Incorporation of Formation Water into Rate-Transient Analysis of Tight Oil Wells with High Water-Oil Ratio: A Field Example from North America
Notice bibliographique
Résumé
Abstract Analysis of water production from multi-fractured horizontal wells in tight oil reservoirs has been mostly limited to analytical and/or numerical modeling of early-time fracture water production during flowback. During the flowback period, flush production of water occurs with a rapid decline in water rate. The rapid decline in water rate has tempted practitioners to ignore the impact of water production on rate-transient analysis (RTA) of multi-fractured horizontal wells (MFHWs) during the online production period. However, some tight oil reservoirs exhibit high water-oil ratios (WORs) throughout their production history. In these cases, water production should be included in RTA to ensure accurate hydraulic fracture characterization, short- and long-term rate forecasting, and fluid-in-place estimation, amongst other applications. In this study, two analytical methods are developed for incorporating formation water production into RTA of tight oil reservoirs producing during the transient linear flow period based on two scenarios: 1) water and oil producing from the same reservoir layer and 2) water and oil producing from different reservoir layers. For the first scenario, an estimate of formation water saturation is obtained using WOR history which, in turn, is used in an analytical method (facilitated by decoupling of phase saturations and pressure) to correct the single-phase oil or two-phase oil and gas RTA results. For the second scenario, the linear flow parameters calculated from corrected linear flow analysis of hydrocarbon (single-phase oil or two-phase oil and gas) and water layers are combined to obtain the total linear flow parameter. Practical application of the proposed analytical methods is demonstrated using a field example from a North American tight oil reservoir (with formation WOR ~ 1.3). The studied MFHW was first history-matched using the two water production scenarios – the linear flow parameter (Aki) was then calculated based on the simulation model match parameters. The results of the analytical methods used to correct for water production (for the two scenarios) were then compared with the numerical simulation results and determined to be within acceptable engineering error (10 %). This study further reveals that neglecting formation water production in RTA of this well leads to unacceptable errors (exceeding 100%) in the linear flow parameter estimates. The current study provides novel practical methods for correcting RTA to account for high formation water production. The methods are proved to be robust, easy to implement, and effective in reducing the errors of the widely used single-phase oil, and recently developed two-phase oil and gas, RTA models.
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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 | 0,000 |
| 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 ».