Journal of Southwest Petroleum University(Science & Technology Edition) ›› 2023, Vol. 45 ›› Issue (4): 155-163.DOI: 10.11885/j.issn.1674-5086.2021.04.30.04

• OIL AND GAS ENGINEERING • Previous Articles     Next Articles

Prediction Method of Core Permeability and Fracture Aperture Based on Machine Learning

CHEN Lin1, LI Pengwu2, ZHANG Shaojun1, LI Zhijie2, DU Xiaoyong1   

  1. 1. Research Institute of Oil and Gas Engineering, Tarim Oilfield Branch, PetroChina, Korla, Xinjiang 841000, China;
    2. National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan 610500, China
  • Received:2021-04-30 Online:2023-08-10 Published:2023-07-18

Abstract: Stress sensitivity is one of the main reasons for the damage of tight sandstone gas reservoirs. The prediction of the change law of core permeability and fracture aperture under stress sensitivity damage is always the key point in the field of tight sandstone reservoir protection. Based on the stress-sensitive experiment and survey data, the core permeability prediction model and fracture opening prediction model were established by using machine learning multiple linear regression algorithm coupled with the confining pressure permeability relationship model and the K-p function parameter prediction model. The accuracy of the model was tested by correlation coefficient, root mean square error and relative error. The results show that the average correlation coefficient of the prediction results of the confining pressure permeability model in fractured and nonfractured cores is greater than 0.96. The prediction results of K-p function parameter prediction model show that the root mean square error in fractured cores is higher than that in non-fractured cores, but the relative error of fractured cores is lower than that of non-fractured cores. It shows that the permeability prediction model is more suitable for fractured cores. The coefficient of determination between the fracture opening prediction model and the measured value is 0.978, indicating a high prediction accuracy. The permeability prediction model and fracture aperture prediction model can provide guidance for the exploitation and protection of tight sandstone reservoir.

Key words: machine learning, permeability prediction, fracture aperture, stress sensitivity, tight sandstone, multiple linear regression

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