Matches in SemOpenAlex for { <https://semopenalex.org/work/W4221004038> ?p ?o ?g. }
- W4221004038 endingPage "118985" @default.
- W4221004038 startingPage "118985" @default.
- W4221004038 abstract "Carbon dioxide storage in underground saline aquifers is considered a promising technique for decreasing atmospheric CO2 emissions. The CO2 residual and solubility in deep saline aquifers are crucial processes for improving CO2 storage security. In this study, the trapping efficiency of CO2 sequestration in saline formations was predicted by developing three supervised machine learning (ML)-based models: random forest (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR). A diverse field-scale simulation database of 1509 samples were collected from the literature, and the proposed models were examined for training and testing. To verify the prediction accuracy of the three ML models, the prediction results were analysed and compared using graphical and statistical indicators. From the prediction results, the proposed ML models were ranked based on their accuracy: XGBoost > RF > SVR. The XGBoost-based predictive model achieved an extremely low root mean square error (RMSE = 0.0041) and high correlation factor (R2 = 0.9993) for both residual and solubility trapping efficiency. However, RF and SVR exhibited RMSEs of 0.0243 and 0.074 and R2 values of 0.9781 and 0.9284, respectively. Furthermore, the applicability of the XGBoost model was validated and only 15 suspected data points were detected across the entire database. Therefore, the proposed model can be a valuable and viable template for predicting the CO2 trapping index in other saline formations worldwide. Utimately, the XGBoost model has been tested against reservoir simulation models in comprehensive blind testing and may be used as a robust screening and process planning tool for the uncertainty assessment of carbon storage projects." @default.
- W4221004038 created "2022-04-03" @default.
- W4221004038 creator A5005583322 @default.
- W4221004038 creator A5027047123 @default.
- W4221004038 creator A5027411074 @default.
- W4221004038 creator A5062041023 @default.
- W4221004038 date "2022-05-01" @default.
- W4221004038 modified "2023-10-16" @default.
- W4221004038 title "Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers" @default.
- W4221004038 cites W1584236903 @default.
- W4221004038 cites W1843259518 @default.
- W4221004038 cites W1967497314 @default.
- W4221004038 cites W1970567530 @default.
- W4221004038 cites W1983449038 @default.
- W4221004038 cites W1984981661 @default.
- W4221004038 cites W2008553611 @default.
- W4221004038 cites W2021230538 @default.
- W4221004038 cites W2029439840 @default.
- W4221004038 cites W2032247692 @default.
- W4221004038 cites W2042410044 @default.
- W4221004038 cites W2050277054 @default.
- W4221004038 cites W2055232176 @default.
- W4221004038 cites W2072242747 @default.
- W4221004038 cites W2073108784 @default.
- W4221004038 cites W2080975874 @default.
- W4221004038 cites W2085175545 @default.
- W4221004038 cites W2090198342 @default.
- W4221004038 cites W2102636708 @default.
- W4221004038 cites W2110298999 @default.
- W4221004038 cites W2125104283 @default.
- W4221004038 cites W2156157987 @default.
- W4221004038 cites W2177926054 @default.
- W4221004038 cites W2238044020 @default.
- W4221004038 cites W2256184401 @default.
- W4221004038 cites W2305516179 @default.
- W4221004038 cites W2325348676 @default.
- W4221004038 cites W2466359547 @default.
- W4221004038 cites W2530018583 @default.
- W4221004038 cites W2530085706 @default.
- W4221004038 cites W2579660559 @default.
- W4221004038 cites W2618340465 @default.
- W4221004038 cites W2626176756 @default.
- W4221004038 cites W2732153156 @default.
- W4221004038 cites W2736933663 @default.
- W4221004038 cites W2750346591 @default.
- W4221004038 cites W2751436407 @default.
- W4221004038 cites W2759565175 @default.
- W4221004038 cites W2768141164 @default.
- W4221004038 cites W2768272593 @default.
- W4221004038 cites W2791611621 @default.
- W4221004038 cites W2793472625 @default.
- W4221004038 cites W2795411881 @default.
- W4221004038 cites W2803396148 @default.
- W4221004038 cites W2804571714 @default.
- W4221004038 cites W2806863484 @default.
- W4221004038 cites W2884332071 @default.
- W4221004038 cites W2888812182 @default.
- W4221004038 cites W2889337219 @default.
- W4221004038 cites W2896471956 @default.
- W4221004038 cites W2897229585 @default.
- W4221004038 cites W2899707954 @default.
- W4221004038 cites W2901004617 @default.
- W4221004038 cites W2906252920 @default.
- W4221004038 cites W2911142753 @default.
- W4221004038 cites W2911964244 @default.
- W4221004038 cites W2937300184 @default.
- W4221004038 cites W2941431020 @default.
- W4221004038 cites W2946296542 @default.
- W4221004038 cites W2950630445 @default.
- W4221004038 cites W2960121199 @default.
- W4221004038 cites W2966935623 @default.
- W4221004038 cites W2967665696 @default.
- W4221004038 cites W2972085911 @default.
- W4221004038 cites W2981685989 @default.
- W4221004038 cites W2981802428 @default.
- W4221004038 cites W2982014245 @default.
- W4221004038 cites W3006628032 @default.
- W4221004038 cites W3027340277 @default.
- W4221004038 cites W3032477746 @default.
- W4221004038 cites W3040112566 @default.
- W4221004038 cites W3043270278 @default.
- W4221004038 cites W3046031108 @default.
- W4221004038 cites W3050185872 @default.
- W4221004038 cites W3082511992 @default.
- W4221004038 cites W3091838433 @default.
- W4221004038 cites W3092050062 @default.
- W4221004038 cites W3093766507 @default.
- W4221004038 cites W3102476541 @default.
- W4221004038 cites W3120147074 @default.
- W4221004038 cites W3138648392 @default.
- W4221004038 cites W3146336484 @default.
- W4221004038 cites W3153601476 @default.
- W4221004038 cites W3154048915 @default.
- W4221004038 cites W3192152888 @default.
- W4221004038 cites W3195820802 @default.
- W4221004038 cites W3197082922 @default.
- W4221004038 cites W3202914192 @default.
- W4221004038 cites W3213417474 @default.