Petroleum Science

, Volume 8, Issue 1, pp 49–54

Identification of the Quaternary low gas-saturation reservoirs in the Sanhu area of the Qaidam Basin, China

Authors

  • Xiongyan Li
    • State Key Laboratory of Petroleum Resource and ProspectingChina University of Petroleum
    • Key Laboratory of Earth Prospecting and Information TechnologyChina University of Petroleum
    • State Key Laboratory of Petroleum Resource and ProspectingChina University of Petroleum
    • Key Laboratory of Earth Prospecting and Information TechnologyChina University of Petroleum
    • Department of Computer Science and TechnologyChina University of Petroleum
  • Jinyu Zhou
    • Exploration and Development InstituteChangqing Petroleum Company
  • Xu He
    • Department of Computer Science and TechnologyChina University of Petroleum
  • Yihan Chen
    • State Key Laboratory of Petroleum Resource and ProspectingChina University of Petroleum
    • Key Laboratory of Earth Prospecting and Information TechnologyChina University of Petroleum
  • Hongyan Yu
    • State Key Laboratory of Petroleum Resource and ProspectingChina University of Petroleum
    • Key Laboratory of Earth Prospecting and Information TechnologyChina University of Petroleum
Article

DOI: 10.1007/s12182-011-0114-4

Cite this article as:
Li, X., Li, H., Zhou, J. et al. Pet. Sci. (2011) 8: 49. doi:10.1007/s12182-011-0114-4

Abstract

Low gas-saturation reservoirs are gas bearing intervals whose gas saturation is less than 47%. They are common in the Quaternary of the Sanhu area in the Qaidam Basin. Due to the complex genesis mechanisms and special geological characteristics, the logging curves of low gas-saturation reservoirs are characterized by ambiguity and diversity, namely without significant log response characteristics. Therefore, it is particularly difficult to identify the low gas-saturation reservoirs in the study area. In addition, the traditional methods such as using the relations among lithology, electrical property, physical property and gas bearing property, as well as their threshold values, can not effectively identify low gassaturation reservoirs. To solve this problem, we adopt the decision tree, support vector machine and rough set methods to establish a predictive model of low gas-saturation reservoirs, which is capable of classifying a mass of multi-dimensional and fuzzy data. According to the transparency of learning processes and the understandability of learning results, the predictive model was also revised by absorbing the actual reservoir characteristics. Practical applications indicate that the predictive model is effective in identifying low gas-saturation reservoirs in the study area.

Key words

Sanhu areaQaidam Basinlow gas-saturation reservoirdecision treesupport vector machinerough setpredictive modelidentification

Copyright information

© China University of Petroleum (Beijing) and Springer-Verlag Berlin Heidelberg 2011