

[82] Santos, S. M., Gaspar, A. T., & Schiozer, D. J. (2018). Managing reservoir uncertainty in petroleum field development: Defining a flexible production strategy from a set of rigid candidate strategies. Journal of Petroleum Science and Engineering, 171, 516-528.

[83] Sardinha, C., Petr, C., Lehmann, J., & Pyecroft, J. (2014, September). Determining interwell connectivity and reservoir complexity through frac pressure hits and production interference analysis. In SPE Canada Unconventional Resources Conference (p. D031S010R005). SPE.

[84] Satter, A., & Iqbal, G. M. (2015). Reservoir engineering: the fundamentals, simulation, and management of conventional and unconventional recoveries. Gulf Professional Publishing.

[85] Schultz, R. A., Mutlu, U., & Bere, A. (2016, June). Critical issues in subsurface integrity. In ARMA US rock mechanics/geomechanics symposium (pp. ARMA-2016). ARMA.

[86] Selvaggio, P., Freni, F., Rossi, R., Di Giorgio, D. C., & Colombo, I. (2018, November). New generation integrated asset modelling: High-resolution reservoir multi models coupled with an external steady-State network solver. In Abu Dhabi International Petroleum Exhibition and Conference (p. D022S155R001). SPE.

[87] Semenov, A., Altaf, B., Allouti, A., Bu-Hindi, H., & Ibrahim, L. (2017, November). History matching of integrated reservoir simulation model for green field offshore Abu Dhabi. In Abu Dhabi International Petroleum Exhibition and Conference (p. D021S030R002). SPE.

[88] Shafiei, A., Dusseault, M. B., Zendehboudi, S., & Chatzis, I. (2013). A new screening tool for evaluation of steamflooding performance in Naturally Fractured Carbonate Reservoirs. Fuel, 108, 502-514.

[89] Shah, N. K., Li, Z., & Ierapetritou, M. G. (2011). Petroleum refining operations: key issues, advances, and opportunities. Industrial & Engineering Chemistry Research, 50(3), 1161-1170.

[90] Tavallali, M. S., & Karimi, I. A. (2016). Integrated oil-field management: from well placement and planning to production scheduling. Industrial & Engineering Chemistry Research, 55(4), 978-994.

[91] Temizel, C., Aktas, S., Kirmaci, H., Susuz, O., Zhu, Y., Balaji, K., ... & Yegin, C. (2016, September). Turning Data into Knowledge: Data-Driven Surveillance and Optimization in Mature Fields. In SPE Annual Technical Conference and Exhibition? (p. D031S057R001). SPE.

[92] Temizel, C., Canbaz, C. H., Tran, M., Abdelfatah, E., Jia, B., Putra, D., ... & Alkouh, A. (2018, December). A comprehensive review heavy oil reservoirs, latest techniques, discoveries, technologies and applications in the oil and gas industry. In SPE International Heavy Oil Conference and Exhibition (p. D012S024R001). SPE.

[93] Toby, S. (2014, October). Making the Best of Integrated Asset Modelling. In SPE Russian Petroleum Technology Conference (pp. SPE-171161). SPE.

[94] Trebolle, R., Hamdan, V. H., Sepulveda, W., Whitney, P., Grigorescu, R., Mohammad, A., ... & Curteis, C. (2011, May). Opportunities/Lessons Learnt and New Technologies for Redevelopment of a Mature Field. In SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition (pp. SPE-149105). SPE.

[95] Udy, J., Hansen, B., Maddux, S., Petersen, D., Heilner, S., Stevens, K., ... & Hedengren, J. D. (2017). Review of field development optimization of waterflooding, EOR, and well placement focusing on history matching and optimization algorithms. Processes, 5(3), 34.

[96] Uemura, S., Kataoka, R., Fukabori, D., Tsushima, S., & Hirai, S. (2011). Experiment on liquid and supercritical CO2 distribution using micro-focus X-ray CT for estimation of geological storage. Energy procedia, 4, 5102-5107.

[97] Uzondu, F. N., & Ofoedu, A. T. (2014). Modeling Of Asphaltic Sludge Generation from Spent Engine Oil.

[98] Uzondu, F. N., & Ofoedu, A. T. (2011): Feasibility Of Spent Engine Oil And Charcoal As Raw Materials For The Production Of Black Printing Ink.

[99] Wenrui, H., Jingwei, B., & Bin, H. (2013). Trend and progress in global oil and gas exploration. Petroleum Exploration and Development, 40(4), 439-443.

[100] Wilson, A. (2018). Data-Driven Technologies Accelerate Planning for Mature-Field Rejuvenation. Journal of Petroleum Technology, 70(01), 54-57.

[101] Yap, F. K. (2016). Representative models for history matching and robust optimization (Doctoral dissertation, MSc thesis, Delft University of Technology).

[102] Younis, R. M. (2011). Modern advances in software and solution algorithms for reservoir simulation. Stanford University.

[103] Yuan, B., & Wood, D. A. (2018). A comprehensive review of formation damage during enhanced oil recovery. Journal of Petroleum Science and Engineering, 167, 287-299.

[104] Zhao, H., Kang, Z., Zhang, X., Sun, H., Cao, L., & Reynolds, A. C. (2016). A physics-based data-driven numerical model for reservoir history matching and prediction with a field application. SPE Journal, 21(06), 2175-2194.

@article{1712853,
      author = {Lymmy Ogbidi, Benneth Oteh},
      title = {Carbon Storage Modeling Approaches Strengthening Subsurface Assessment Reliability for Long Term Geologic Containment},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {5},
      pages = {367-387},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712853.pdf},
      abstract = {Carbon capture and storage (CCS) technologies are essential for mitigating climate change by reducing atmospheric CO? emissions. Effective long-term geologic containment of carbon dioxide (CO?) in subsurface reservoirs is crucial to the success of CCS projects. This abstract explores various carbon storage modeling approaches that enhance the reliability of subsurface assessments for geologic CO? containment over extended periods. The primary modeling strategies include reservoir simulation models, geomechanical models, and geochemical models, each contributing unique insights into the behavior and stability of CO? in storage sites. Reservoir simulation models simulate fluid flow and pressure changes within the reservoir, enabling accurate predictions of CO? migration, trapping mechanisms, and potential leakage paths. Geomechanical models assess the integrity of cap rock seals and fault zones to ensure the physical containment of CO? under varying geological conditions. Geochemical models focus on the chemical interactions between CO?, formation water, and rock minerals, which influence long-term CO? dissolution, mineral trapping, and the potential for reservoir corrosion. Integrating these models with real-time monitoring data enhances the accuracy of predictions and supports adaptive management strategies for reservoir performance. The reliability of these models is further improved by advanced data assimilation techniques, machine learning algorithms, and high-resolution geological characterization, all of which allow for continuous model refinement and better decision-making during storage site development and operation. These approaches are critical for assessing the suitability of potential storage sites, minimizing risks, and ensuring the safe, effective, and permanent containment of CO?. As such, the development of more robust and accurate modeling techniques will significantly contribute to the scalability and sustainability of CCS technologies, ultimately playing a key role in global efforts to combat climate change.},
      keywords = {Carbon Capture and Storage (CCS), carbon dioxide (CO?) Containment, Subsurface Modeling, Reservoir Simulation, Geomechanics, Geochemistry, Long-Term Storage, Geologic Containment, Monitoring Data, Machine Learning.},
      month = {November},
  }