Machine Learning and Natural Language Processing for Automated Analysis of Drilling and Completion Data
Notice bibliographique
Résumé
Abstract During Drilling and Completion (D&C) operations large volumes of data are typically collected in oil and gas fields. These datasets typically contain hidden (valuable) information that could be used to improve D&C performance (e.g., by identifying bottlenecks in drilling operations, analyzing non-productive time, optimizing rig schedule based on key indicators, etc.). Unfortunately, D&C datasets are typically not well suited for data mining: they are not structured, they are text heavy and they often contain numerous gaps and errors that hinder automated pre-processing techniques. In this paper, an innovative method to automatically extract smart analytics and opportunities from D&C reports is presented. Initially, a combination of Natural Language Processing, Data Mining, and Machine Learning algorithms are used to quality check a large volume of drilling data (including the text in the daily drilling reports), extract crucial information, and predict the non-productive time and its type. This results in a significant reduction of the labor-intensive quality check task for thousands of datasets and also the unbiased classification of the events. 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, which was successfully applied to fields in North and South America, is applied here to two onshore fields located in the Middle East. By applying the developed tool, the data processing and integration time that used to take months to accomplish could be reduced to only a few days. In addition, analyzing metrics such as the Drilling Efficiency Index, normalized drilling days for each field and well type, cost analysis, detailed analysis of non-productive time, effect of completion parameters on production, design efficiency, etc. enabled us to quickly identify the D&C bottlenecks in each field and provide customized solutions to diagnose each problem. In addition, the historical data was used to improve future rig scheduling and resource allocation by applying advanced optimization algorithms with cumulative oil production, net present value, and operation time as the objective functions. In the final stage, the results were vetted by the experts to assure it meet the best D&C practices. The novelty of the presented method lies in using advanced technologies such as Natural Language Processing, Data Mining and Machine learning to QC, mine, integrate and analyze large volumes of D&C data in a very short time, find the bottlenecks and optimize the future plan with evident benefits of improving D&C performance and capital efficiency from a global 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 ».