Applications of Machine Learning and Data Mining in SpeedWise® Drilling Analytics: A Case Study
Notice bibliographique
Résumé
Abstract The daily drilling report (DDR) contains the daily activities and parameters during drilling and completion (D&C) operations that can be used to identify the bottlenecks and improve efficiency. However, the datasets are large, unstructured, text heavy, not correlated to other datasets, and contain numerous gaps and errors. Thus, conducting any meaningful drilling analytics becomes cumbersome. In this paper, an innovative method is introduced to automatically clean the data and extract intelligent analytics and opportunities from these reports. Natural language processing (NLP) and deep neural network (DNN) models are developed to extract information from unstructured DDRs. Numbers of interest (such as depths, hole sizes, casing sizes, setting depths, etc.) are extracted from text. Drilling phase, non-productive time (NPT) and the associated types are predicted with DNN models. With 30% of the dataset for training, accuracies achieved on the remaining data include 87.5% for drilling phase, 90.7% for time classifications (productive or non-productive), and 89% for associated NPT types. Then, the D&C datasets are integrated with other data sources such as production, geology, reservoir, etc. to generate a set of crucial drilling and reservoir management metrics. The proposed method was successfully applied to several major oil fields (with total of more than 2,000 wells) in the Middle East, North America, and South America. Here, a case study is presented in which the developed method was applied to more than 200 wells drilled from 2012 to 2016 in a major oil field. By using the proposed method, the data processing and aggregation time that used to take months to accomplish was reduced to only a few days. As a result, major types of NPT were rapidly identified, which include rig-related issues such as repair and maintenance (30%), followed by stuck pipe (23%), hole/mud related issues (such as wellbore stability, mud loss, shale swelling, etc.) (20%), and downhole equipment failures and maintenance (14%). Drilling solutions such as contractual advices, improving the mud formulations, and drilling with a rotary steerable system (RSS) were proposed to possibly mitigate the NPT and improve drilling efficiency. Implementation of the proposed solutions eventually resulted in reducing the drilling time and improving capital efficiency. Novel technologies such as NLP, data mining, and machine learning are applied to rapidly QC, mine, integrate and analyze large volumes of D&C data. In addition, this novel approach assists D&C obstacles identification and future plan optimization with evident benefits for improving performance and capital efficiency from a reservoir management perspective.
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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 ».