Modeling and Optimization of Asphaltene Deposition in Porous Media Using Genetic Algorithm Technique
Notice bibliographique
Résumé
Modeling and Optimization of Asphaltene Deposition in Porous Media Using Genetic Algorithm Technique V.. Hematfar; V.. Hematfar Petroleum University of Technology Search for other works by this author on: This Site Google Scholar R.. Kharrat; R.. Kharrat Petroleum University of Technology Search for other works by this author on: This Site Google Scholar M. H. Ghazanfari; M. H. Ghazanfari Sharif University of Technology Search for other works by this author on: This Site Google Scholar M. B. Bagheri M. B. Bagheri Sharif University of Technology Search for other works by this author on: This Site Google Scholar Paper presented at the International Oil and Gas Conference and Exhibition in China, Beijing, China, June 2010. Paper Number: SPE-130455-MS https://doi.org/10.2118/130455-MS Published: June 08 2010 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Hematfar, V.. , Kharrat, R.. , Ghazanfari, M. H., and M. B. Bagheri. "Modeling and Optimization of Asphaltene Deposition in Porous Media Using Genetic Algorithm Technique." Paper presented at the International Oil and Gas Conference and Exhibition in China, Beijing, China, June 2010. doi: https://doi.org/10.2118/130455-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Oil and Gas Conference and Exhibition in China Search Advanced Search Abstract Different models have been proposed for deposition of asphaltene on reservoir rocks that due to complexity of asphaltene nature, most of them have not been productive. Here, a reliable model is proposed which despite of previous models, considers the change in asphaltene saturation in the core. The obtained experimental data in the laboratory was used for model validation. In this work, a series of core flooding tests was carried out in presence of connate water at different solvent-oil volume ratios. Pressure drop was measured at three different terminals along the core. The obtained experimental data as well as mass balance equations, momentum equation, asphaltene deposition and permeability reduction models were employed in an iterative scheme to simulate the deposition process. Genetic algorithm (GA), which is a powerful tool, was applied for history matching, optimization and determination of the model parameters. Well match observed between the model results and experimental data confirms the accuracy of the proposed mathematical model of asphaltene deposition in porous media. Also, applied improvement on the model resulted in accurate simulation as well as determination of precipitated asphaltene saturation. Optimization shows that all deposition mechanisms, surface deposition, entrainment and pore plugging, are dominant during the permeability evolution process. Results of this work can be helpful for reliable simulation of the dynamic asphaltene deposition process during different production schemes. Keywords: asphaltene deposition, model parameter, production chemistry, artificial intelligence, hydrate remediation, scale inhibition, remediation of hydrates, paraffin remediation, optimization problem, machine learning Subjects: Production Chemistry, Metallurgy and Biology, Information Management and Systems, Inhibition and remediation of hydrates, scale, paraffin / wax and asphaltene Copyright 2010, Society of Petroleum Engineers You can access this article if you purchase or spend a download.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».