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Enregistrement W2783509403 · doi:10.2118/189423-ms

New Approach: Confirming Well Quality at Well Delivery with Stringent Well Integrity Checks at a World Class Drilling Project

2018· article· en· W2783509403 sur OpenAlexaff
Stephen Butt

Notice bibliographique

RevueSPE/IADC Middle East Drilling Technology Conference and Exhibition · 2018
Typearticle
Langueen
DomaineEngineering
ThématiqueDrilling and Well Engineering
Établissements canadiensMemorial University of Newfoundland
Organismes subventionnairesnon disponible
Mots-clésPerformance indicatorQuality (philosophy)Computer scienceScale (ratio)Set (abstract data type)DrillingEngineeringBusiness

Résumé

récupéré en direct d'OpenAlex

Abstract Drilling a well is comprised of multiple activities which are linked to the well objectives and requirements set in the design phase. Some of the activities have short term impacts on the well such as logging a section etc., and some of the activities have long term impacts on the well such as cementing, wellbore accessibility etc. It is quite important to list the activities based on their impact on a well and rate them individually to get the overall impact on the objectives of a well by these activities. Conventionally a well quality score was reported 6-12 months after a well was completed. The quality cycle to improve the performance of a well became ineffective and irrelevant due to late reporting. The results of the activities of a completed well were so late that many wells had been drilled and completed during the reported period. First, this major flow turned the existing Well Quality KPIs into laggard KPIs, which were not contributing to enhancing the Quality of a delivered well. Second, the well quality score was distributed among four different categories where Well Integrity was an isolated category, and a well integrity issue has minimum impact on overall well quality scoring. Third, the scoring guidelines were very generic and were depended on the evaluator judgment. A lack of verification of the results was also evident during KPI reporting, which made the KPIs score skeptical and unreliable. Fourth a fixed scoring structure was used to evaluate all type of wells at the same scale. Such as the scoring of a complex well was treated the same manner as a scoring on a workover well. Last, some activities were ignored in the well quality scoring such as Coring Quality, minimum Well Integrity requirements etc. The overall score does not represent the actual picture of a well using existing Well Quality KPIs, which was impacting the overall project quality score. A new approach was adapted to capture the well quality score right after a well is delivered so that improvement ideas can be implemented in the current drilling wells in the execution phase and coming wells in the design phase without any delays. The quality cycle was improved resulting in shorter well duration with lesser well integrity issues. A new weightage system was introduced to capture all activities in a well, where these activities are evaluated individually. Scoring criteria for each activity is defined clearly. Based on deviation from the planned activity, the actual score is recorded accordingly by the user. Later these activities are verified by the end users, so verification is enhancing the trust as well the validity of a lesson learned. Users and end users are connected at an early stage after a well completed to capture the feedback. Improvements get quickly implemented as the quality cycle is short and quick. The new scoring method introduced a wide range of Well Integrity checks based on rigorous and clear guidelines, where failure to meet key well integrity policies can result in nulling the overall score of a well. New well quality scoring guidelines provide a clear and efficient approach to score the key performance indicators of a well at the right time. Consistency in scoring, timely reporting and right weightage for well quality scoring results in high quality well programs, application of fit-for purpose technologies and better knowledge transfer among team members.

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 candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,452
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
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 Opus0,051
Tête enseignante GPT0,239
Écart entre enseignants0,188 · 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.

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

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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