Intelligent prediction method for fracture pressure based on stacking ensemble algorithm

Fracture pressure is an important reference for wellbore stability analysis and hydraulic fracturing. Considering the low prediction accuracy, significant deviations, and limited applicability of traditional methods for predicting formation fracture pressure, this paper proposes an intelligent predi...

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Bibliographic Details
Published in:Geomechanics and geophysics for geo-energy and geo-resources. Vol. 9; no. 1; pp. 1 - 24
Main Authors: Zhang, Hao, Ren, Yangfeng, Zhang, Yan, Zheng, Shuangjin
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.12.2023
Springer
Subjects:
Energy
Engineering
Environmental Science and Engineering
Foundations
Fracture pressure
Geoengineering
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Hydraulics
Intelligent prediction
Logging data
Sensitivity analysis
Stacking ensemble algorithm
Stacking ensemble algorithm
Logging data
Sensitivity analysis
Intelligent prediction
Fracture pressure
ISSN:2363-8419, 2363-8427
Online Access:Get full text
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Summary:Fracture pressure is an important reference for wellbore stability analysis and hydraulic fracturing. Considering the low prediction accuracy, significant deviations, and limited applicability of traditional methods for predicting formation fracture pressure, this paper proposes an intelligent prediction method for fracture pressure using conventional well logging data based on the Stacking ensemble algorithm. The base learners of the model include RF, KNN, and LSTM algorithms with low correlation. The meta-learner adopts the XGBoost algorithm. The effectiveness of the model is validated using the fracture pressure data from Dagang Oilfield. The prediction results indicate that the stacking algorithm outperforms individual algorithms. After optimization with genetic algorithm, the R 2 of the stacking model is 0.989, RMSE is 0.009%, and MAE is 0.32%. The global sensitivity analysis results show that AC and DEN in the well logging data have higher sensitivity to the fracture pressure. When using intelligent fracture pressure prediction methods, it is essential to ensure the accuracy of AC and DEN data. The work demonstrates the reliability and effectiveness of the method proposed for the intelligent prediction of fracturing pressure using conventional well logging data through Stacking ensemble algorithm to overcome the limitations of traditional methods. Article highlights An intelligent prediction method of fracture pressure based on conventional logging data was proposed. The prediction model is based on Stacking ensemble algorithm, which outperforms individual algorithms in terms of performance. Global sensitivity analysis shows that AC and DEN in logging data exhibit high sensitivity to fracture pressure.
ISSN:2363-8419
2363-8427
DOI:10.1007/s40948-023-00690-5