Reservoir prediction upon seismic attribute fusion and two-phase medium detection in Penglaizhen Formation of Western Sichuan
DOI:
https://doi.org/10.62813/see.2026.01.01Keywords:
Penglaizhen Formation, Forward modeling, Attribute fusion, Gas-bearing detection, Reservoir predictionAbstract
In the Xindu Gas Field of Western Sichuan Basin, the reservoirs of the third member of Penglaizhen Formation are mainly underwater distributary channel sandstones in the shallow water delta front. These reservoirs are thin and exhibit rapid lateral changes. Consequently, it is challenging to accurately predict their planar distribution and thickness variations using conventional seismic analyses. Therefore, forward modeling and seismic multi-attribute fusion technology has been adopted to predict the reservoir parameters, effectively addressing the shortcomings of strong multiplicity in reservoir boundary interpretation and low coincidence in reservoir thickness prediction by conventional seismic attribute analysis. Then, guided by the theory of two-phase medium gas-bearing detection and in combination with the response characteristics of “low-frequency resonance and high-frequency attenuation” of seismic waves passing through gas-bearing reservoir, the favorable gas-bearing areas in the third member of Penglaizhen Formation are predicted. Using this approach, the correlation coefficient between the predicted sand body thickness and the actual drilling results reaches 0.851, and the coincidence degree of the predicted favorable reservoirs with the wells is 86%. By integrating sand body thickness and gas-bearing detection results, a comprehensive prediction of favorable reservoir zones is conducted, which is then validated against production data. This approach has enhanced the reservoir prediction accuracy in Penglaizhen Formation reservoirs of the Xindu Gas Field and provides novel reference methods for exploration and development in analogous fields.
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Copyright (c) 2026 Xingcong Chen, Prof. Gang Lu, Shengyi Wang, Xihe Lu, Zhuoyang Liu, Ruochen Tang, Prof. Changcheng Wang, Lin Qiao
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