Operators' Group, Rig Contractors, and OEM/Service Company Work to Solve Rig Data Quality Issues
Notice bibliographique
Résumé
Abstract In the current economic climate Operators must reduce drilling costs, so they are turning to well data analytics, real-time advisory, and automation systems to make sustainable improvements (Behounek et al. 2017). Rig surface sensor data is critical to improvement; however, documented issues with consistent, reliable, quality data complicates and delays the value from these systems. The Operators Group for Data Quality (OGDQ) seeks to accelerate the adoption of standardized key measurement specifications, data storage, transmission, transformation, and integration by working with Rig Contractors, Original Equipment Manufacturers (OEMs), and Service Companies. The OGDQ effort focuses on key measurements used for important drilling process decision making. For this paper, the OGDQ worked with Rig Contractors and an OEM/Service Company to advance recommended data quality components in work processes and commercial agreements. By bringing transparency to the process, the authors hope to contribute to the efforts to address operational data quality issues and to drive alignment and improvements among Operators, Rig Contractors, OEMs, and Service Companies. This paper outlines an approach to putting data quality into practice, including initially identifying the problem, field verification, developing key measurement specifications, constructing framework components, and anticipating management of change issues. Quality drilling data is essential to both rig and office personnel who are tasked with decision making for fast-paced well programs. Quality drilling data is also essential for the data-driven systems developed to assist in managing well delivery. Rig studies show several cases where Operators independently uncovered systematic errors for 10 key measurements used for drilling process decision making (Zenero 2014; Zenero et al. 2016). The 10 key measurements are listed as follows: Rotary/Top Drive TorqueJoint Makeup/Breakout TorqueHookloadRotary/Top Drive Rotational SpeedStand Pipe PressureDrilling Fluid Pump RateDrilling Fluid Tank/Pit VolumeDrilling Fluid DensityDrilling Fluid ViscosityBlock Position Widespread agreement on data quality practices among Operators, Rig Contractors, OEMs, and Service Companies is crucial for their quick adoption, and an industry-wide approach has a profound effect on drilling operations. Widely adopted practices will support and drive requirements for sensor quality, calibration, field verification, and maintenance. This standardization will, in turn, significantly enable improved drilling operations, drilling analysis, and big data processing by correcting many errors resulting from poor data quality. This paper outlines the methodology used to develop a guide for commercial drilling components, and illustrates the application of this guide with selected drilling data use cases.
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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 | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».