Prediction method and system for shale gas pressure post-transformation fracture network

Abstract

The invention provides a prediction method for a shale gas pressure post-transformation fracture network, and the method comprises the steps of collecting basic parameters which need to be used in a shale gas numerical simulation process, and selecting the gas production rate, the bottom hole pressure and the water production rate of a multi-section fractured horizontal well as fitting data; constructing a level set function for the representative points, and determining level set function values of all the representative points in the reservoir according to the microseismic distribution; taking the fracturing fracture data as an average value, taking an average value in a preset proportion as a variance, generating a plurality of groups of parameter sets containing main fracture parameters and secondary fracture parameters, and classifying fracture types and permeation types of all positions in the reservoir; and constructing an iterative historical fitting algorithm based on a preset algorithm, and obtaining the distribution of the reconstructed fracture network after the shale pressure and the distribution after the fracturing fluid invades the stratum according to the converged parameters. According to the invention, information such as spatial distribution and permeability of the post-fracturing reconstruction seam network can be automatically inverted according to production data of the shale gas production well.

Description

technical field [0001] The invention relates to the technical field of petroleum exploitation, in particular to a prediction method and system for fracture network reconstruction after shale air pressure. Background technique [0002] The development of shale gas requires horizontal drilling and multi-stage fracturing technology. Accurate understanding of fracturing stimulation parameters is the key to production prediction and stimulation scheme design such as refracturing and infill wells. Accurate understanding of fracturing stimulation parameters is the basis for production prediction, and accurate understanding of fracture network distribution between single well fracturing intervals is the key to refracturing design. In addition, it is the key to infill well design to accurately understand the degree of fracture connectivity among multi-wells. In addition, accurate quantification of the uncertainty of the fracture network after fracturing is the key to risk assessment...

AI Technical Summary

Problems solved by technology

This method is simple to operate, but on the one hand, it is subject to greater subjective influence, and the estimation results often have large deviations.

On the other hand, it is impossible to consider the influence of a large number of invalid microseismic

[0005] The second method: judging by fracturing simulation results, this method needs to provide accurate in-situ stress distribution parameters to ensure the simulation accuracy, but due to the existence of reservoir heterogeneity, it is usually very difficult to obtain accurate in-situ stress data, so This method has limited application in the field

However, the existing ensemble Kalman filter method has limited application in shale gas fracture network inversion, mainly because this method is a sequential assimilation method, and it is not suitable for the strongly nonlinear problem of reconstructing fracture network distribution after shale gas pressure. There are deficiencies such as poor convergence

Images