Dr. Archana is an Assistant Professor in the Department of Petroleum Engineering at IIT (Indian School of Mines), Dhanbad, with over 19 years of experience spanning academia, research, and the software industry. She holds a PhD (IIT-ISM Dhanbad, 2018), M.Tech (IIT Guwahati, 2006), and B.Sc. Engineering in Chemical Engineering (BIT Sindri, 2004). Prior to academia, she worked at Wipro Technologies as a Business Analyst, Software Developer, and Team Lead. Her research expertise includes carbon capture, utilization and storage (CCUS), enhanced oil recovery, reservoir characterization, tracer techniques, unconventional resources, and AI/ML applications in petroleum engineering, with several publications in reputed journals such as Energy & Fuels and Journal of Petroleum Science and Engineering. She has led and contributed to multiple funded research projects and industry consultancy assignments, including SERB-supported work on bioremediation and projects with oil and gas companies. She has also organized international conferences and FDPs, delivered NPTEL courses, and completed extensive training in reservoir simulation and AI/ML tools through CMG and other platforms. She is a member of SPE and the Indian Institute of Chemical Engineers and actively teaches courses in petroleum engineering, AI applications, reservoir modeling, drilling, and carbon capture technologies.        

Reservoir Simulation using synthetic data for CO2 Sequestration in saline aquifers using CMG simulator Abstarct:  IntroductionCO₂ sequestration in saline aquifers is a promising solution for mitigating greenhouse gas emissions. These deep geological formations offer vast storage capacities for securely trapping CO₂ through multiple mechanisms: Structural Trapping (CO₂ is contained beneath impermeable cap rock layers), Residual Trapping (Capillary forces immobilize CO₂ in pore spaces), Solubility Trapping (CO₂ dissolves into formation brine), Mineral Trapping (CO₂ reacts with minerals form stable carbonate components). Understanding these mechanisms and their interactions is critical for optimizing storage efficiency and ensuring long-term security. The objective of the study is to do extensive sensitivity analysis on model of layered permeability using commercial simulator CMG, to analyze the influence of geological parameters (porosity, pressure, permeability, thickness) and other factor such as injection rate on the trapping mechanisms. These are done to determine which of the trapping mechanism is more likely effective to hold CO₂ in long termstorage. MethodCMG builder was used to develop a basic 3D reservoir model, the parameter input are as below mentioned. A 5-spot pattern well is considered in the current study. A well is placed at the centre of model to act as injection well with well being perforated throughout the model at each layer, it allows us to study the CO₂ plume migration in all directions. Four production wells are placed at edges to simulate pressure management, preventing excessive pressure build up in the aquifer. The aquifer dimensions are 53000x53000x1000ft, with permeability is taken. The aquifer model is structured in a 40x40x40 grid arrangement (64000 blocks).   ConclusionsBased on the study, solubility trapping is dominant during the injection phase, whereas residual trapping overtakes it post-injection, particularly in high-pressure, high-permeability, and highly porous aquifers. The thickness of the aquifer plays a crucial role, with larger layers storing more CO₂ and favoring residual trapping over solubility trapping. Injection rate variations also impact the trapping mechanisms, where higher rates enhance residual trapping but reduce solubility trapping efficiency. Higher porosity facilitates solubility trapping initially, while residual trapping strengthens over time. Pressure variations significantly influence residual trapping, making high-pressure environments more effective for longterm CO₂ storage. The permeability ratio further affects trapping efficiency, where increased horizontal permeability enhances CO₂ retention. Overall, the study suggests that optimizing aquifer selection, pressure conditions, and injection rates can maximize CO₂ storage efficiency, with residual trapping playing a dominant role in long-term sequestration. AcknowledgementWe sincerely thank CMG (Computer Modelling Group) for generously providing academic license of CMG software to our Institute, Department of Petroleum Engineering, IIT(ISM). This invaluable support has greatly contributed to fostering advanced research in reservoir simulation and modeling. References Luo, A., Li, Y., Chen, X., Zhu, Z. and Peng, Y., 2022. Review of CO2 sequestration mechanism in saline aquifers. Natural Gas Industry B, 9(4), pp.383-393. Zapata, Y., Kristensen, M.R., Huerta, N., Brown, C., Kabir, C.S. and Reza, Z., 2020. CO2 geological storage: Critical insights on plume dynamics and storage efficiency during long-term injection and post-injection periods. Journal of Natural Gas Science and Engineering, 83, p.103542. De Silva, G.P.D., Ranjith, P.G. and Perera, M.S.A., 2015. Geochemical aspects of CO2 sequestration in deep saline aquifers: A review. Fuel, 155, pp.128-143. Kumar, A., 2004. A simulation study of carbon sequestration in deep saline aquifers (Doctoral dissertation, University of Texas at Austin).