What is the Magic of Water in Producing Sand?
Notice bibliographique
Résumé
What is the Magic of Water in Producing Sand? Hans Vaziri; Hans Vaziri BP America Inc. Search for other works by this author on: This Site Google Scholar Bob Barree; Bob Barree Barree & Associates Search for other works by this author on: This Site Google Scholar Yuxing Xiao; Yuxing Xiao Dalhousie University Search for other works by this author on: This Site Google Scholar Ian Palmer; Ian Palmer BP America Inc. Search for other works by this author on: This Site Google Scholar Mike Kutas Mike Kutas BP America Inc. Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. Paper Number: SPE-77683-MS https://doi.org/10.2118/77683-MS Published: September 29 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Vaziri, Hans, Barree, Bob, Xiao, Yuxing, Palmer, Ian, and Mike Kutas. "What is the Magic of Water in Producing Sand?." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. doi: https://doi.org/10.2118/77683-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractWe performed a comprehensive sand prediction study of several deep, HPHT wells within a large reservoir and the findings revealed that, for these wells, common criteria based on critical drawdown, minimum bottomhole pressure, depletion or fluid velocity failed to predict the outcome by a relatively large margin. All these wells were subjected to relatively high levels of drawdown and also very high fluid velocities and, with the exception of one well, none showed any sanding until water production was encountered.In this paper, we provide a rationale for why water can be highly effective in inducing sand production and we support our argument using advanced numerical modeling. This exercise also ranks the performance of some of the common tools and theories that are conventionally used for sand prediction. We also provide reasons why some of these models do not perform satisfactorily for the cases studied.The originality of the work is in demonstrating that prior to sand production, the dis-aggregated rock (i.e., individual sand particles) around the wellbore is basically held together by the capillary tension which is destroyed by water flow. While the capillary tension appears to be insignificant (as it is in the order of 1 psi or so), it provides a significant resistance against sand mobilization. The importance of this issue has been quantified using advanced numerical modeling. This concept is vastly different from the previous theories that propose water weakens the rock through chemical interaction or changing the relative permeability.IntroductionWhile a great deal of work has been done in the general area of sand production1–15, approaches used to quantify the volumes of the produced sand have faced challenges in the validation process (Class A prediction). In fairness, it is difficult to firmly single out the deficiencies when predictions do not materialize in a consistent fashion as the quality of the input data, monitoring of sanding events as well as the assumptions and physics used for modeling sanding can all be potential culprits. Following up on this line of reasoning, it is not the intention of this paper to prove or refute any previous work done in sand production studies nor to show that our method is universally superior. The primary intention is to provide a deeper insight into the mechanisms of sanding, in general, and water-production induced sanding, in particular. We try to support the views presented using basic fundamentals along with field observations.Significance and Potential ApplicationsConventional sand production models2,3,15–17 predict the onset of sanding which in practice is presumed to signify large-scale sanding. This single case solution scenario does not give operators options to assess risks and benefits which is becoming increasingly more relevant under the currently optimized completion and production practices. In essence, operators would like to know, at any stage in a well's life, how much sand will be produced (rate and duration) for a given production strategy (e.g., maximum drawdown, effects of bean-up and shut-in cycles, impact of water). By better understanding the role of various variables one is enabled to choose the optimal completion method for the life of the well (which may exclude installation or deferring sand control measures) and quantify the impact of aggressive fluid production strategies in terms of volume and rate of sanding. Keywords: stability, capillary tension, reservoir characterization, spe 77683, mechanism, cementation, adhesion, water production, production control, upstream oil & gas Subjects: Well & Reservoir Surveillance and Monitoring, Reservoir Characterization, Perforating, Completion Installation and Operations, Completion Operations This content is only available via PDF. 2002. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| 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 ».