Notice bibliographique
Résumé
Abstract An effective chemical treatment program should reduce the rate of well failures while enhancing well productivity and minimizing cost. The unique challenges to achieving these goals include the variation in the downhole conditions, fluid composition, completion types and number of wells. The execution of chemical programs often relies on third party vendors with a vast resource base and proven technology. These traits, however, must be coupled with knowledge of the well history and proper oversight from operations, facilities, and production engineering groups. Managing and effectively utilizing collected data helps to move from "blanket type" chemical programs to a more targeted well-by-well approach. This work uncovers several opportunities for improvement in already established chemical programs. It is especially beneficial for the onshore fields which are challenged with hundreds or even thousands of wells. The systematic improvement strategy applied in this study began by assessing existing data for identification of scaling, corrosion, and organic deposition problems. This allowed the Local Chemical Management Team (LCMT) to reveal gaps in the information needed for a more comprehensive understanding of formation damage and flow assurance issues. Proper identification of controlling damage type per formation and area of the field enabled redesigning of well completions, testing of water compatibilities for fracture stimulation, and customization of acid treatments with improved acid placement and production uplift. Early attempts to collect fluid samples and integrate water, gas and solid analysis in the scale prediction modeling software revealed the critical nature of quality checks in sampling procedures, field tests, and lab reports. The lab audits and chemical program data management led to reorganization of the database structure and improvement in measuring and reporting of the results to the LCMT. The value of information gained by laboratory tests offered by the chemical provider was assessed in current field settings and communicated to engineering and operations personnel. A better understanding of organic deposits also supplemented wellbore and sand face clean outs. Inclusion of a flow assurance focus in an established corrosion and well failure prevention program increased well productivity and decreased operating cost per well. Improvements in the database structure included utilization of historical data for treatment optimization, integration of oil, water, and gas with quantitative solids analysis, and the establishment of key performance metrics and reporting procedures. Customization of remedial treatments per zone and geographic location, in addition to a review of well histories and completions complemented production optimization practices. Awareness of flow assurance issues and chemical management programs was provided to operations and engineering staff through a number of on-site training sessions.
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 | 0,000 | 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 ».