Notice bibliographique
Résumé
Guest editorial Over the past decade, well technology has advanced exponentially. Horizontal wells, multiple completions, and wells that cycle between steam injection and heavy oil production have superseded the traditional vertical well with one completion zone. Advances in the way the oil and gas sector describes and identifies well data elements have not matched the growth in well technology, however. Even the way the sector defines basic well concepts, such as wellbores, completions, and depths, can vary between companies and regulatory jurisdictions. Several factors have contributed to that inconsistency. The foundation for regulations and well technology dates back more than a century: many terminologies arose when the petroleum sector was in its infancy. Drilling technology has changed dramatically in the past 10 years, yet the regulatory descriptions have remained relatively static. Part of the reason is the lack of international standards; for many years, guidance was offered by the American Petroleum Institute in the US, but lapsed in the 1980s. The myriad of modern drilling techniques also add to the confusion. Start with a simple producing well, which is wellbore WB1 (Fig. 1). Later, when the operator deepens the well (WB2) and completes new formations (WB2-C1 and WB2-C2), there is some disagreement about whether the deepened segment is part of the same wellbore, or a new wellbore. Of those who think it is a new wellbore, some measure the new wellbore from the surface to the new bottom hole, and some think the new wellbore is only the newly drilled portion. Many modern wells have far more complex production histories. In Fig. 2, the operator has drilled a pilot well, WB1, with two lateral segments, WB2 and WB3, which are then completed in separate reservoirs. Knowing which production exists in which wellbores becomes far more important; an engineering decision made on incorrect or incomplete information has the potential to create significant repercussions. In Search of a Solution The Professional Petroleum Data Management Association (PPDM), based in Calgary, has been working to resolve the issue. PPDM was formed in 1989 and incorporated in 1991. It is a not-for-profit organization that provides data management expertise for the petroleum exploration and production industry. The membership is made up of more than 150 petroleum companies, government agencies, software application providers, data vendors, and service companies. The petroleum industry generates a large amount of data, including exploration seismic, well drilling, and oil and gas production information. PPDM originally set out to develop an open data model that would allow the industry to reduce costs and allow companies to become more productive, efficient, and competitive. Over the past two decades, industry professionals working as volunteers with PPDM have created a comprehensive data model that benefits a wide array of functions within an oil company, such as well information, surface and mineral rights management, well activities and operations, equipment and facilities management, seismic information, and production and reserves data.
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 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,056 | 0,029 |
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 ».