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Prospect Evaluation of the Cretaceous Yageliemu Clastic Reservoir Based on Geophysical Log Data: A Case Study from the Yakela Gas Condensate Field, Tarim Basin, China

Author

Listed:
  • Wakeel Hussain

    (School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
    Department of Petroleum Engineering, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Muhsan Ehsan

    (Department of Earth and Environmental Science, Bahria University, Islamabad 44000, Pakistan)

  • Lin Pan

    (Department of Petroleum Engineering, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Xiao Wang

    (Department of Petroleum Engineering, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Muhammad Ali

    (School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Shahab Ud Din

    (Faculty of Petroleum Engineering, China University of Petroleum, Beijing 102249, China)

  • Hadi Hussain

    (Department of Petroleum Engineering, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Ali Jawad

    (School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Shuyang Chen

    (SinoPEC Northwest Oilfield Company, Urumqi 830011, China)

  • Honggang Liang

    (SinoPEC Northwest Oilfield Company, Urumqi 830011, China)

  • Lixia Liang

    (SinoPEC Northwest Oilfield Company, Urumqi 830011, China)

Abstract

This paper evaluated the oil and gas potential of the Cretaceous Yageliemu clastic reservoir within the Yakela condensed gas field lying in the Kuqa Depression, Tarim Basin, China. The petrophysical properties of the interest zones in the Kuqa area were characterized using geophysical logs from five wells. The results reveal that the gas-bearing zones are characterized by high resistivity, good permeability (K) and effective porosity (Φeff), low water saturation (Sw), and low shale concentration (Vsh), reflecting clean sand. The shale distribution model showed that these shales have no major influence on porosity and fluid saturation. The average shale volume, average effective porosity, and hydrocarbon saturation indicate that the Cretaceous Yageliemu Formation in the studied area contains prospective reservoir properties. The spatial distribution of petrophysical parameters, reservoir rock typing (RRT), and lithofacies were analyzed using the cross plots of litho saturation (volumetric analysis), iso-parametric representations of the petrophysical characteristics, cluster analysis, and self-organizing feature maps, respectively. The southeastern and northeastern regions of the research area should be ignored because of their high water and shale concentrations. The sediments in the southwest and northwest include the most potential reservoir intervals that should be considered for the future exploration and development of oil and gas fields in the study area.

Suggested Citation

  • Wakeel Hussain & Muhsan Ehsan & Lin Pan & Xiao Wang & Muhammad Ali & Shahab Ud Din & Hadi Hussain & Ali Jawad & Shuyang Chen & Honggang Liang & Lixia Liang, 2023. "Prospect Evaluation of the Cretaceous Yageliemu Clastic Reservoir Based on Geophysical Log Data: A Case Study from the Yakela Gas Condensate Field, Tarim Basin, China," Energies, MDPI, vol. 16(6), pages 1-25, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2721-:d:1097353
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    References listed on IDEAS

    as
    1. Chinedu S. Orji & Etim D. Uko & Iyeneomie Tamunobereton-ari, 2019. "Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria,Using Well-Log Data," Malaysian Journal of Geosciences (MJG), Zibeline International Publishing, vol. 3(2), pages 33-42, May.
    2. Nadir Fawad & Taixun Liu & Daidu Fan & Qazi Adnan Ahmad, 2022. "Sedimentary Facies Analysis of the Third Eocene Member of Shahejie Formation in the Bonan Sag of Bohai Bay Basin (China): Implications for Facies Heterogeneities in Sandstone Reservoirs," Energies, MDPI, vol. 15(17), pages 1-29, August.
    3. Mazahir Hussain & Shuang Liu & Umar Ashraf & Muhammad Ali & Wakeel Hussain & Nafees Ali & Aqsa Anees, 2022. "Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type," Energies, MDPI, vol. 15(12), pages 1-15, June.
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    Cited by:

    1. Nafees Ali & Xiaodong Fu & Jian Chen & Javid Hussain & Wakeel Hussain & Nosheen Rahman & Sayed Muhammad Iqbal & Ali Altalbe, 2024. "Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves," Energies, MDPI, vol. 17(15), pages 1-22, July.

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