Notice bibliographique
Résumé
Technology Focus Production engineers are often tasked with analyzing existing data to try to understand the factors affecting equipment failures or subpar performance, to guide the implementation of actions geared toward mitigating such problems and improving overall operations. Sometimes, this can become quite a difficult task. One reason is that the existing data can be of bad quality, containing records that are incomplete (missing critical information), inconsistent (with information incompatible with other information in the same record or in other records), or inaccurate (because of errors in measuring or recording the values for the parameters). Sorting through such issues with the data can sometimes be very time consuming. It often is not entirely successful; and it is never fun. Another reason is that evaluating the effect of each possible influential factor is usually more complex than we first think. Early on in my career, I came across a quote, attributed to Sir Cyril Hinshelwood, in a book about data reduction and analysis, which I share often with my younger colleagues. It describes the normal stages of developing a theory on the basis of existing data: The first stage usually involves “gross simplifications, reflecting partly the need for practical views and even more a too-enthusiastic aspiration for the elegance of form.” In the second stage, “the symmetry of the hypothetical system is distorted and the neatness marred as recalcitrant facts increasingly rebel against uniformity.” In the third stage, “if and when it is obtained, a new order emerges, more intricately contrived, less obvious, and with its parts subtly interwoven, since it is of nature’s and not of man’s conception.” Putting the puzzle together is the fun part. Some of the papers in this feature illustrate how tough production challenges can be tackled on the basis of thorough analysis and interpretation of good-quality data. Collecting such good-quality data has a cost. If collecting the data is worth the effort because of the value that we can extract from the information it contains, then implementing measures to ensure its quality should also be warranted. It will allow us to spend our time in the most rewarding (and entertaining) part of the task, which is coming up with a good theory for the trends we can see in the data. Recommended additional reading at OnePetro: www.onepetro.org. SPE 153005 An Exhaustive Study of Scaling in the Canadian Bakken: Failure Mechanisms and Innovative Mitigation Strategies From More Than 400 Wells by Jonathan J. Wylde, Clariant Oil Services, et al. SPE 159385 Paraffin-Deposition Analysis for Crude Oils Under Turbulent-Flow Conditions by Hamidreza Karami Mirazizi, The University of Tulsa, et al. SPE 153224 Coiled-Tubing Sand Cleanout at Low-Bottomhole-Pressure, Large-Diameter-Casing, and Long-Horizontal-Well Applications in Deepwater West Seno Field by J.B. Putra Koesnihadi, Chevron, et al. OTC 23622 Treating and Releasing Produced Water at the Ultradeepwater Seabed by Timothy P. Daigle, Fluor Offshore Solutions, 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».