An Improved Correlation to Estimate Productivity Index in Horizontal Wells
Notice bibliographique
Résumé
Proposal Because their great efficiency in producing higher flow rates per unit pressure drawdown, horizontal wells have currently become a popular alternative for the development of hydrocarbon fields around the world. So far, most of the introduced correlations to estimate the productivity index for these wells have shown certain differences among their results. This does not allow us to properly establish which one of them provides the closest value to the actual one, since there is no evidence of a trustable enough reference point. Throughout the years, several investigations for the determination of horizontal-well productivity index have been carried out. These researches have been focused on the determination of steady-state solutions for the above-mentioned parameter, therefore, a diverse number of correlations have been introduced. These correlations have been presented by such very well-known researchers as Giger, Borisov3, Merkulov2, Renard & Dupuy2–5 and Joshi2–5. They are mainly based upon complex analytic solutions which may have some uncertainties when applying them. This paper proposes an improved steady-state correlation to calculate productivity index for horizontal wells and evaluates the most commonly used existing correlations to estimate this parameter by using numerical simulation. Besides that, a sensitivity analysis on the influence of the variation of each variable in the existing and proposed models was carried out. The analysis was conducted by generating a synthetic drawdown test by means of a commercial reservoir simulator. Using the pressure derivative curve, a time range where steady-state behavior takes place was defined. Then, a simulation was performed with the purpose of determining the pressure distribution in the reservoir within that range of time. This allows us to estimate the horizontal-well productivity index for any drainage radius. More than 500 simulation runs were performed to estimate the results obtained by the improved correlation introduced in this work and the existing ones. Several plots of productivity index versus each one of the model variables were constructed for comparison purposes. It was observed that Joshi's correlation matches well with the simulated results. However, the proposed correlation provides much better results than those provided by Joshi's within a very wide range of variation of the parameters involved in the different correlations.
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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 ».