A Novel Probabilistic Rig Based Drilling Optimization Index to Improve Drilling Performance
Notice bibliographique
Résumé
Abstract Currently, real-time adjustments to drilling parameters such as weight on bit (WOB), drillstring revolutions per minute (RPM), flow rate, etc., are based primarily on experience. This is mainly due to the uncertain nature of information (both downhole and surface) available and inability of humans to aggregate multiple data streams in real-time to make optimal decisions. The objective therefore is to build a decision support tool that can overcome these limitations by automatically aggregating this data, identifying drilling inefficiency and suggesting optimal drilling parameters. The methodology presented in this paper uses a Bayesian network to represent the drilling process and is capable of representing uncertainty in a way that is robust to bad sensor data. The model is updated in real-time and tracks variations in drilling conditions. Various dysfunctions such as bit balling, bit bounce, whirl, torsional vibrations, high mechanical specific energy (MSE), auto-driller erratic behavior, etc., are identified by tracking the movement characteristics of various sensor data in relation to model predicted values. A holistic drilling optimization index is thus derived by aggregating all this information. This index coupled with the drilling dysfunction prediction ultimately enables recommendation of drilling parameter corrections. The drilling optimization index has been integrated into a drilling rig data aggregation system currently in operation on twenty rigs in North America. The system has access to real-time data, both at low frequency (less than 1 Hz) as well as data in the 1 to 10 Hz range, and also contextual data (such as data typically available in a tour sheet or well plan). In deploying the system, human factors aspects were given significant consideration. A typical driller is not familiar with concepts such as Bayesian networks, MSE, etc. By displaying the effectiveness of drilling as a single, dimensionless parameter, an index that varies between 0 and 1, with 0 representing inefficient drilling and 1 representing optimal drilling, the message is effectively communicated to the driller. The index is currently depicted in a very intuitive "speedometer" type of visual. Values are low and closer to 0 when dysfunctions occur, and when that happens suggestions are provided on how to mitigate the dysfunctions. These suggestions are visually presented in the form of operational cones in the WOB-RPM space. Additionally, the variation of the index with drilling depth is displayed to enable the driller to identify how formation changes impact drilling performance. This was found to be useful to drilling engineers who are generally tasked with optimizing the drilling process.
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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 ».