A review of intelligent decision-making strategy for geological CO2 storage: Insights from reservoir engineering

Mao, Jinjie; Jahanbani Ghahfarokhi, Ashkan

doi:10.1016/j.geoen.2024.212951

Cited by 4 publications

(3 citation statements)

References 192 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples of this category include the studies published by Dehaghani and Soleimani, 76 Alatefi et al, 77 Olofinnika et al, 78 and Al-Khafaji et al 79 Among the prior papers, the recent paper of Alatefi et al 77 is the richest one as it summarizes many , 80 Ng et al, 70 Song et al, 81 and Mao and Jahanbani Ghahfarokhi. 82 • A part of a chapter in books devoted to the application of AI techniques in the petroleum industry or gas injection methods, such chapter four 74 of the book published by Hemmati-Sarapardeh et al 71 and the chapter written by Hadavimoghaddam et al 83 Due to the limited number of reviewed papers in the prior works, our current study provides a comprehensive examination of the application of AI techniques for modeling the MMP of CO 2 -oil systems. By reviewing approximately 80 studies in this area, we shed light on many previously unexplored aspects.…”

Section: Previous Workmentioning

confidence: 99%

“…Examples of this category include the studies published by Dehaghani and Soleimani, Alatefi et al., Olofinnika et al, and Al-Khafaji et al Among the prior papers, the recent paper of Alatefi et al is the richest one as it summarizes many previous studies on the application of AI techniques for predicting the MMP of CO 2 – oil systems. A special section in review papers related to CCSU or the application of AI techniques in the oil and gas industry: some recent review papers included a special section or certain paragraphs devoted to discussing the implementation of AI techniques for modeling the MMP of the systems CO 2 – oil. Examples of this category include the review papers published by Du et al, Ng et al, Song et al, and Mao and Jahanbani Ghahfarokhi A part of a chapter in books devoted to the application of AI techniques in the petroleum industry or gas injection methods, such chapter four of the book published by Hemmati-Sarapardeh et al and the chapter written by Hadavimoghaddam et al …”

Section: Previous Workmentioning

confidence: 99%

“…A special section in review papers related to CCSU or the application of AI techniques in the oil and gas industry: some recent review papers included a special section or certain paragraphs devoted to discussing the implementation of AI techniques for modeling the MMP of the systems CO 2 – oil. Examples of this category include the review papers published by Du et al, Ng et al, Song et al, and Mao and Jahanbani Ghahfarokhi …”

Section: Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Amar,

Djema,

Ourabah

et al. 2024

Energy Fuels

View full text Add to dashboard Cite

The carbon dioxide (CO 2 ) based enhanced oil recovery methods (EORs) are considered among the promising techniques for increasing the recovery factor from mature oil reservoirs and reducing the amount of CO 2 emission in the atmosphere. Determining the minimum miscibility pressure (MMP) of CO 2 − oil systems is a crucial step for successfully implementing CO 2 − EOR processes. Therefore, various approaches have been proposed for determining this key parameter. However, the laboratory tests are expensive and timeconsuming, while most available correlations present moderate accuracy. To address these shortcomings, various studies have applied artificial intelligence (AI) techniques to model the MMP of the CO 2 − oil systems. In this study, we reviewed the published works for predicting the MMP of the CO 2 − oil systems using AI-based models. Our analyses revealed the robustness of these techniques in modeling MMP. In this context, it was noticed that more than 70 AI-based paradigms have been utilized for estimating the MMP of CO 2 − oil systems. Among the applications, the hybrid schemes combining machine learning and nature-inspired algorithms (ML-NIA) take the top spot, accounting for 27% of applications. Additionally, the investigation demonstrated that the artificial neural network (ANN) is the most utilized ML method in the modeling phase, while the genetic algorithm (GA) is the most widely applied NIA for improving ML performance. In the second part of this study, we suggest an updated correlation based on gene expression programming (GEP) for the accurate prediction of MMP of CO 2 − oil systems. Our proposed explicit correlation yielded excellent predictive performance, achieving overall root-mean-square error and determination coefficient (R 2 ) values of 0.9253 and 09713, respectively. These promising statistical metrics enabled the newly updated GEP-based correlation to outperform the preexisting models. Besides, the physical validity and interpretability of the proposed correlation were proven using the trend analysis and the Shape Dependence Analysis plot, respectively. Lastly, the findings of this study provide a dual benefit, the review and the illustration of the published studies on applying AI techniques for modeling the MMP of the CO 2 − oil systems; and second, the newly implemented GEP-based correlation can significantly enhance the effective prediction of the MMP in CO 2 -oil systems, thus, facilitating the simulation of various CO 2 − based EOR processes.

show abstract

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Amar,

Djema,

Ourabah

et al. 2024

Energy Fuels

View full text Add to dashboard Cite

show abstract

A review on carbon dioxide sequestration potentiality in basaltic rocks: Experiments, simulations, and pilot tests applications

Mwakipunda,

Yu,

Komba

et al. 2024

Geoenergy Science and Engineering

View full text Add to dashboard Cite

Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models

Amiri,

Jahanbani Ghahfarokhi,

Rocca

et al. 2024

Algorithms

View full text Add to dashboard Cite

Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. The present study focuses on CO2 storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO2 saturation and pressure over all grid blocks for 50 years. 18 million samples and 31 features, including reservoir static and dynamic properties, build the input data. Models comprise 3–5 hidden layers with 128–512 units apiece. SRMs showed a runtime improvement of 300 times and an accuracy of 99% compared to the 3D numerical simulator. The genetic algorithm was then employed to determine the optimal rate and duration of CO2 injection, which maximizes the volume of injected CO2 while ensuring storage operations’ safety through constraints. The optimization continued for the reproduction of 100 generations, each containing 100 individuals, without any hyperparameter tuning. Finally, the optimization results confirm the significant potential of Smeaheia for storing 170 Mt CO2.

show abstract

A review of intelligent decision-making strategy for geological CO2 storage: Insights from reservoir engineering

Cited by 4 publications

References 192 publications

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

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

A review on carbon dioxide sequestration potentiality in basaltic rocks: Experiments, simulations, and pilot tests applications

Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models

Contact Info

Product

Resources

About