Abo Bibliothek: Guest
International Journal for Uncertainty Quantification

Erscheint 6 Ausgaben pro Jahr

ISSN Druckformat: 2152-5080

ISSN Online: 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

COMPARISON OF RISK ANALYSIS METHODOLOGIES IN A GEOSTATISTICAL CONTEXT: MONTE CARLO WITH JOINT PROXY MODELS AND DISCRETIZED LATIN HYPERCUBE

Volumen 8, Ausgabe 1, 2018, pp. 23-41
DOI: 10.1615/Int.J.UncertaintyQuantification.2018019782
Get accessGet access

ABSTRAKT

During the development of petroleum fields, uncertainty quantification is essential to base decisions. Several methods are presented in the literature, but its choice must agree with the complexity of the case study to ensure reliable results at minimum computational costs. In this study, we compared two risk analysis methodologies applied to a complex reservoir model comprising a large set of geostatistical realizations: (1) a generation of scenarios using the discretized Latin hypercube sampling technique combined with geostatistical realizations (DLHG) and (2) a generation of scenarios using the Monte Carlo sampling technique combined with joint proxy models, entitled the joint modeling method (JMM). For a reference response, we assessed risk using the pure Monte Carlo sampling combined with flow simulation using a very high sampling number. We compared the methodologies, looking at the (1) accuracy of the results, (2) computational cost, (3) difficulty in the application, and (4) limitations of the methods. Our results showed that both methods are reliable but revealed limitations in the JMM. Due to the way the JMM captures the effect of a geostatistical uncertainty, the number of required flow simulation runs increased exponentially and became unfeasible to consider more than 10 realizations. The DLHG method showed advantages in such a context, namely, because it generated precise results from less than half of the flow simulation runs, the risk curves were computed directly from the flow simulation results (i.e., a proxy model was not needed), and incorporated hundreds of geostatistical realizations. In addition, this method is fast, straightforward, and easy to implement.

REFERENZIERT VON
  1. Schiozer Denis José, de Souza dos Santos Antonio Alberto, de Graça Santos Susana Margarida, von Hohendorff Filho João Carlos, Model-based decision analysis applied to petroleum field development and management, Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles, 74, 2019. Crossref

  2. Borges Jéssica Ferreira, de Paula Valdeir Francisco, Evangelista Francisco, Bezerra Luciano Mendes, Reliability and uncertainty quantification of the net section tension capacity of cold-formed steel angles with bolted connections considering shear lag, Advances in Structural Engineering, 24, 7, 2021. Crossref

  3. Mahjour Seyed Kourosh, Mendes da Silva Luís Otávio, Meira Luis Augusto Angelotti, Coelho Guilherme Palermo, de Souza dos Santos Antonio Alberto, Schiozer Denis José, Evaluation of unsupervised machine learning frameworks to select representative geological realizations for uncertainty quantification, Journal of Petroleum Science and Engineering, 209, 2022. Crossref

  4. Mahjour Seyed Kourosh, Santos Antonio Alberto Souza, Correia Manuel Gomes, Schiozer Denis José, Developing a workflow to select representative reservoir models combining distance-based clustering and data assimilation for decision making process, Journal of Petroleum Science and Engineering, 190, 2020. Crossref

  5. Mirzaei-Paiaman Abouzar, Santos Susana M.G., Schiozer Denis J., A review on closed-loop field development and management, Journal of Petroleum Science and Engineering, 201, 2021. Crossref

Digitales Portal Digitale Bibliothek eBooks Zeitschriften Referenzen und Berichte Forschungssammlungen Preise und Aborichtlinien Begell House Kontakt Language English 中文 Русский Português German French Spain