A Probabilistic Belief System to Track the Cleanliness of a Hole in Real-Time
Notice bibliographique
Résumé
Abstract Good hole cleaning is essential to maintaining drilling efficiency and preventing non-productive events such as stuck pipe during well construction operations. A cuttings transport model may be used to determine the cleanliness of a hole, but its real-time rig site implementation is often made difficult by lack of necessary inputs to the model. There is a need for a simpler yet reliable approach to quantifying hole cleanliness using data readily available at any rig site. This paper proposes a method that relies on the detection of events over a long time horizon and the use of key parameters relating to such events to quantify hole cleanliness. These events are related through duration and frequency to probabilistic features in a Bayesian network, to infer the probability that the hole cleaning process has been efficient or poor. These events are also weighted by their age to ensure that current beliefs are not strongly influenced by those that are far in the past. The method was deployed on a drilling advisory system and is currently used on rigs in North American land operations. The events and features found to be most relevant to quantifying hole cleanliness were the circulation rates during drilling, tight spots when moving the drillstring, bit hydraulics, and prolonged periods of inactivity. Proactive hole cleaning actions such as working of the pipe, off bottom circulation and pipe rotation were also considered. The Bayesian network model used by the proposed method was able to be run with low computational overhead (micro-seconds on a standard edge device) compared to a traditional cuttings transport model. This functionality is enabled by an event logging procedure that keeps track of hole-cleaning events over time and consolidates several hours (days) of drilling information into relevant hole-cleaning features that can be processed quickly. The proposed method differentiates itself from the published methods on hole cleaning analysis in two main ways. First, it does not attempt to estimate the cuttings bed height or accumulation over time. Instead, it attempts to infer the probability that the hole cleaning operations are effective over time using features in data that suggest efficient or poor hole cleaning. Second, this method provides a clear indication of when hole cleaning actions are needed and why. The approach was validated with statistical methods using surface datasets from six wells involved in North American land operations. Through this validation it was determined that the method was highly effective in correctly characterizing hole conditions throughout the well operation. On the rig, the system was helpful not only in alerting the drillers whenever hole cleanliness deteriorated but also in providing the most likely causes of the deterioration. This provided the rig crew real-time guidance to make actionable decisions to avoid non-productive events.
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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 ».