A Quick Decline Method for Forecasting Multiple Wells Using Sparse Functional Principal Component Analysis
Notice bibliographique
Résumé
Abstract Accurate production forecasting for multiple wells that have both sparse and irregular measurements concurrently is a challenging task. Type-well analysis is commonly employed to model the average decline behavior of a group of wells from empirical relationships. The modeled type-well represents the behavior of a typical well in the studied reservoir. However, modifying the type-well to forecast individual well data is difficult. In this study, sparse functional principal component analysis (FPCA) is utilized to accurately forecast production from multiple wells simultaneously from the systematic statistical trends inferred from the group of wells. Sparse FPCA analyzes an ensemble of irregularly-sampled timeseries to describe the underlying random process (RP) using the decomposed components. As such, one can sample from the estimated RP and generate a smooth and regularly-sampled timeseries. The sparse FPCA is primarily an interpolation method where the reconstructed timeseries could not reach beyond the horizon set by the ensemble length. However, with the proposed approach in this study, the decomposed components of FPCA are extrapolated using an autoregressive integrated moving average (ARIMA) model to generate the full probabilistic forecasts beyond the horizon. In this proposed method, the underlying RP is extrapolated first, and then the extended timeseries are generated simultaneously by sampling from the new RP. To validate the accuracy of the extrapolated data in the short-term, part of the timeseries with longer histories are excluded from the training process and only used for testing. The sparse FPCA was applied to analyze monthly gas production data from 200 multi-fractured horizontal wells (MFHWs) of a selected operator in the Montney Formation in Canada. The results indicate that the production data of all the wells could be easily condensed using only two principal components, describing more than 99% of the information content of the production timeseries. Additionally, the resulting decomposed components were convoluted, and the production profiles of the wells with short histories were extended from the information contents of the ensemble. Additionally, with the proposed stochastic ARIMA technique, the production profiles of all the wells were forecasted for 400 months beyond the ensemble limit. The results demonstrate that the extrapolation could accurately match the measured data used for testing, which provides confidence in the stochastic long-term forecast. This study demonstrates for the first time that sparse FPCA can be combined with the ARIMA model to quickly conduct the probabilistic production forecast for hundreds and even thousands of MFHWs simultaneously, which can significantly improve the current type-well modeling workflows.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».