图书馆订阅: Guest
Begell Digital Portal Begell 数字图书馆 电子图书 期刊 参考文献及会议录 研究收集
国际不确定性的量化期刊
影响因子: 3.259 5年影响因子: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN 打印: 2152-5080
ISSN 在线: 2152-5099

Open Access

国际不确定性的量化期刊

DOI: 10.1615/Int.J.UncertaintyQuantification.2018019782
pages 23-41

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

Susana Santos
University of Campinas
Ana Teresa F. S. Gaspar
Department of Energy, School of Mechanical Engineering, University of Campinas, Brazil
Denis J. Schiozer
Department of Energy, School of Mechanical Engineering, University of Campinas, Brazil

ABSTRACT

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.


Articles with similar content:

A GRADIENT-ENHANCED SPARSE GRID ALGORITHM FOR UNCERTAINTY QUANTIFICATION
International Journal for Uncertainty Quantification, Vol.5, 2015, issue 5
Brendan Harding, Jouke H. S. de Baar
3-D NATURAL CONVECTION BENCHMARK IN AN ENCLOSURE USING PENATLY AND PRESSURE PROJECTION METHODS
ICHMT DIGITAL LIBRARY ONLINE, Vol.4, 2001, issue
Juan C. Heinrich, Darrell W. Pepper
PROGRAMMABLE CONTROLLERS PROGRAM GENERATION USING A TEXTUAL INTERFACE
Flexible Automation and Intelligent Manufacturing, 1997:
Proceedings of the Seventh International FAIM Conference, Vol.0, 1997, issue
Antonio Batocchio, Orlando Duran
EFFECTIVE SIGNAL DETECTION FOR THE SPATIAL MULTIPLEXING MIMO SYSTEMS
Telecommunications and Radio Engineering, Vol.77, 2018, issue 13
V. B. Kreyndelin , А. P. Shumov, М. G. Bakulin, V. V. Vityazev
VPS: VORONOI PIECEWISE SURROGATE MODELS FOR HIGH-DIMENSIONAL DATA FITTING
International Journal for Uncertainty Quantification, Vol.7, 2017, issue 1
Marta D'Elia, Mohamed S. Ebeida, Ahmad Rushdi, Eric T. Phipps, Laura P. Swiler