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国际不确定性的量化期刊

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ISSN 打印: 2152-5080

ISSN 在线: 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

REDUCING FRACTURE PREDICTION UNCERTAINTY BASED ON TIME-LAPSE SEISMIC (4D) AND DETERMINISTIC INVERSION ALGORITHM

卷 9, 册 2, 2019, pp. 187-204
DOI: 10.1615/Int.J.UncertaintyQuantification.2019027680
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摘要

The uncertainty of hydraulic fracture is high due to the complex geological features of which there is limited accurate understanding, and the limitations of the fracture diagnosis method. However, hydraulic fractures are one of the main driving forces for oilfields to improve economic benefit and important reference imformation for further development and adjustment of oilfields. Therefore, reducing fracture morphology uncertainty is a key challenge for the further development of oilfields. To improve this situation, we present a novel method based on the time-lapse (4D) seismic and discrete network deterministic inversion (DNDI) algorithm for mapping the geometry of hydraulic fracture. The time-lapse (4D) seismic method can provide spatial and dynamic change of reservoir; this information is used by DNDI to optimize fracture geometry continually, where the embedded discrete fracture model (EDFM) is implied to simulate reservoir production, and objective function is constructed using Bayesian theory for reaching iterative convergence quickly. An uncertainty analysis of results based on the posterior probability is also presented in this paper. Finally, this method has been validated in different scale study cases.

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