Notice bibliographique
Résumé
Abstract This paper first presents a consistent mathematical framework to predict sand production volume, focusing on the coupling between hydro-mechanical factors, formation deformation and the resulting permeability change. Two types of sand production mechanisms are considered: production of coarse sands under mechanical failure and production of fine sands under hydro-dynamical erosion. The Drucker-Prager constitutive law with cap hardening is adopted to describe both dilative and collapsing deformation behaviour. The finite element method is used to solve the coupled governing equation system. After the model is validated with a field history case, it is used to compute two examples of wellbore pressure drawdown and the associated impact on the near-wellbore sanding process and permeability change. The calculation indicates that the permeability can be modified any time during the sanding. For example, under suitable reservoir depletion, the near-wellbore permeability can increase by 30%. However, more drastic pressure depletion under the otherwise identical in situ and operating conditions causes compaction near the wellbore and permeability decline by nearly 40%. Therefore, these simulated cases suggest that a balanced pressure depletion strategy should be used to manage the sand production. Introduction Sanding becomes more critical as operators follow more aggressive production strategies. Sand production occurs when the reservoir fluid, under high production rates, dislodges a portion of the formation solids leading to a continuous flux of formation solids into the wellbore. As a result, the sanding may compromise oil production, increase completion costs, and erode casing, pipes and pumps, or plug the well if sufficient quantities are produced. Moreover, the sanding process may cause complex temporal and spatial changes in permeability in the near-wellbore region. Generally, erosion during sanding increases permeability near the wellbore and thus benefits the petroleum production. Therefore, sand production has proven effective to increase well productivity, both in heavy oil and light oil reservoirs. However, stress concentration around the wellbore and/or perforation tips, if aggressive pressure drawdown is carried out, can induce localized formation collapse and compaction. Such a collapse/compaction region may spread outwards deep into the formation as the fluid flow and sand production continues. The formation compaction may lead to permeability impairment which is equivalent to formation damage, albeit mechanically induced. This is particularly serious for weakly consolidated sandstone reservoirs such as in the Shengli Oil Field in China. Therefore, however demanding operators are by wanting to increase production, it is critical to design a proper production strategy that minimizes the negative impact of sand production on field operation and reservoir permeability change and/or maximize its beneficial effect. This can only be achieved via an improved understanding of the sanding mechanism and associated permeability changes; in particular, the need to understand the sanding process under an integrated theoretical frame system(1, 2). The challenge is to develop relevant mathematical models to quantitatively interpret the evolution of the sanding process so that the amount of sand production can be predicted. A quantitative model will allow engineers to understand the complicated sanding phenomena, evaluate the impact of sand production on reservoir production and provide an efficient measure to reduce unnecessary costs during field operations.
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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 | 0,000 | 0,001 |
| 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 | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».