Numerical Evaluation of Geomechanical Parameters Affecting Productivity Index in Weak Rock Formations?Part 2: Field Application
Notice bibliographique
Résumé
Abstract The numerical model proposed in Part 1 was first verified and then applied to a well-documented field case involving openhole cavity completion in a coalbed methane reservoir. Following calibration against the field observation, the numerical model was used to test the effect of strength, permeability, reservoir depth, and pressure gradient on cavitation and production. The sensitivity studies indicate that the potential for cavitation and production increases with reduction in strength properties, reduction in permeability, increase in depth, and increase in pressure gradient. Among them, the most influential parameter is the apparent cohesion. The smaller the cohesion, the larger the size of both cavitation and the adjoining plasticfailed zone. The latter is particularly important for boosting production because within the plastic zone, permeability increases due to shearing (dilation) and reduction in the mean effective stress. A corollary of the above is that in competent rocks, the response may be reversed since the creation of the cavity results in the development of a relatively tight plastic zone and a large zone outside the plastic zone within which permeability becomes depressed because of a net increase in the effective mean stress. In such formations, there would be a net reduction in permeability and hence productivity. Introduction In this study, we promote the benefits of maximizing sand production under controlled conditions. In many fields where the geological conditions (reservoir strength properties, stratigraphy, stress state) are favourable, creating massive sand production during the completion phase which can boost production by severalfold. In other fields where large amounts of sand production cannot be easily managed, mini sand bursts can be considered for removing the near wellbore plugging (skin damage); this typically improves productivity by about 30% and saves the cost of installing gravel packs and screens. While the benefits of sand production have been noted in a number of fields, such as the Gulf of Mexico and the North Sea(1, 6), the industry, in general, has been reluctant to consider it as routine operation for a number of reasons. For instance: insufficient data are available to make reliable predictions; the mechanisms are not well understood; concerns over the fact that sand production may result in total instability (ongoing sand production); traditional practices are hard to change; and, experience and field data gathered thus far are considered insufficient to demonstrate the significant cost effectiveness of sanding. The objective of the study presented here is to demonstrate the mode of sand production and its influence on the productivity index. In a companion paper, the theory used in the proposed numerical model is described. In this paper, the model is applied to a well-documented field case involving openhole cavity completion in a coalbed methane reservoir. The numerical findings are compared against the field data and the results and field implications are then discussed. As pointed out in the companion paper, however, the proposed numerical model cannot be applied to wormhole development in heavy oil sands where quite different sand production mechanisms are involved.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 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 ».