Notice bibliographique
Résumé
Technology Focus Brant Bennion, in the Journal of Canadian Petroleum Technology Distinguished Authors series, titled his 1999 article on formation damage “The Impairment of the Invisible by the Inevitable and Uncontrollable, Resulting in an Indeterminate Reduction in the Unquantifiable.” This is a brilliant definition of formation damage because it reflects very well the lack of relevant data (in particular, permeability data) that are essential for adequate design of drilling and completion fluids. In addition, the opening sentence in Bennion’s article is as relevant today as it was in 1999: “Formation damage is a hot topic these days—with justifiable reason as we move to the exploitation of more challenging oil and gas reservoirs in tighter, deeper, and more depleted conditions.” In order to avoid some of the detrimental effects of formation damage, a key aspect is laboratory testing of representative core material under representative downhole conditions. Thin sections, dry scanning electron microscopy (SEM), cryogenic SEM, and X-ray diffraction have been used for a number of years to identify the main damage mechanisms, mechanical or liquid, contributing to the observed returned permeability measurement from coreflooding. High-resolution images can be obtained from dry and cryogenic SEM, but they offer only a limited view at any one time. One technique that has been applied recently to identify and quantify potential formation damage is that of microcomputed tomography (CT). This provides high-resolution scans of whole plugs and allows, for example, the identification of changes in pore structure because of fines mobilization, and the visualization of the filter cake after cleanup, depth of mud solids, and filtrate invasion. The combination of micro- CT with techniques previously used for formation-damage analysis is providing new understandings in the interaction of drilling and completion fluids with core material. One of the more frustrating aspects of coreflooding has been how to relate the results obtained to potential well-inflow performance. Computational fluid dynamics is one technique that appears to have bridged this gap. This technique incorporates the data obtained from coreflooding into a model that recreates the actual well geometry, depth of formation damage, mud thickness, and distribution of restrictions in the tubing, such as safety valves. With this approach, production rates can be obtained that provide useful insights into selecting appropriate drilling and completion fluids, for example. Enjoy the papers selected. Formation damage is still a “hot topic.” JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 165092 Case-Study Analysis of Formation Damage Induced by Brine Workover Fluid on Burcioaia Reservoir (Romania) and Research on Damage- Removal Methods by A. Dragomir, OMV Petrom, et al. SPE 165169 Formation Damage and the Importance of a Rigorous Diagnostic: A Case History in Nigerian Deep Water by Jean-Noel Furgier, Total, et al. SPE 169435 Integrated Analysis To Identify and Prevent Formation Damage Caused by Completion Brines: A Colombian Field Application by M.G. Jaimes, Ecopetrol, et al.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,080 | 0,032 |
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 ».