Lithologic Reservoirs ›› 2023, Vol. 35 ›› Issue (6): 117-126.doi: 10.12108/yxyqc.20230613

• PETROLEUM EXPLORATION • Previous Articles     Next Articles

Determination of effective permeability of granitic buried-hill fractured reservoirs in Bongor Basin,Chad

GUO Haifeng1, XIAO Kunye2, CHENG Xiaodong1, DU Yebo2, DU Xudong1, NI Guohui1, LI Xianbing2, JI Ran1   

  1. 1. International Branch, China National Logging Corporation, Beijing 102206, China;
    2. Research Institute of Science and Technology Co., Ltd., CNPC, Beijing 100083, China
  • Received:2023-04-17 Revised:2023-06-27 Online:2023-11-01 Published:2023-11-07

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Abstract: A new effective permeability calculation method was proposed for the granitic buried-hill reservoirs in Bongor Basin of Chad based on drilling,logging,and oil testing data. Apparent effective permeability was derived from well testing results,serving as labeled data for the sample dataset. The method relies primarily on domain knowledge and mechanism-driven models,and is supplemented by machine learning to establish feature logs. The dual-prediction model XGBoost+KNN was employed to calculate apparent effective permeability,with SHAP values used for model interpretability analysis. The results show that:（1）Permeability indicator logs,namely apparent acoustic impedance and porosity,were utilized to select 26 effective testing intervals from19 wells,based on their intersection with production index. The well testing results were converted into apparent permeability values,ranging from 0.01 to 1 601.50 m D,resulting in a dataset comprising 51 348 depth data points and 14 input logs. The sample dataset adequately covers the main buried hill zones and incorporates input log response characteristics from diverse lithologies,reservoir qualities,and well testing production performance,thus achieving sufficient representativeness.（2）The XGBoost model effectively uses various logs,inclu-ding the apparent acoustic impedance log,normalized depth log representing the vertical zoning characteristics of buried-hill reservoirs,density log,window average of natural gamma ray log,acoustic log,neutron log,window average of neutron-density porosity difference log,deep and shallow resistivity log,and window standard deviation of natural gamma ray log. The model’s predictions exhibit consistency with qualitative understanding of buried-hill reservoir quality and demonstrate higher accuracy compared to the KNN model.（3）The granitic buried-hill reservoirs of Bongor Basin,Chad,with effective permeability greater than 1.00 m D,are classified as effective reservoirs,while those exceeding 50.00 mD are considered good reservoirs. The calculated results using the proposed method are consistent with the well testing results.

Key words: granitic buried hill, fractured reservoir, apparent effective permeability, machine learning, sample dataset building, XGBoost, reservoir parameter modeling, Bongor Basin

CLC Number: 

  • TE122
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