Its a commonly used method to predict layer by using seismic attributes, especially for some of the less well control channel sand bodies whose role is more important. Putaohua reservoir in Gaotaizi oilfield mainly develop shallow water delta front subaqueous distributary channel sand bodies which has narrow rivers and thin sand bodies, meanwhile, the existing well density is difficult to control the trend and boundary of the channel. By using seismic forward modeling analysis techniques, this paper researched the differences of seismic reflection characteristics among different geological model of channel sand bodies, then , further pointed out the methods of channel sand prediction by using the peak number attribute and analyzed the predictive effect. The results show that this method can effectively improve the prediction accuracy of thin interbedded reservoir.
By using geological and geophysical data and according to the basic characteristics of sequence boundary, Putaohua Reservoir in Daan Oilfield is divided into a rise hemicycle in long-term cycles, a full middle-term cycle, nine short-term cycles and a number of ultra-short-term cycles.The high-resolution sequence stratigraphic framework of Putaohua reservoir under monosandbody rank is established, and a set of techniques and methods to high-resolution sequence stratigraphy correlation in shallow delta is summed up.
Pu I formation in Gaotaizi oilfield mainly developed delta distributary channels. The sand body is narrow and has a complex superimposition in vertical, which caused the sand body hard to forecast. To solve this problem the method of seismic sedimentology was applied to building high frequency stratigraphic framework and extracting high-precision stratal slice. The technique of 90-degree phasing conversion and multi-attribute comprehensive analysis to the channel sand was also performed. At last Pu I formation in Gaotaizi Oilfield is divided into fifteen fifth-order sequences. The attributes of Rms amplitude and Arc length are considered as the best attribute to reflect the characteristics of the channel sand in study area. By a multiple linear regression to the attributes, the prediction accuracy between wells is improved from 61% to 78%.
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