Fracture surface density parameter prediction method and device based on array induction logging
By acquiring resistivity curves at multiple depths through array induction logging and preprocessing them, combined with a neural network model, the problem of unstable prediction of fracture surface density parameters under high-angle or vertical fracture conditions in tight sandstone and mudstone reservoirs was solved, and accurate prediction of fracture surface density parameters was achieved.
CN122014218APending Publication Date: 2026-05-12CHINA UNIV OF GEOSCIENCES (WUHAN)
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Patent Information
- Application Number
- CN202610331653.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-18
- Publication Date
- 2026-05-12
AI Technical Summary
Technical Problem
Existing technologies struggle to reliably predict fracture surface density parameters in tight sandstone and mudstone reservoirs under conditions of high-angle or vertical fractures.
Method used
Initial resistivity curves at multiple depths are obtained using array induction logging. After preprocessing and noise reduction, fracture surface feature vectors are constructed and trained using a neural network model to achieve accurate prediction of fracture surface density parameters.
Benefits of technology
Under high-angle or vertical crack conditions, it can stably predict crack surface density parameters, improving the accuracy and reliability of the prediction.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a fracture surface density parameter prediction method and device based on array induction logging, and the method comprises the steps: obtaining at least three initial resistivity curves of a target well through an array induction mode; the at least three initial resistivity curves are preprocessed, at least three target resistivity curves are obtained, and data noise in the target resistivity curves is smaller than data noise in the initial resistivity curves; based on the at least three target resistivity curves, determining an initial crack surface feature vector of the target well, and performing normalization processing on the initial crack surface feature vector to obtain a target crack surface feature vector; and inputting the target fracture surface feature vector into the target prediction model for analysis to obtain a fracture surface density parameter of the target well, thereby solving the technical problem that the fracture surface density parameter is difficult to stably predict under the condition of a high-angle or vertical fracture.
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