Local Updating of Reservoir Properties for Production Data Integration
Notice bibliographique
Résumé
Abstract Reliable reservoir performance forecasts with as little uncertainty as possible are key information for optimal reservoir management tasks. These production forecasts are directly related to the reservoir size and internal porous media properties. There is a need for improved techniques for production data integration to construct realistic reservoir models by using geostatistical techniques. A methodology is proposed that integrates production data into reservoir models by the local updating of porosity and permeability fields. The focus is on conditioning a proposed initial model to injection/production rate and pressure history in an iterative fashion. For each pass, a perturbation location is selected and master point locations are defined and used as reference to calculate the pressure and flow rate sensitivity coefficients subject to changes in porosity and permeability. The optimal changes of porosity and permeability at the master point locations are propagated to the whole grid by kriging. Integrating flow simulation and kriging algorithms within an optimization process constitutes the proposed methodology. This method makes it possible to condition the permeability/porosity distributions to injection/production rate and pressure history data from large reservoirs with complex heterogeneities and changes of well system with time. A field case application demonstrates that the proposed methodology is efficient and practical for large reservoir models. Introduction Many people are working on production data integration and several methods have been proposed. However, there is a challenge to condition reservoir property models to production data for large scale fields accounting for realistic field conditions. Direct calculation schemes are avoided considering that they are often limited to 2-D single-phase flow. Stochastic approaches such as simulated annealing or genetic algorithms require a lot of simulation runs, making them practically unfeasible for large scale application.(1,)(2)(3) Algorithms and software for production data integration based on hydrogeological developments such as sequential self calibration and pilot point methods have not proven applicable in complex reservoir settings with multiphase flow, 3-D structure and changing well conditions.(4) Streamline simulation based methods suffer the same limitations although some papers show that it has been used in large reservoirs.(5) The convergence of results for gradual deformation methods is very slow so that lots of iterations are needed for large 3-D models (6)(7). All the production data integration methods relay on flow simulation. Streamline simulation is commonly proposed as a solution to be used in large 3-D reservoir models due to its computational efficiency and analytical sensitivity coefficient calculation. However, the simplification of streamline simulation may promote computational efficiency at the expenses of accuracy reduction in cases of high heterogeneity with multiphase flow, 3-D structure and changing well conditions. There is a need for a novel computational efficient production data integration method that can be used in large complex 3-D reservoir models with many wells and long production and injection history. Basic Idea and General Procedure of the Proposed Methodology Our basic idea consists on the numerical calculation of the sensitivity coefficients on the basis of two flow simulations - an initial base case and a single sensitivity case.
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 ».