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International Journal for Uncertainty Quantification

年間 6 号発行

ISSN 印刷: 2152-5080

ISSN オンライン: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

EFFECTIVE PARAMETRIZATION FOR RELIABLE RESERVOIR PERFORMANCE PREDICTIONS

巻 2, 発行 3, 2012, pp. 259-278
DOI: 10.1615/Int.J.UncertaintyQuantification.2012003765
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要約

The purpose of reservoir modeling and simulation is to predict reservoir performance for development and depletion planning. Despite decades of research, efficient and reliable reservoir performance predictions are still a challenge in practice. In this paper, we present an overview of reservoir modeling as it is commonly practiced today and the challenges it faces. More specifically, we focus on the challenges posed by the large amount of uncertainty inherent in the characterization of reservoirs that are heterogeneous at multiple scales. We discuss the practical implications of these challenges and recent developments toward addressing them. In particular, we examine the need for effective parametrization of geologic concepts and related recent advances in parametrization and parameter reduction techniques, including their advantages and limitations. Using numerical examples from two different depositional environments, we show that effective parametrization can be achieved by taking advantage of the geologic hierarchy underlying most geologic concepts and a general understanding of the impact of geologic features on fluid flow. Finally, we propose an approach to systematically derive fit-for-purpose parametrization for practical reservoir modeling problems.

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