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Research on Test and Logging Data Quality Classification for Gas–Water Identification

Author

Listed:
  • Zehou Xiang

    (College of Energy, Chengdu University of Technology, Chengdu 610059, China)

  • Kesai Li

    (College of Energy, Chengdu University of Technology, Chengdu 610059, China)

  • Hucheng Deng

    (College of Energy, Chengdu University of Technology, Chengdu 610059, China
    State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu 610059, China)

  • Yan Liu

    (College of Energy, Chengdu University of Technology, Chengdu 610059, China)

  • Jianhua He

    (College of Energy, Chengdu University of Technology, Chengdu 610059, China)

  • Xiaoju Zhang

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Xianhong He

    (College of Energy, Chengdu University of Technology, Chengdu 610059, China)

Abstract

Tight sandstone oil and gas reservoirs are widely distributed, rich in resources, with a bright prospect for exploration and development in China. Due to multiple evolutions of the structure and sedimentary system, the gas–water distribution laws are complicated in tight sandstone gas reservoirs in the northern Ordos area. It is difficult to identify gas and water layers in the study area. In addition, in the development and production, various factors, such as the failure of the instrument, the difference in construction parameters (injected sand volume, flowback rate), poor test results, and multi-layer joint testing lead to unreliable gas test results. Then, the inaccurate logging responses will be screened by unreliable gas test results for different types of fluids. It is hard to make high-precision fluid logging identification charts or models. Therefore, this article combines gas logging, well logging, testing and other data to research the test and logging data quality classification. Firstly, we select reliable standard samples through the initial gas test results. Secondly, we analyze the four main factors which affect the inaccuracy of gas test results. Thirdly, according to these factors, the flowback rate and the sand volume are determined as the main parameters. Then, we establish a recognition chart of injected sand volume/gas–water ratio. Finally, we proposed an evaluation method for testing quality classification. It provides a test basis for the subsequent identification of gas and water through the second logging interpretation. It also provides a theoretical basis for the exploration and evaluation of tight oil and gas reservoirs.

Suggested Citation

  • Zehou Xiang & Kesai Li & Hucheng Deng & Yan Liu & Jianhua He & Xiaoju Zhang & Xianhong He, 2021. "Research on Test and Logging Data Quality Classification for Gas–Water Identification," Energies, MDPI, vol. 14(21), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6991-:d:664040
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    References listed on IDEAS

    as
    1. Wenrui Shi & Xingzhi Wang & Yuanhui Shi & Aiguo Feng & Yu Zou & Steven Young, 2019. "Application of Dipole Array Acoustic Logging in the Evaluation of Shale Gas Reservoirs," Energies, MDPI, vol. 12(20), pages 1-17, October.
    2. Guanqun Yang & Wenhui Huang & Jianhua Zhong & Ningliang Sun, 2020. "Occurrence, Classification and Formation Mechanisms of the Organic-Rich Clasts in the Upper Paleozoic Coal-Bearing Tight Sandstone, Northeastern Margin of the Ordos Basin, China," Energies, MDPI, vol. 13(11), pages 1-19, May.
    3. Xiaoying Lin & Jianhui Zeng & Jian Wang & Meixin Huang, 2020. "Natural Gas Reservoir Characteristics and Non-Darcy Flow in Low-Permeability Sandstone Reservoir of Sulige Gas Field, Ordos Basin," Energies, MDPI, vol. 13(7), pages 1-17, April.
    4. Cuiqiao Xing & Hongjun Yin & Kexin Liu & Xingke Li & Jing Fu, 2018. "Well Test Analysis for Fractured and Vuggy Carbonate Reservoirs of Well Drilling in Large Scale Cave," Energies, MDPI, vol. 11(1), pages 1-15, January.
    5. Hongyang Chu & Xinwei Liao & Peng Dong & Zhiming Chen & Xiaoliang Zhao & Jiandong Zou, 2019. "An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)," Energies, MDPI, vol. 12(15), pages 1-27, July.
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