Sand Control Design through Assessment of Mathematical Models Representing Particle Size Distribution of Reservoir Sands
Notice bibliographique
Résumé
Abstract Standalone screen (SAS) design conventionally relies on particle size distribution (PSD) of the reservoir sands. The sand control systems generally use D-values, which are certain points on the PSD curve. The D-values are usually determined by a linear interpretation between adjacent measured points on the PSD curve. However, the linear interpretation could result in a significant error in the D-value estimation, particularly when measured PSD points are limited and the uniformity coefficient is high. Using the mathematical representation of the PSD is an efficient method to mitigate these errors. The aim of this paper is to assess the performance of different mathematical models to find the most suitable equation that can describe a given PSD. The study collected a large databank of PSDs from published SPE papers and historical drilling reports. These data indicate significant variations in the PSD for different reservoirs and geographical areas. The literature review identified more than 30 mathematical equations that have been developed and used to represent the PSD curves. Different statistical comparators, namely, adjusted R-squared, Akaike's Information Criterion (AIC), Geometric Mean Error Ratio, and Adjusted Root Mean Square Error were used to evaluate the match between the measured PSD data with the calculated PSD from the formulae. The curve fit performance of the equations for the overall data set as well as PSD measurement techniques were studied. A particular attention was paid towards investigating the effect of fines content on the match quality for the calculated versus measured curves. It was found that certain equations are better suited for the PSD database used in this investigation. In particular, Modified Logestic Growth, Fredlund, Sigmoid and Weibull models show the best performance for a larger number of cases (highest adjusted R-squared, lowest Sum of Squared of Errors predictions (SSE), and very low AIC). Some of the models show superior performance for limited number of PSDs. Additionally, the performance of PSD parameterized models is affected by soil texture: For higher fines content, the performance of equations tends to deteriorate. Moreover, it appears the PSD measurement techenique can influence the performance of the equations. Since the majority of the PSD resources used here did not mention their method of measurement, the effect of measurement technique could only be tested for a limited data, which indicates the measurement technique may impact the match quality. Fitting of parameterized models to measured PSD curves, although well known in sedimentology and soil sciences, is a relatively unexplored area in petroleum applications. Mathematical representation of the PSD curve improves the accuracy of D-values determination, hence, the sand control design. This mathematical representation could result in a more scientific classification of the PSDs for sand control design and sand control testing purposes.
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Comment cette classification a été obtenuedéplier
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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».