Prediction of Shale Gas Production by Hydraulic Fracturing in Changning Area Using Machine Learning Algorithms

Machine learning has been widely used for the production forecasting of oil and gas fields due to its low computational cost. This paper studies the productivity prediction of shale gas wells with hydraulic fracturing in the Changning area, Sichuan Basin. Four different methods, including multiple l...

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Bibliographic Details
Published in:Transport in porous media 2023-08, Vol.149 (1), p.373-388
Main Authors: Li, Dongshuang, You, Shaohua, Liao, Qinzhuo, Sheng, Mao, Tian, Shouceng
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Civil Engineering
Classical and Continuum Physics
Earth and Environmental Science
Earth Sciences
Errors
Forecasting
Gas wells
Geotechnical Engineering & Applied Earth Sciences
Hydraulic fracturing
Hydrogeology
Hydrology/Water Resources
Industrial Chemistry/Chemical Engineering
Machine learning
Optimization
Parameters
Particle swarm optimization
Productivity
Shale gas
Support vector machines
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Summary:Machine learning has been widely used for the production forecasting of oil and gas fields due to its low computational cost. This paper studies the productivity prediction of shale gas wells with hydraulic fracturing in the Changning area, Sichuan Basin. Four different methods, including multiple linear regression (MLR), support vector machine (SVM), random forest (RF) and artificial neural network (ANN) are used, and their performances are compared by the value of the mean absolute percentage error to determine the best method of all. The training and validation results show that the MLR and SVM methods exhibit poor performances with relatively high errors (> 15%), while the ANN and RF methods show obviously better results, where the RF has a median error (~12%) and the ANN has the smallest error (
ISSN:0169-3913
1573-1634
DOI:10.1007/s11242-023-01935-3