| DOI: | 10.1306/01031110110
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| Scopus记录号: | 2-s2.0-80051987632
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| 论文题名: | Bayesian networks for prospect analysis in the North Sea |
| 作者: | Martinelli G.; Eidsvik J.; Hauge R.; Førland M.D.
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| 刊名: | AAPG Bulletin
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| ISSN: | 0149-1835
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| EISSN: | 1558-9565
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| 出版年: | 2011
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| 发表日期: | 2011
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| 卷: | 95, 期:8 | | 起始页码: | 1423
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| 结束页码: | 1442
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| 语种: | 英语
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| Scopus关键词: | Conditional probabilities
; Decision problems
; Decision-making problem
; Evaluation criteria
; Flexible framework
; Gain information
; Geologic process
; Graphic models
; North Sea
; Oil and gas exploration
; Statistical computations
; Statistical modeling
; Trap levels
; Value of perfect information
; Abandoned wells
; Distributed parameter networks
; Hydrocarbons
; Inference engines
; Intelligent networks
; Offshore oil wells
; Petroleum prospecting
; Problem solving
; Visualization
; Bayesian networks
; Bayesian analysis
; computer simulation
; decision making
; drilling
; exploration
; gas field
; hydrocarbon
; numerical model
; oil field
; reservoir
; statistical analysis
; Atlantic Ocean
; North Sea
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| Scopus学科分类: | Energy
; Earth and Planetary Sciences
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| 英文摘要: | We propose a flexible framework for evaluating prospect dependencies in oil and gas exploration and for solving decisionmaking problems in this context. The model uses a Bayesian network (BN) for encoding the dependencies in a geologic system at source, reservoir, and trap levels. We discuss different evaluation criteria that allow us to formulate specific decision problems and solve these within the BN framework. The BN model offers a realistic graphic model for capturing the underlying causal geologic process and allows fast statistical computations of marginal and conditional probabilities. We illustrate the use of our BN model by considering two situations. In the first situation, we wish to gain information about an area where hydrocarbons have been discovered, and use the value of perfect information to determine which locations are the best to drill. In the second situation, we consider the problem of abandoning an area when only dry wells are drilled. For this latter, we use an abandoned revenue criterion to determine the drilling locations. The application is from the North Sea. Our main focus is the description, visualization, and interpretation of the results for relating the statistical modeling to the local understanding of the geology. Copyright © 2011. The American Association of Petroleum Geologists. |
| URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-80051987632&doi=10.1306%2f01031110110&partnerID=40&md5=b40b11f25b16ae6fea4cc829631641e3
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| Citation statistics: |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/13404
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| Appears in Collections: | 过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响
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| Recommended Citation: | |
Martinelli G.,Eidsvik J.,Hauge R.,et al. Bayesian networks for prospect analysis in the North Sea[J]. AAPG Bulletin,2011-01-01,95(8)
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