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Enregistrement W1977918872 · doi:10.2118/84442-ms

Management of the Well Construction Process Using an Intranet-Based Learnings System

2003· article· en· W1977918872 sur OpenAlexaff
Eric Diggins, Brad Muir, Ronald K. Bell

Notice bibliographique

RevueSPE Annual Technical Conference and Exhibition · 2003
Typearticle
Langueen
DomaineEngineering
ThématiqueDrilling and Well Engineering
Établissements canadiensNexen (Canada)
Organismes subventionnairesnon disponible
Mots-clésStandardizationProcess managementBusinessProcess (computing)IntranetWorkforceOperations managementControl (management)Knowledge managementEngineering managementEngineeringComputer science

Résumé

récupéré en direct d'OpenAlex

Abstract Nexen is a mid-sized independent E&P operator with operations worldwide. Not unlike many similar sized companies, Nexen had relatively few Drilling and Completions systems and processes in place prior to 2000 and relied heavily on the knowledge and experience of staff and contractors. Given the increasing level of risk exposure around Nexen Well Construction activities and the consequent impact on total corporate capital expenditure, the need was seen to develop a system to enable standardization and management of policies, procedures and guidelines as they pertained to Well Construction. Key areas of concentration in the initial stages of management system development included the typical critical tasks as follows: Health, Safety and Environment processes / standards Well design, planning, integrity and life cycle operations. Contract / contractor management Cost control and the business process including organization It was also recognized that one of the shortcomings of many organizations, particularly ones in which activities are spread over wide geographical areas, was the capture and application of project learnings, good and bad, and incorporation of same into future projects. The system therefore needed to incorporate this facility in some fashion. The retention of learnings or knowledge was also related somewhat to the high proportion of consultants in the team and the relative transient nature of the workforce in this respect - the lessons learned were, at times, "walking out the door" with people. Further, the demographics of the team highlighted a very real concern and it was recognized that Nexen would have to be in a position to attract, develop and retain a younger workforce if we were to be successful going forward in addressing the Company growth plans. The system needed also to facilitate the career development and training of new grads / young engineers providing some structure and guidance that was absent. The following two areas were subsequently developed to complete the perceived needs of the Nexen drilling team. Key Learnings capture & continuous improvement. Staff development. Due to the diverse range of operations and geographical areas in which this information was to be applied and to ensure that the most up-to-date information was being provided in a manner simplifying the issues around document control, it was crucial to have this information available on a real-time basis. It was decided that a web-based system met these objectives in the most efficient and cost-effective manner. The final key was the development of a process to assure that personnel (both staff and consultant) had a comprehensive understanding / knowledge of critical information, contained within the management system, related to their job function. To address this need, a knowledge assessment element was utilized. By completing an on-line induction process, the individual, as well as Nexen Drilling Management, could have the confidence that he or she understood the critical processes by which Nexen manages its Well Construction function. The Nexen Well Management System (WMS), a collaborative effort between Nexen Petroleum International, TTG Systems, OGCI and Advanced Well Technologies (AWT) is the result of these efforts to address the team's requirements and objectives.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,190
Score d'incertitude au seuil0,340

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,217
Écart entre enseignants0,206 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2003
Routes d'admission1
Résumé présentoui

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