Iterative Integration of Dynamic Data in Reservoir Models
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Résumé
Iterative Integration of Dynamic Data in Reservoir Models Tarun Kashib; Tarun Kashib U. of Calgary Search for other works by this author on: This Site Google Scholar Sanjay Srinivasan Sanjay Srinivasan U. of Texas at Austin Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. Paper Number: SPE-84592-MS https://doi.org/10.2118/84592-MS Published: October 05 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Kashib, Tarun, and Sanjay Srinivasan. "Iterative Integration of Dynamic Data in Reservoir Models." Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. doi: https://doi.org/10.2118/84592-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 Annual Technical Conference and Exhibition Search Advanced Search AbstractConditioning reservoir models to dynamic data such as historical production response is challenging because of the complexity of the relationship between the model parameters and the response variable. The focus of this paper is to present a methodology for efficiently integrating dynamic production data into reservoir models. In contrast to some of the other methods, the proposed methodology attempts to quantify the information in production data pertaining to reservoir heterogeneity in a probabilistic manner. The conditional probability representing the uncertainty in permeability at a location is iteratively updated to account for the additional information contained in the dynamic response data. A localized perturbation procedure is also presented to account for multiple flow regions within the reservoir. Such an improved scheme utilizes a set of locally varying deformation parameters to guide the iterative updating process in order to obtain a global history match.IntroductionGeostatistics provides a framework for integrating diverse types of reservoir specific data in order to develop multiple realizations of the reservoir. The acquired data may display spatial dependency only (static data) such as well logs, core measurements, etc. or may display joint space-time dependency (dynamic data) such as production response, time-lapse seismic, etc. Several algorithms are available to condition reservoir models to static data, however conditioning to dynamic data is complex because of the non-linear relationship between the input model parameters (spatially varying petrophysical properties) and the output response (e.g. well pressure as a function of time). Manually adjusting reservoir models so that they reflect the dynamic response information accurately is very time consuming and tedious. In addition, such manual adjustments might lead to reservoir models that do not exhibit the correct spatial covariance structure. Consequently, though the adjusted reservoir models may display an excellent match to the historical production records, they may yield totally erroneous future predictions of reservoir performance. The ability to forecast future production scenarios accurately is the ultimate objective of any reservoir modeling exercise.This problem could be alleviated if the historical dynamic data are integrated into the reservoir model construction step such that the final model is conditioned to all the available static data as well as the dynamic data. Provided the rules for integrating production information into geologic models can be clearly established through calibration, incremental information derived from production data collected during the productive life of the field can be used to continuously update reservoir models. Keywords: integration, history matching, permeability field, dynamic data, reservoir simulation, probability, realization, reservoir model, spe 84592, information Subjects: Reservoir Simulation, Evaluation of uncertainties, History matching 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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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 | 0,001 |
| 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 découle