A Comprehensive Approach to Modeling Sanding During Oil Production
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Résumé
A Comprehensive Approach to Modeling Sanding During Oil Production Alireza Nouri; Alireza Nouri Dalhousie University Search for other works by this author on: This Site Google Scholar Hans Vaziri; Hans Vaziri Dalhousie University Search for other works by this author on: This Site Google Scholar Hadi Belhaj; Hadi Belhaj Dalhousie University Search for other works by this author on: This Site Google Scholar Rafiqul Islam Rafiqul Islam Dalhousie University Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Port-of-Spain, Trinidad and Tobago, April 2003. Paper Number: SPE-81032-MS https://doi.org/10.2118/81032-MS Published: April 27 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Nouri, Alireza, Vaziri, Hans, Belhaj, Hadi, and Rafiqul Islam. "A Comprehensive Approach to Modeling Sanding During Oil Production." Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Port-of-Spain, Trinidad and Tobago, April 2003. doi: https://doi.org/10.2118/81032-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Latin America and Caribbean Petroleum Engineering Conference Search Advanced Search AbstractSand production has been a major dilemma facing operating oil companies over many years, sometimes substantially increasing production costs. On the other hand, a very controlled solid production can enhance oil production. A dependable predictive model is vital for planning production strategies in order to optimize well production. To date, despite several research studies, sand production remains the nightmare of petroleum engineers.Even though many researchers have tried to predict sand production in the past, none of them suggested a comprehensive model that takes care of a variety of mechanisms at different points and different times. Moreover, rare models predict sanding rate and volume along with sanding initiation. This paper presents a comprehensive numerical modeling of sand production that appreciates the different behavior of the medium near and far well-bore from early to late life-time. Sanding criteria were adopted according to the physics of the problem, by taking the sequential nature of sand production into consideration.The numerical model that was used not only assesses sand production qualitatively but can also give the sanding rate at different times. This was used to model the observations of sand production in a large block test, and the sanding rate and volume generated from numerical modeling agreed with experimental results. With each stage of increased drawdown or depletion, a burst of sand took place which enlarged the cavities initiated from the perforations. The expansion of the cavity was soon stabilized and this behavior was predicted by numerical modeling. Moreover, besides considering shear and tensile failure of the material, the possibility of volumetric failure has been discussed.IntroductionSand Production in the petroleum industry is a phenomenon of solid particles being produced together with reservoir fluid. This phenomenon is costing the industry billions of dollars every year. Corrosion of pipelines and other facilities, sand-oil separation costs, possible wellbore choke, environmental effects, reduction of production rate and possible work-overs for clean-up operations are some examples of these costs. On the other hand, a controlled sanding or even sand production invocation has proved to be very effective in increasing production rate, especially in heavy oil recovery, asphalt wells and low PI wells 1,2,3.Sand production takes place if the material around the cavity is disaggregated and then there is enough fluid flow rate to produce the grain particles. Disaggregation of the material initiates from cavity faces and propagates inside the medium. Material disaggregation can take place if the material fails under excessive drawdown or depletion or a combination of them. Depletion and drawdown fail the medium under either of shear or tensile or volumetric failure mechanisms or a combination of them. After failure of the rock disaggregates the material, the resulted grains are produced by the existence of enough pressure gradients in tension. Friction between the grains and capillary tension are the resisting forces against grains movements 4,5.To date, there has been no comprehensive mathematical model that considers all the mechanisms associated with sand production. A model is presented in this paper that takes into account different failure mechanisms that may play a role in sanding. Therefore, in the rest of this paper, first, possible failure mechanisms associated with sanding are explained. For that, a comprehensive model for simulating the behavior of the formation against the applied loads in early and late life, near and far well-bore is presented. The significance of this numerical model is providing a tool for better understanding and modeling of sand production by better prediction of the failure mechanism, sanding rate, and volume. Keywords: drawdown, numerical modeling, mpa, porosity, gradient, cavity face, completion installation and operations, modeling, shear failure, reservoir geomechanics Subjects: Reservoir Characterization, Perforating, Reservoir geomechanics, Completion Installation and Operations, Completion Operations This content is only available via PDF. 2003. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0,001 |
| 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 ».