The invention relates to a method for predicting
fracture pressure of a stratified shale extended reach well based on a
machine learning model. The method comprises the following steps: S1, acquiring
well drilling data, collecting an underground
rock core, carrying out an anisotropic shale tensile mechanical experiment along a
bedding direction, and constructing a shale fracture criterion according to an experiment result; s2, constructing a stratified shale extended reach well
fracture pressure model, and correcting and integrating the calculated
fracture pressure value in combination with field ground fracture experimental data; s3, combining well body structure conditions of the highly-deviated well, using a mechanical model to calculate fracture pressure, and using field small
pressure data verification to form a fracture pressure calculation and analysis
database; s4, training the prediction model based on a
machine learning method, determining a WT-LSTM model structure, setting a
hidden layer and performing training; and S5, optimizing the rupture pressure model based on
machine learning through hyper-parameter adjustment,
feature selection and importance evaluation means. According to the method, the prediction accuracy of the fracture pressure of the highly-deviated well is improved by integrating
multiple factors, data support is provided for on-site complex stratified shale fracturing
engineering design, reasonable parameter selection of extended reach well construction is facilitated, adaptability and safety are improved, and data support is provided for stratified
shale oil and gas
resource development.