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International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 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

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DATA-DRIVEN CALIBRATION OF P3D HYDRAULIC FRACTURING MODELS

Volume 10, Edição 4, 2020, pp. 375-398
DOI: 10.1615/Int.J.UncertaintyQuantification.2020033602
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RESUMO

Modeling the propagation of a hydraulic fracture is challenging due to the complex nonlinear equations and the presence of multiple scales behavior at the fracture tips. These complexities have motivated researchers to propose simplified models relying on constrained fracture geometry patterns. Amongst them, in the oil and gas industry domain, the Pseudo-3D (P3D), that computes fracture evolution in reservoirs confined by symmetric stress barriers, is frequently employed. The different assumptions made to obtain the P3D model lead to inaccurate predictions, like the overestimation of fracture height for important operating conditions. To correct such drawbacks, we propose a model discrepancy term in a multi-fidelity approach. The main difficulty associated is to construct a mapping between the inputs and outputs that faithfully represents the error between high and low fidelity models. In this paper, we investigated the efficiency modeling such discrepancy by using Gaussian processes and artificial neural networks. In this analysis, we employ three data sets with different input-output dimensions. The best model obtained is then used for carrying out an Uncertainty Quantification analysis aiming at identifying the impact of parametric uncertainty in fracture propagation computation.

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