Drilling Optimization Using Bit Selection Expert System and ROP Prediction Algorithm Improves Drilling Performance and Enhances Operational Decision Making by Reducing Performance Uncertainties
Notice bibliographique
Résumé
Abstract A rule based drill bit selection expert software system and Rate of Penetration (ROP) prediction algorithm has been recently applied in the optimization process of a 4500 m vertical foothills well in Western Canada. Post well analysis shows that when the expert system recommendations were followed by the operator, increases in ROP and run length over the local pacesetter well were experienced in each hole section. ROP increases of 15% in the 311.1mm section, 52% in the 215.9mm section and 60% in the 142.9mm section were achieved, as well as bit life increases up to 33% with TCI bits. Although the operator did not follow all of the expert system recommendations through the entire well, these increases did contribute to savings in drilling time below AFE of 15 days over the entire well. Comparison with the actual drilling performance showed close agreement in trend to the predicted ROP through most lithological intervals, which helped to confirm the accuracy of the process of geological / pore pressure predictions and the ROP prediction algorithm. The expert system is a rule based bit selection system that uses a detailed description of the drilling environment, including meter based lithology, synthetic wireline logs, predicted pore pressures and anticipated operating parameters of the well or hole interval being analyzed to produce a bit selection recommendation including IADC bit type and bit features. The ROP algorithm has been developed as a drilling optimization tool and attempts to model the technical limit ROP that can be expected through a given hole interval. The ROP algorithm uses as its inputs detailed lithological descriptions of the anticipated formations, hole size, mud weight, predicted pore pressure, bit type, and anticipated operating parameters to calculate an accurate meter based ROP prediction. The ROP algorithm has been applied in several drilling environments worldwide and comparisons with actual drilling performance have been used to modify the calculations and improve predictions. The ROP algorithm improves drilling decisions, and provides performance analysis while guiding financial planning. The ROP algorithm can be applied in the planning phase of a project to develop time curves based on expected performance and to compare and contrast potential bit/BHA types based on performance predictions. Furthermore, the ROP algorithm can be used in post-well analysis to identify areas where potential drilling performance was not achieved, and help in identifying improvements for future projects.
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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 | 0,001 |
| 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 ».