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Enregistrement W4242339750 · doi:10.2523/75667-ms

Well Performance Prediction of a Well Experiencing Changes in Completion

2002· article· en· W4242339750 sur OpenAlexaboutno aff
T. Marhaendrajana, J. Desroches

Notice bibliographique

RevueProceedings of SPE Gas Technology Symposium · 2002
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCitationComputer scienceDownloadProduction (economics)Information retrievalLibrary scienceWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Well Performance Prediction of a Well Experiencing Changes in Completion T. Marhaendrajana; T. Marhaendrajana Schlumberger Oilfield Services Search for other works by this author on: This Site Google Scholar J. Desroches J. Desroches Schlumberger Oilfield Services Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. Paper Number: SPE-75667-MS https://doi.org/10.2118/75667-MS Published: April 30 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Marhaendrajana, T., and J. Desroches. "Well Performance Prediction of a Well Experiencing Changes in Completion." Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. doi: https://doi.org/10.2118/75667-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Unconventional Resources Conference / Gas Technology Symposium Search Advanced Search AbstractThis paper presents a new semi-analytical method to predict the production performance of a well stimulated by acid or hydraulic fracture treatment. The new method considers the production history before stimulation, in contrast to current analytical methods that ignore the production history of the "old" well. The initial condition of the "new" well is based upon an "assumed stabilized condition" of the old well.The superposition technique has been extensively used in the literature to model production history (i.e., variable flow rate and variable bottomhole pressure history) and predict well performance. There has not been a detailed study using analytical or semi-analytical solutions that includes the change in well completion (well model change) and that investigates the influence of the production history of the unstimulated well on the well performance prediction of the "stimulated" well. This paper addresses both issues.The semi-analytical method presented here considers changes in well completion, which are becoming more frequent as recompletions become more common. We present validations of this approach using a numerical reservoir simulator for oil and gas reservoirs in which the wells are recompleted with a hydraulic fracture treatment after some initial production.IntroductionThe effect of production history on the analysis of well performance data has been investigated by many authors for years. Horner1 provides a method for treating the variable-rate case based on the application of the superposition theorem. This method requires knowledge of production history as a function of time. Van Everdingen and Meyer2 also presented the use of the general superposition for analyzing pressure buildup data preceded by variable production rate history. More applications of the van Everdingen and Meyer method can be found in Whitson and Sognesand.3 Odeh and Jones4 use a superposition method based on the logarithmic solution, which should be used only during a radial flow regime.The drawback of using full superposition is that the computation is lengthy when there are many significant rate changes. In addition, the complete rate history may not always be available.A simple method was proposed by Horner1 to obtain an equivalent producing time by dividing the total cumulative production by the last established rate, which is later known as material balance time. Although Horner did not present a theoretical justification for this method, it is still used by the majority of reservoir analysts today.The theoretical justification of the material balance time was presented by Blasingame et al.5 The authors showed that the material balance time concept is rigorously accurate for "stabilized flow" or a pseudosteady-state-like flow regime. The lower bound for the start of the stabilized flow is the time to reach pseudosteady state for a constant rate production. This means that any new transient introduced by large changes in rate after this time will eventually die and that stabilized flow will dominate.To our knowledge, the effect of production history has not been investigated for a case where the well completion changes (i.e., the well model changes). This paper examines this phenomenon and provides a semi-analytical method for predicting the well performance under this condition by implementing the material balance time approach. The primary application of this method is the evaluation of the performance of a proposed recompletion.Approximate Solution for a Well Experiencing Changes in CompletionFig. 1 is an illustration of a well experiencing changes in completion. The well model change may represent a conventional vertical well that is hydraulically fractured or otherwise recompleted. Well model 1 refers to the unstimulated well that has been produced up to t = t2, with rates q1 and q2. At t = t2, the well is stimulated and is produced with rate q3. In a case where the well is not stimulated (well models are the same), this problem reduces to a variable-rate problem. Keywords: production control, completion, fluid dynamics, unstimulated well, gas case, permeability, production monitoring, bottomhole, semi-analytical solution, well performance prediction Subjects: Well & Reservoir Surveillance and Monitoring, Reservoir Fluid Dynamics, Formation Evaluation & Management, Flow in porous media, Drillstem/well testing, Well performance, inflow performance 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,403
Score d'incertitude au seuil0,608

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,212
Écart entre enseignants0,198 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2002
Routes d'admission1
Résumé présentoui

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