Method for predicting porosity of ten-meter-level clastic rock reservoir based on wave impedance inversion body

By combining seismic and well logging data, the wave impedance inversion technique was used to solve the problem of porosity prediction in deep clastic reservoirs at depths of 10,000 meters. This enabled reliable prediction of porosity between wells and throughout the entire area, and provided detailed porosity distribution maps.

CN117784240BActive Publication Date: 2026-07-24CHINA UNIV OF GEOSCIENCES (WUHAN)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA UNIV OF GEOSCIENCES (WUHAN)
Filing Date
2023-12-25
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing technologies are insufficient to effectively predict the porosity of deep clastic reservoirs at depths of 10,000 meters, especially in terms of porosity prediction between wells and across the entire region.

Method used

By combining seismic and well logging data and using wave impedance inversion technology, well-seismic calibration and wavelet optimization are performed to establish an initial low-frequency model, optimize inversion parameters, and conduct inversion quality control. Well logging data and rock physics relationships are used to distinguish sandstone, mudstone and coal seams, establish a fitting relationship between porosity and wave impedance, and predict porosity distribution.

Benefits of technology

It has enabled accurate prediction of porosity in clastic reservoirs at the 10,000-meter level, ensuring the reliability and accuracy of porosity distribution between wells and throughout the region, and providing detailed characteristic illustrations of porosity throughout the region.

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Abstract

The application provides a method for predicting porosity of a ten-thousand-meter-class clastic rock reservoir based on wave impedance inversion volume, and relates to the field of clastic rock reservoir porosity prediction. First, three parameters and six inversion data volumes in the inversion process are determined through well-seismic calibration and wavelet optimization. Secondly, in view of whether the inversion data volume is reliable, three aspects of inversion quality control are carried out: formation QC quality control, profile well control quality control, plane trend quality control and single well sampling, and through the above quality control steps, it is finally shown that the target area seismic inversion data volume result is reliable. Then, using logging data, rock physical relationship is used to distinguish sandstone, mudstone and coal seam, under the constraint of inversion wave impedance and calculation of RT double three-dimensional data volume, the sandstone development range is recognized, and a sandstone result interpretation data volume is formed. Finally, the wave impedance identified sandstone data volume is fitted with the measured core porosity, and the porosity distribution of the whole area is effectively predicted.
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