A method and system for identifying low-speed mudstone based on attribute clustering

Through the method based on attribute clustering, seismic data and machine learning technology are used to distinguish low-speed mudstone from gas-containing sandstone, solving the problem of medium-low-speed mudstone identification error in medium-depth reservoirs, and improving exploration accuracy and exploration success rate.

CN119001849BActive Publication Date: 2025-08-05SOUTHWEST PETROLEUM UNIV
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Patent Information

Application Number
CN202411152655.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-08-21
Publication Date
2025-08-05
Estimated Expiration
2044-08-21

AI Technical Summary

Technical Problem

The identification of medium and low-speed mudstone in the existing technology in the medium and deep reservoirs has errors, resulting in a decrease in exploration accuracy and drilling risks, affecting the effective identification and development of reservoirs.

Method used

The method based on attribute clustering is adopted to distinguish low-speed mudstone from gas-containing sandstone through seismic data analysis, geological modeling and machine learning technology. Attribute clustering analysis is performed using CLARA clustering algorithm and PAM method, a three-layer horizontal layered medium model is established for seismic forward performance, seismic attributes are extracted and selected, redundant attributes are reduced, and recognition accuracy is improved.

Benefits of technology

It significantly improves the reservoir identification accuracy in low-speed mudstone development zones, reduces interference from highlight traps, provides reliable exploration decision support, reduces exploration risks, and optimizes well site deployment.

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Abstract

The present invention discloses a method and system for identifying low-velocity mudstone based on attribute clustering. It includes: by analyzing the logging data and seismic data of exploration wells in the work area, dividing the low-velocity mudstone into bright spot type and non-bright spot type, and analyzing their characteristics. Establishing a three-layer medium model and conducting seismic forward modeling to obtain the seismic response characteristics of different models. Then, extracting and optimizing seismic attributes from the forward modeling results. Using the CLARA clustering algorithm and the PAM method to conduct clustering analysis on the attributes to identify the types of low-velocity mudstone and gas-bearing sandstone. The present invention improves the identification accuracy of low-velocity mudstone and gas-bearing sandstone, and provides technical support for exploration and well location deployment.
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