Machine-Learning Approach Determines Spatial Variation in Shale Decline Curves
Notice bibliographique
Résumé
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 196110, “Machine Learning of Spatially Varying Decline Curves for the Duvernay Formation,” by Aleksandr Bakay, Jef Caers, and Tapan Mukerji, SPE, Stanford University, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September-2 October. The paper has not been peer reviewed. The two most common techniques for forecasting production performance for a new shale well are decline- (type) curve analysis and machine learning. The complete paper describes an automated machine-learning approach to determine the spatial variation in decline type curves for shale gas production, based on existing data of production, completion, and geological parameters. The methodology allows the user to decide whether the focus should be purely on forecast quality or on a combination of forecast and clustering quality. The resulting model will enable the prediction and uncertainty quantification of production profiles for new target wells or areas in the basin. Methods of Forecasting Production Performance Decline-curve analysis is the most-popular technique for forecasting production performance in shale formations because of the need for fast decisions. The technique involves borrowing decline curves from the closest wells or from wells with similar geological, completion, or fluid properties. The idea of decline-curve analysis is based on the fact that a similar production profile is expected from the closest wells or from wells with similar properties. However, the process is often manual and very subjective. As a result of the approach, each existing well is assigned to a particular cluster of decline curves, each cluster having a certain typical decline curve. Clusters can be spatial or represented in completion variable space. To obtain a production forecast for a new well, the authors use the decline curve from a cluster to which it is believed that the new well will belong, usually sampling from a cluster map. The second approach to forecasting shale production performance is machine learning that focuses on statistical correlations. A statistical model is created that connects decline curves with the same well parameters used in decline curve analysis. Typically, the result is a trained machine-learning model. For a new well, it provides production performance, with or without an uncertainty range. Additionally, maps can be created of forecasted production profiles or total recovered fluid. The objective of the project described in the complete paper was to provide a methodology that creates clusters of decline curves and estimates decline curves for a particular location or set of variables. The intent was to limit the manual aspect of clustering and create a robust work flow.
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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 | 0,001 |
| 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 | 0,001 |
| 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 ».