Lithologic Reservoirs ›› 2024, Vol. 36 ›› Issue (6): 12-22.doi: 10.12108/yxyqc.20240602

• PETROLEUM EXPLORATION • Previous Articles     Next Articles

Prediction of shale formation pore pressure based on Zebra Optimization Algorithm-optimized support vector regression

ZHAO Jun1, LI Yong1, WEN Xiaofeng2, XU Wenyuan2, JIAO Shixiang1   

  1. 1. School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China;
    2. Changqing Branch, China Petroleum Group Logging Co., Ltd, Xi'an 710201, China
  • Received:2024-05-17 Revised:2024-07-11 Online:2024-11-01 Published:2024-11-04

Abstract: To address the complexity and heterogeneity of the pore structure of seventh member of Triassic Yanchang Formation(Chang 7 member)in Longdong area,a method based on the Zebra Optimization Algorithm-optimized Support Vector Regression(ZOA-SVR)model was proposed. This method utilizes formation pressure test data and conventional well logging curves. It was applied in actual wells,and compared with other machine learning models,as well as conventional methods for predicting formation pressure. The results show that:(1)The ZOA-SVR model uses actual formation pressure data as target variables,selecting seven input feature parameter,such as depth, sonic transit time,density,compensated neutron,natural gamma,deep resistivity,and clay content,associated with shale formation pressure data correlation above 0.7 in the study area. The model was trained with 40 samples, validated with 5-fold cross-validation,and optimized with 10 initial Zebra populations and a maximum of 70 iterations. After optimizing penalty factors and kernel parameters,the model achieved a fit indicator R2 of 0.942. Predicted formation pressure data had absolute errors below 1 MPa on both training and test sets,with an aver age relative error of 2.42% compared to measured data.(2)The ZOA-SVR model demonstrated significant advantages in predicting of Chang 7 Member formation pressure in the study area compared to models based on Particle Swarm Optimization,Grey Wolf Algorithm,and Ant Colony Algorithm. It shows a better parameter adjustment and optimization capabilities,with coefficient of determination increases of 0.209,0.327,and 0.142 respectively. It also exhibited higher accuracy in pressure prediction compared to Equivalent Depth Method,Eaton Method,and Effective Stress Method,reducing relative errors by 32.53%,15.31%,and 5.91% respectively. (3)Application of the ZOA-SVR model in actual wells indicated stable vertical distribution of chang 7 Member formation pressure in the study area. Pressure in mud shale sections is higher than the sandstone sections,with pressure coefficients mainly range from 0.80 to 0.90,indicating an overall abnormally low pressure environment consistent with actual formation conditions.

Key words: shale, formation pressure, zebra optimization algorithm, support vector regression, machine lear-ning, well logging curves, Chang 7 member, Triassic, Longdong area

CLC Number: 

  • P618.13
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