Description method of reservoir sand bodies on ultra-deep coal layer

The invention discloses a description method of reservoir sand bodies on an ultra-deep coal layer. The method comprises the following steps of: firstly, analyzing waveform characteristics of erosional truncation type pinchout points and coal layer characteristics by means of forward modeling of diff...

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Bibliographic Details
Main Authors LIU LEI, LI CHAO, ZHANG ZAIJIN, DU YUSHAN, LI JUN, XIAO WEN, FAN TENGTENG, ZHANG JUNHUA, ZHANG SHUFAN, LI YUHANG
Format Patent
LanguageEnglish
Published 24.02.2016
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Summary:The invention discloses a description method of reservoir sand bodies on an ultra-deep coal layer. The method comprises the following steps of: firstly, analyzing waveform characteristics of erosional truncation type pinchout points and coal layer characteristics by means of forward modeling of different models, and providing bases for boundary determination of practical data sand bodies and coal layer stripping; utilizing overlying strata low trough attribute extreme points to identify pinchout lines of the sand bodies; utilizing a multichannel dynamic matching tracking method under the control of layer positions to carry out coal layer high-shielding stripping, and highlighting reservoir weak signals; on the basis of coal layer high-shielding stripping, utilizing a relatively-high time frequency resolution in GST to carrying out frequency division and reconstruction on frequency spectrum components of main information of the reservoir in a time frequency domain, wherein the processed data can predict the sand bodies more accurately; and finally, according to GR pseudo-acoustic impedance inversion, obtaining three-dimensional wave impedance data bodies for comprehensive analysis, and obtaining a reservoir prediction result. By adopting the method provided by the invention, the pinchout lines of the sand bodies are effectively described, the range of the sand bodies of the ultra-deep reservoir is precisely predicted, and the precision of seismic reservoir prediction is greatly improved.
Bibliography:Application Number: CN201510704780