History-Matching and Forecasting Tight/Shale Gas Condensate Wells Using Combined Analytical, Semi-Analytical, and Empirical Methods
Notice bibliographique
Résumé
Abstract The primary focus of the majority of current, and foreseeable, natural gas drilling within North America is low-permeability liquid-rich gas and gas condensate reservoirs, where the liquid fraction is now a major source of revenue. Development of these liquid-rich resources is aided by the use of multi-fractured horizontal wells (MFHWs), and is at an early stage; further research is required to appropriately manage the resource for optimal hydrocarbon recovery. The appropriate forecasting methodologies to apply to these tight liquid-rich plays are a focus of current research. Although numerical simulation, which can account for complex PVT, reservoir and fracture characteristics in these liquid-rich plays, is the most rigorous method for forecasting, this technique usually cannot be applied to every well in a field because of a lack of supporting data and time required for analysis. Empirical methods provide an alternative for routine forecasting, but their lack of a physical basis means that model fitting parameters are difficult to constrain, leading to large uncertainties in forecasting. Analytical methods, while capable of incorporating more rigorous physics, require more information than empirical methods and likely also cannot be applied to every well in a field. In order to address the limitations of existing empirical and analytical methods for forecasting MFHWs producing from liquid-rich tight gas/shale, we demonstrate application of a workflow recently introduced by Clarkson (2013b). In this workflow, analytical models are first used to history-match and forecast MFHWs that have sufficient data, and then empirical models are used to match the analytical model forecast to constrain model parameters for wells in which the analytical methods cannot be applied. For this purpose, a suite of analytical models are proposed, that can model a range in flow-regime sequences from simple linear-to-boundary flow scenarios, to more complex flow regime sequences exhibited by MFHWs with branched fractures. Similarly, a suite of empirical methods are used, and the models yielding the most accurate matches to the analytical models are selected for forecasting. Lastly, in order to bridge the gap between analytical and empirical methods, we utilize the recently developed semi-analytical method introduced by Clarkson and Qanbari (2014), which has as its basis the contacted gas-in-place calculations of Agarwal (2010). Although the analytical and semi-analytical models used in this work are strictly applicable to single-phase flow scenarios, we have demonstrated using simulation cases (as have others) that constant condensate gas ratios can occur for tight/shale gas condensate wells exhibiting transient linear flow and flowing at near constant flowing bottomhole pressure. For these cases, the single-phase forecasting methods can be applied, and both gas and condensate phases may be forecast accurately, even if multi-phase flow is occurring in the reservoir. We demonstrate the accuracy of these methods using simulated cases, and apply our workflow to an actual field example of a liquid-rich shale MFHW. This study will be of interest to those petroleum engineers who are faced with forecasting a large number of liquid-rich shale wells, and desire methods that can be simply applied to constrain forecasts and improve accuracy.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».