Stochastic Modeling of Multi-Phase Flowback From Multi-Fractured Horizontal Tight Oil Wells
Notice bibliographique
Résumé
Abstract As a result of current commodity price differentials, North American resource development has shifted towards unconventional liquids-rich and light tight oil plays. Due to the low permeability of these plays, extensive hydraulic fracturing is commonly required for commercial development. Operators are seeking new methods to characterize hydraulic fractures, particularly early in the well life. One such method is to utilize regularly gathered high frequency (hourly or greater) fluid production and flowing pressures to model the flowback process. Previous studies have suggested that this data can be quantitatively analyzed to estimate hydraulic fracture half-length and other fracture properties. Flowback of multi-fractured horizontal tight oil wells, stimulated with water-based fluids, commonly exhibit two distinct segments: a) a short period of single-phase water flow, which continues until breakthrough of formation fluids; and b) multi-phase flow (water, oil and gas) following breakthrough. The first flow regime observed in data collected at common frequencies typically consists of fracture storage/depletion of fracture fluid. This flow regime is followed by the breakthrough of hydrocarbon and formation water which results in a deviation from the depletion signature. The analysis procedure used for analyzing this data builds upon the analytical history-matching methodology presented by Clarkson and Williams-Kovacs (2013b). Consistent with the previous work we model the first segment as a single phase depletion of the fracture pore volume, from which a pre-breakthrough estimate of fracture permeability and half-length can be determined. The second segment is modelled by assuming transient linear flow of oil and formation water to the fracture, under the assumption of perfect displacement of frac water by formation fluids. From the second segment we are able to estimate long-term effective fracture permeability and half-length. However, as pointed out by Clarkson and Williams-Kovacs (2013b) there is a large degree of uncertainty in this type of analysis as a result of the number of unknowns which are being adjusted to provide an adequate history-match. To better understand the uncertainty and the impact of each parameter, stochastic simulation will be used to provide a range of parameter values, which provide an adequate fit of the data, and to determine which parameters have the greatest impact on the match. Additional improvements over the previous work include the consideration of different fracture geometries, the use of several fracture models to estimate fracture permeability, modeling produced water salinity to track the contribution of formation water and salt dissolution and additional constraints on relative permeability curve selection. The field case presented by Clarkson and Williams-Kovacs (2013b) for a prolific light tight oil reservoir is reanalyzed, along with a second well from the same pad for proof of concept and demonstration of the developed techniques.
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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) | 0,002 | 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 tête enseignante, pas un consensus.
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