Method for modeling diversion capacity, drainage and mining method and system based on diversion capacity model

By employing a conductivity modeling method and iterative optimization algorithm, the comprehensive impact of fracture conductivity under the rate-pressure sensitivity effect was addressed, enabling accurate prediction and optimization of oil and gas well productivity.

CN122174682APending Publication Date: 2026-06-09CHINA UNIV OF PETROLEUM (EAST CHINA)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA UNIV OF PETROLEUM (EAST CHINA)
Filing Date
2026-05-08
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies fail to fully consider the combined effects of velocity-pressure coupling, resulting in unreasonable production and drainage parameter design, making it difficult to accurately describe the dynamic changes in fracture conductivity and affecting oil and gas well productivity.

Method used

A conductivity modeling method was adopted. By preparing multi-scale fractured core samples, multiple sets of experimental condition combinations were obtained. The conductivity value was determined by using a machine learning model. Static conductivity-velocity curves were plotted and segmented to establish a conductivity model. The drainage velocity sequence was optimized by combining iterative optimization algorithms.

Benefits of technology

It significantly improved the prediction accuracy of hydraulic fracture conductivity, optimized drainage parameters, and increased the production capacity of oil and gas wells.

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Abstract

The application discloses a flow conductivity modeling method, a drainage and mining method and system based on the flow conductivity model, and relates to the field of unconventional gas reservoir development. The flow conductivity modeling method comprises the following steps: preparing a multi-scale fractured core sample; obtaining multiple sets of experimental condition combinations; determining the flow conductivity value of the multi-scale fractured core sample based on a machine learning model according to the multiple sets of experimental condition combinations, and drawing a static flow conductivity-flow velocity curve and establishing a segmented representation model according to the flow conductivity value. The application accurately describes the dynamic change rule of the hydraulic fracture flow conductivity with the flow velocity and the effective stress, fully considers the comprehensive influence of the fracture flow conductivity under the coupling effect of the velocity sensitivity-pressure sensitivity effect, significantly improves the prediction accuracy of the hydraulic fracture flow conductivity, provides a data basis for the reasonable design of the drainage and mining parameters, and effectively improves the productivity of the oil and gas well.
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