An Artificial Intelligence Belief System Reduces Nonproductive Time
Notice bibliographique
Résumé
This article contains highlights of paper SPE 189799, “Self-Learning Probabilistic Detection and Alerting of Drillstring Washout and Pump Failure Incidents During Drilling Operations,” by A. Ambrus, formerly with Intellicess; P. Ashok, D. Ramos, A. Chintapalli, Intellicess; A. Susich, T. Thetford, B. Nelson, M. Shahri, J. McNab, and M. Behounek, Apache Corp., presented at the 2018 IADC/SPE Drilling Conference and Exhibition, Fort Worth, Texas, 6–8 March. https:/doi.org/10.2118/189700-MS Introduction In recent years, detection and alerting systems have been applied to numerous drilling failures, including stuck pipe, fluid influx/loss, and drilling dysfunctions. But the detection of drillstring washout and mud pump failure has been left primarily to traditional methods that rely solely on standpipe pressure and pump rates or on measurement-while-drilling (MWD) sensor data. Drillers commonly use a simple hydraulic coefficient relating standpipe pressure to pump rate to detect drillstring washouts. MWD pressure and mud motor data may improve detection but this approach remains problematic owing to poor-quality sensor data and various factors that affect flow and pressure data. As a result, drillers often do not recognize a crack in the drillstring before it grows into a washout that may cost operators hundreds of thousands of dollars in lost time, equipment replacement, and fishing operations. Drillers also rely on standpipe pressure changes to detect mud pump failures and degradation from damaged pump parts. High-frequency pump pressure data may enhance pump wear detection and accelerometer data may be used to infer valve leaks, but these solutions are impractical in most field applications. To address these shortcomings, a methodology has been introduced using basic rig sensors and contextual information to detect various drilling failure modes. It has applied that same approach to early wash-out and pump failures detection and alerting. Methodology The detection and alerting system uses a Bayesian network, which aggregates key sensor inputs and contextual data and predictions from hydraulic modeling. Cumulatively they are the inputs to a probabilistic belief system. The probabilistic model outputs belief values of between 0 and 1 that are indicative of specific drilling, equipment, and sensor failures. Just as the human brain uses experience to generate beliefs upon which to act and react to the world around it intelligently, the probabilistic model uses past and present trends and artificial intelligence (AI) to generate beliefs about drilling dysfunctions and equipment failures. The model also assesses these trends to increase its accuracy through self-learning and self-calibration that enables it to adjust for poor sensor data, drilling conditions, and model uncertainties. As a consequence, when the value of a belief rises to a specified level and the system triggers an alert, the driller can act upon it with increased certainty that it is not a false alarm. Bayesian networks consist of discrete, or continuous-valued, nodes. Nodes are connected via conditional probability tables (CPTs) and linked by arrows generally representing the direction of causation. Each CPT is assigned a specific weight based on its relative impact on specific outcomes. A Bayesian Network in which the key node is “Unplanned Event” includes several event types and their associated probabilities, or beliefs. This network arrangement can be used to detect a broad variety of incidents related to well control and hydraulics, such as washouts, pump failures, kicks, lost circulation, wellbore breathing, packoff and others to which a probability value is assigned.
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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 | 0,001 | 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 ».