Construction of a Carbonate Reservoir Model Using Pressure Transient Data (Field Case Study)
Notice bibliographique
Résumé
Abstract A geological model is usually constructed using geophysical and petrophysical data by virtue of geological information. In this work, we built a carbonate reservoir model (Iranian offshore field) not only by seismic and well log data but also by the integration of 99 well test data available for this field. Many features such as sealing faults, aquifer, fracturing and layering systems were observed during the well test interpretation. Some of the above features were identified only by pressure transient data such as one sealing fault in which it was not observed in the seismic data. The existence of this fault is later confirmed by geological information acquired during drilling of a horizontal well in the field. In addition to the identification of structural features, permeability data from well testing were also used for the construction of iso-permeability map instead of depending on permeability data from core analysis. This is very significant assignment in a heterogeneous carbonate reservoir and when a single porosity model is used. Permeability obtained from well testing is the effective permeability in the drainage area of each well while permeability from core analysis does not represent the property of a heterogeneous reservoir. Numerical simulation of the fluid flow in the field then validated the permeability values from well testing. Introduction Reaching to higher recovery factor during the production of a reservoir requires a relatively accurate reservoir description. Reservoir description has a significant effect on the design, operation and economic success of optimum depletion or application of any EOR method. Using pressure transient test for describing reservoir heterogeneity has been the subject of several authors. Lefkovites et al.1studied the behavior of bounded reservoirs composed of stratified layers communicating only through the well bore. Russel and Prates 2 studied the practical aspects of interflow cross flow. Kazemi and Seth 3 studied the effect of anisotropy and stratification on pressure transient analysis of wells with restricted flow entry. Bixel et al. 4obtained solutions for the pressure behavior of a well located near a linear discontinuity where the reservoir properties are uniform on either side. Heterogeneities may be small scale as in carbonate reservoirs where the rock has fractures or may be large scale such as faults, fluid contacts, thickness changes, lithology changes and multi layers with different properties in each layer. Warren and Root 5, and Kazemi 6 studied the transient testing in naturally fractured reservoirs. Integrated well testing and geological efforts fo building a static model were presented by some authors. Ayestaran and Nurmi 7 presented a reservoir description of a heterogeneous reservoir using well testing. They confirmed the existence of reservoir boundaries, faults, and low permeability region in a reservoir. Massonnat and Bandizoil 8 integrated the geological and well test data in order to construct a better modeling of a heterogeneous reservoir. Osman 9studied the effects of geometry and the type of reservoir boundaries on drawdown testing. Kabir 10used the integration of well test and geological data in construction of a geological model for the Greater Burgan field of Kuwait.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».