Stochastic regridding of geological models for flow simulation
Notice bibliographique
Résumé
Research Article| December 01, 2015 Stochastic regridding of geological models for flow simulation Saina Lajevardi; Saina Lajevardi Department of Civil & Environmental Engineering University of Alberta 5-052 Markin/CNRL NREF Edmonton, AB T6G 2W2 Search for other works by this author on: GSW Google Scholar Clayton V. Deutsch Clayton V. Deutsch School of Mining and Petroleum Engineering University of Alberta 3-133 Markin/CNRL NREF Edmonton, AB T6G 2W2 Search for other works by this author on: GSW Google Scholar Author and Article Information Saina Lajevardi Department of Civil & Environmental Engineering University of Alberta 5-052 Markin/CNRL NREF Edmonton, AB T6G 2W2 Clayton V. Deutsch School of Mining and Petroleum Engineering University of Alberta 3-133 Markin/CNRL NREF Edmonton, AB T6G 2W2 Publisher: Canadian Society of Petroleum Geologists Received: 20 Nov 2014 Accepted: 26 May 2015 First Online: 13 Jul 2017 Online Issn: 2368-0261 Print Issn: 0007-4802 © the Society of Canadian Petroleum Geologists Bulletin of Canadian Petroleum Geology (2015) 63 (4): 374–392. https://doi.org/10.2113/gscpgbull.63.4.374 Article history Received: 20 Nov 2014 Accepted: 26 May 2015 First Online: 13 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Saina Lajevardi, Clayton V. Deutsch; Stochastic regridding of geological models for flow simulation. Bulletin of Canadian Petroleum Geology 2015;; 63 (4): 374–392. doi: https://doi.org/10.2113/gscpgbull.63.4.374 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of Canadian Petroleum Geology Search Advanced Search Abstract Regridding geological models to a higher resolution for flow simulation is an important problem in geostatistical modeling. For practical reasons, over a large area, models can only be built at a relatively coarse resolution. Subsequently, the resolution of specified regions of interest must be increased before upscaling for flow modeling. The construction of a high-resolution model of the entire reservoir at the beginning of the evaluation may be impractical because of computational and time constraints. It is standard practice to implement nearest neighbor interpolation to increase the resolution of models. Although it is a simple practical solution, nearest neighbor interpolation introduces spatial continuity artifacts that are often unrealistic. This paper proposes an automatic stochastic regridding approach based on simulation. The simulation is conditioned to the initial coarse resolution model/realization. The process includes the extraction of specified regions of interest, definition of corresponding local variography, and implementation of Sequential Gaussian Simulation (SGS) and/or Sequential Indicator Simulation (SIS) to characterize continuous and categorical variables, respectively. In each specified region, the local variography can be defined by either implementing automatic fitting algorithms or assigning the global variography initially used to build the coarse resolution model. The regridding process is automated. The advantage of this approach over the conventional nearest neighbor interpolation is in the improvement in the realistic spatial variability features of small scale geologic heterogeneity. The benefits of obtaining a proper regridded model are discussed in a case study of a fluvial reservoir in the McMurray formation. One of the main reasons for generating high resolution models is in the appropriate characterization of small scale impermeable geobodies such as remnant shales. The coarse resolution models are not able to properly characterize the small scale geologic features of the shales; more amount of information is required to characterize smaller scale features. The metric of performance considered is the effective vertical permeability. The automated stochastic regridding workflow described in this paper is available on a Fortran platform with additional scripting which will be distributed upon request. Note that the terms “regridding” and “stochastic regridding” are used interchangeably and both refer to the proposed workflow of modeling at higher resolution. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0,004 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».