Matches in SemOpenAlex for { <https://semopenalex.org/work/W4311147035> ?p ?o ?g. }
- W4311147035 endingPage "105687" @default.
- W4311147035 startingPage "105687" @default.
- W4311147035 abstract "Carbon dioxide enhanced oil recovery (CO2-EOR) is a promising application for carbon capture, utilization and storage (CCUS). Accurate modeling of CO2-oil minimum miscible pressure (MMP) is crucial for CO2-EOR projects. In this study, a knowledge-guided framework for an extreme gradient boosting machine (XGBoost) and interpretable tabular learning architecture (TabNet), called KXGB and KTabNet, respectively, are developed to model the MMP. The proposed models are strengthened using a large MMP database of 421 samples collected from literature. Domain knowledge is integrated into intelligent models to prevent data-driven models from producing predictions that violated the domain knowledge. The Shapley Additive Explanations (SHAP) method is used to explain the proposed model to ensure the credibility of petroleum engineers. To further verify the model’s effectiveness, the same experimental strategy is employed to compare the proposed models with existing machine-learning (ML) methods. The results show that KXGB is the most recommended superior solution for modeling MMP owing to its outstanding performance and simplicity of optimization. The correlation coefficient, root mean square error, and mean absolute error are 0.9833, 0.7637 and 0.55, respectively. However, KTabNet has great potential. Although its accuracy is slightly lower than that of the former, its strong representation ability and decision transparency may be favorable for future research. This study also demonstrate that the proposed framework conforms to certain theoretical rules and has a reasonable domain of applicability. To the best of our knowledge, this is the first study on integration of domain knowledge into MMP modeling methods. The experience and insights obtained from this study can guide CO2-EOR projects and other tabular data modeling in the oil and gas industry." @default.
- W4311147035 created "2022-12-23" @default.
- W4311147035 creator A5002149217 @default.
- W4311147035 creator A5019954912 @default.
- W4311147035 creator A5020514234 @default.
- W4311147035 creator A5022499603 @default.
- W4311147035 creator A5048678445 @default.
- W4311147035 creator A5065912071 @default.
- W4311147035 creator A5073196579 @default.
- W4311147035 date "2023-02-01" @default.
- W4311147035 modified "2023-09-27" @default.
- W4311147035 title "Interpretable knowledge-guided framework for modeling minimum miscible pressure of CO2-oil system in CO2-EOR projects" @default.
- W4311147035 cites W1905949925 @default.
- W4311147035 cites W1933450155 @default.
- W4311147035 cites W1980994638 @default.
- W4311147035 cites W1996324466 @default.
- W4311147035 cites W2010044158 @default.
- W4311147035 cites W2021607192 @default.
- W4311147035 cites W2043308012 @default.
- W4311147035 cites W2054716083 @default.
- W4311147035 cites W2071735680 @default.
- W4311147035 cites W2074435123 @default.
- W4311147035 cites W2079597494 @default.
- W4311147035 cites W2151554678 @default.
- W4311147035 cites W2336578674 @default.
- W4311147035 cites W2618598738 @default.
- W4311147035 cites W2791718738 @default.
- W4311147035 cites W2891005831 @default.
- W4311147035 cites W2908162830 @default.
- W4311147035 cites W2950630445 @default.
- W4311147035 cites W2973817809 @default.
- W4311147035 cites W2978693217 @default.
- W4311147035 cites W2981731882 @default.
- W4311147035 cites W2996599990 @default.
- W4311147035 cites W3001193759 @default.
- W4311147035 cites W3006689658 @default.
- W4311147035 cites W3012450703 @default.
- W4311147035 cites W3028968917 @default.
- W4311147035 cites W3033983598 @default.
- W4311147035 cites W3035203116 @default.
- W4311147035 cites W3040626233 @default.
- W4311147035 cites W3045004532 @default.
- W4311147035 cites W3084992777 @default.
- W4311147035 cites W3112778626 @default.
- W4311147035 cites W3117891459 @default.
- W4311147035 cites W3124626644 @default.
- W4311147035 cites W3134786806 @default.
- W4311147035 cites W3152698385 @default.
- W4311147035 cites W3159243351 @default.
- W4311147035 cites W3212040056 @default.
- W4311147035 cites W3216660278 @default.
- W4311147035 cites W4220734118 @default.
- W4311147035 cites W4221004038 @default.
- W4311147035 cites W4224254510 @default.
- W4311147035 cites W4225826617 @default.
- W4311147035 cites W4229374064 @default.
- W4311147035 cites W4281400158 @default.
- W4311147035 cites W4283258523 @default.
- W4311147035 cites W4283700439 @default.
- W4311147035 cites W4288032641 @default.
- W4311147035 cites W4292399649 @default.
- W4311147035 cites W4293495652 @default.
- W4311147035 cites W4302306785 @default.
- W4311147035 doi "https://doi.org/10.1016/j.engappai.2022.105687" @default.
- W4311147035 hasPublicationYear "2023" @default.
- W4311147035 type Work @default.
- W4311147035 citedByCount "0" @default.
- W4311147035 crossrefType "journal-article" @default.
- W4311147035 hasAuthorship W4311147035A5002149217 @default.
- W4311147035 hasAuthorship W4311147035A5019954912 @default.
- W4311147035 hasAuthorship W4311147035A5020514234 @default.
- W4311147035 hasAuthorship W4311147035A5022499603 @default.
- W4311147035 hasAuthorship W4311147035A5048678445 @default.
- W4311147035 hasAuthorship W4311147035A5065912071 @default.
- W4311147035 hasAuthorship W4311147035A5073196579 @default.
- W4311147035 hasConcept C105795698 @default.
- W4311147035 hasConcept C119857082 @default.
- W4311147035 hasConcept C124101348 @default.
- W4311147035 hasConcept C127413603 @default.
- W4311147035 hasConcept C139945424 @default.
- W4311147035 hasConcept C154945302 @default.
- W4311147035 hasConcept C169258074 @default.
- W4311147035 hasConcept C17744445 @default.
- W4311147035 hasConcept C199539241 @default.
- W4311147035 hasConcept C207685749 @default.
- W4311147035 hasConcept C2779681308 @default.
- W4311147035 hasConcept C2780224610 @default.
- W4311147035 hasConcept C33923547 @default.
- W4311147035 hasConcept C41008148 @default.
- W4311147035 hasConcept C70153297 @default.
- W4311147035 hasConcept C78762247 @default.
- W4311147035 hasConceptScore W4311147035C105795698 @default.
- W4311147035 hasConceptScore W4311147035C119857082 @default.
- W4311147035 hasConceptScore W4311147035C124101348 @default.
- W4311147035 hasConceptScore W4311147035C127413603 @default.
- W4311147035 hasConceptScore W4311147035C139945424 @default.
- W4311147035 hasConceptScore W4311147035C154945302 @default.
- W4311147035 hasConceptScore W4311147035C169258074 @default.