Implementing Carbon Storage Technology to Combat Climate Change

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Injecting CO2 into depleted oil reservoirs is a key strategy for mitigating excess atmospheric carbon. In India, where Carbon Capture, Utilization, and Storage (CCUS) incentives are limited, EOR (Enhanced Oil Recovery) serves as commercially viable method carbon storage. Effective reservoir management (RM) critical to ensuring the success of injection, it integrates insights from primary, secondary, EOR/EOR+ phases. This study introduces an integrated storage development tailored mature Indian oilfield, employing both analytical numerical tools conduct thorough analysis stacked pay reservoir. Using over 30 years dynamic field data, we identified potential exceeding five million metric tons, alongside incremental recovery factor 11% original oil-in-place (OOIP). Additionally, eliminating waterflooding stage enhances capacity by estimated 0.5 while ongoing aquifer water production could contribute approximately 0.35 tons annually. approach highlights significant in fields, emphasizing importance RM, particularly regions lacking robust CCUS policies.

Language: Английский

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Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(16), P. 14891 - 14924

Published: July 25, 2024

The carbon dioxide (CO2) based enhanced oil recovery methods (EORs) are considered among the promising techniques for increasing factor from mature reservoirs and reducing amount of CO2 emission in atmosphere. Determining minimum miscibility pressure (MMP) – systems is a crucial step successfully implementing EOR processes. Therefore, various approaches have been proposed determining this key parameter. However, laboratory tests expensive time-consuming, while most available correlations present moderate accuracy. To address these shortcomings, studies applied artificial intelligence (AI) to model MMP systems. In study, we reviewed published works predicting using AI-based models. Our analyses revealed robustness modeling MMP. context, it was noticed that more than 70 paradigms utilized estimating Among applications, hybrid schemes combining machine learning nature-inspired algorithms (ML-NIA) take top spot, accounting 27% applications. Additionally, investigation demonstrated neural network (ANN) ML method phase, genetic algorithm (GA) widely NIA improving performance. second part suggest an updated correlation on gene expression programming (GEP) accurate prediction explicit yielded excellent predictive performance, achieving overall root-mean-square error determination coefficient (R2) values 0.9253 09713, respectively. These statistical metrics enabled newly GEP-based outperform preexisting Besides, physical validity interpretability were proven trend analysis Shape Dependence Analysis plot, Lastly, findings study provide dual benefit, review illustration applying AI systems; second, implemented can significantly enhance effective CO2-oil systems, thus, facilitating simulation

Language: Английский

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Explicit and Explainable Artificial Intelligent Model for Prediction of CO2 Molecular Diffusion Coefficient in Heavy Crude Oils and Bitumen

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103328 - 103328

Published: Nov. 6, 2024

Language: Английский

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Development of Advanced Machine Learning Models for Predicting CO2 Solubility in Brine

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1202 - 1202

Published: Feb. 28, 2025

This study explores the application of advanced machine learning (ML) models to predict CO2 solubility in NaCl brine, a critical parameter for effective carbon capture, utilization, and storage (CCUS). Using comprehensive database 1404 experimental data points spanning temperature (−10 450 °C), pressure (0.098 140 MPa), salinity (0.017 6.5 mol/kg), research evaluates predictive capabilities five ML algorithms: Decision Tree, Random Forest, XGBoost, Multilayer Perceptron, Support Vector Regression with radial basis function kernel. Among these, XGBoost demonstrated highest overall accuracy, achieving an R2 value 0.9926, low root mean square error (RMSE) absolute (MAE) 0.0655 0.0191, respectively. A feature importance analysis revealed that has most impactful effect positively correlates solubility, while generally exhibits negative effect. higher accuracy was found when developed model compared one well-established empirical ML-based from literature. The results underscore potential significantly enhance prediction over wide range, reduce computational costs, improve efficiency CCUS operations. work demonstrates robustness adaptability approaches modeling complex subsurface conditions, paving way optimized sequestration strategies.

Language: Английский

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Exploring the Potential of Offshore Geological CO₂ Storage in Canada: A Comprehensive Review and Future Outlook

Energy & Fuels, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

Offshore Geological Carbon Storage (GCS) stands at the intersection of energy innovation, climate policy, and marine resource management, offering a strategic approach to reducing atmospheric CO2 levels. Canada's offshore regions present substantial opportunities for large-scale GCS, potentially mitigating portion country's 670 million tonnes annual emissions. While onshore sites have been more extensively examined, Canadian formations offer an underutilized capacity that can be leveraged achieve meaningful targets. This review canvasses extensive evidence based on GCS potential, drawing together multidisciplinary perspectives address site characterization, operational practices, economic dynamics, governance complexities. The intention is provide technically rigorous yet accessible overview elucidates requirements safe efficient GCS. After assessing comprehensive screening criteria selection, we explore technical intricacies govern successful spanning well construction, reservoir real-time monitoring methods. dimension scrutinized with comparative lens placed cost structures versus projects, capital expenses, potential revenue streams. Construction installation constitute 70–80% structure costs, subsea pipelines adding 10–30% overall project costs. Detailed analyses regulatory landscape reveal significant complexity, overlapping jurisdictions lack legal clarity liability long-term stewardship. Indigenous engagement stakeholder consultation remain critical ensuring equitable socially accepted development. Throughout, environmental social dimensions are kept in view. Potential leakage pathways, induced seismicity, ecosystem impacts discussed. Drawing best practices from established international this highlights adaptive learning Canada undertake. In bringing these diverse strands─geoscience, engineering, economics, law, environment, society─this aims illuminate practical pathways advancing Canada.

Language: Английский

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Machine Learning in Carbon Capture, Utilization, Storage, and Transportation: A Review of Applications in Greenhouse Gas Emissions Reduction

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1160 - 1160

Published: April 11, 2025

Carbon Capture, Utilization, and Storage (CCUS) technologies have emerged as indispensable tools in reducing greenhouse gas (GHG) emissions combating climate change. However, the optimization scalability of CCUS processes face significant technical economic challenges that hinder their widespread implementation. Machine Learning (ML) offers innovative solutions by providing faster, more accurate alternatives to traditional methods across value chain. Despite growing body research this field, applications ML remain fragmented, lacking a cohesive synthesis bridges these advancements practical This review addresses gap systematically evaluating all major components—CO2 capture, transport, storage, utilization. We provide structured representative examples for each category critically examine various techniques, objectives, methodological frameworks employed recent studies. Additionally, we identify key parameters, limitations, future opportunities applying enhance systems. Our thus comprehensive insights guidance stakeholders, supporting informed decision-making accelerating ML-driven commercialization.

Language: Английский

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Review of progress and implication of machine learning in geological carbon dioxide storage

Geosystem Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 34

Published: April 30, 2025

Language: Английский

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Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery

Journal of Petroleum Exploration and Production Technology, Journal Year: 2025, Volume and Issue: 15(3)

Published: Feb. 25, 2025

Language: Английский

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Predicting filtration coefficient and formation damage coefficient for particle flow in porous media using machine learning

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104545 - 104545

Published: March 1, 2025

Language: Английский

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Enhanced Solubility and Miscibility of CO2-Oil Mixture in the Presence of Propane under Reservoir Conditions to Improve Recovery Efficiency

Energies, Journal Year: 2024, Volume and Issue: 17(19), P. 4790 - 4790

Published: Sept. 25, 2024

The existence of propane (C3H8) in a CO2-oil mixture has great potential for increasing CO2 solubility and decreasing minimum miscibility pressure (MMP). In this study, the enhanced solubility, reduced viscosity, lowered MMP CO2-saturated crude oil presence various amounts C3H8 have been systematically examined at reservoir conditions. Experimentally, piston-equipped pressure/volume/temperature (PVT) cell is first validated by accurately reproducing bubble-point pressures pure component temperatures 30, 40, 50 °C with both continuous stepwise depressurization methods. well utilized to measure saturation CO2-C3H8-oil systems identifying turning point on P-V diagram given temperature. Accordingly, gas solubilities CO2, C3H8, CO2-C3H8 up 1600 psi temperature range 25–50 are measured. addition, viscosity gas-saturated single liquid phase measured using an in-line viscometer, where maintained be higher than its pressure. Theoretically, modified Peng–Robinson equation state (PR EOS) as primary thermodynamic model work. characterized multiple pseudo-component(s). An exponential distribution function, together logarithm-type lumping method, applied characterize oil. Two linear binary interaction parameters (BIP) correlations developed binaries C3H8-oil reproduce pressures. Moreover, MMPs absence determined assistance tie-line method. It found that mathematical can calculate and/or absolute average relative deviation (AARD) 2.39% 12 feed experiments. Compared it demonstrated more soluble decrease from 9.50 cP 1.89 averaged 1490 1160 addition 16.02 mol% mixture.

Language: Английский

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