Computational intelligence for deepwater reservoir depositional environments interpretation

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{Yu2011716,

• author = "Tina Yu and Dave Wilkinson and Julian Clark and Morgan Sullivan",
• title = "Computational intelligence for deepwater reservoir depositional environments interpretation",
• journal = "Journal of Natural Gas Science and Engineering",
• year = "2011",
• volume = "3",
• number = "6",
• pages = "716--728",
• month = dec,
• note = "Artificial Intelligence and Data Mining",
• keywords = "genetic algorithms, genetic programming, Strongly typed genetic programming, STGP, Deep water reservoir, Stratigraphic interpretation, Depositional environment, Gamma ray interpretation, Computational intelligence, Fuzzy logic, Well log, Co-evolution, Time series, Segmentation, Finite state transducer, Classification rules",
• ISSN = "1875-5100",
• URL = "http://arxiv.org/abs/1301.2638",
• URL = "http://www.cs.mun.ca/~tinayu/Publications_files/1301.2638v1.pdf",
• URL = "http://www.sciencedirect.com/science/article/pii/S1875510011000849",
• DOI = "doi:10.1016/j.jngse.2011.07.014",
• size = "13 pages",
• abstract = "Predicting oil recovery efficiency of a deepwater reservoir is a challenging task. One approach to characterise a deepwater reservoir and to predict its producibility is by analysing its depositional information. This research proposes a deposition-based stratigraphic interpretation framework for deep water reservoir characterisation. In this framework, one critical task is the identification and labelling of the stratigraphic components in the reservoir, according to their depositional environments. This interpretation process is labour intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher's workload and to produce more consistent results, we have developed a novel methodology to automate this process using various computational intelligence techniques. Using a well log data set, we demonstrate that the developed methodology and the designed workflow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.",
• notes = "Chevron",

}

Genetic Programming entries for Tina Yu Dave Wilkinson Julian Clark Morgan Sullivan

Citations