摘要
Aimed at the serious mismatch between the synthetic seismogram and the real data of the Sinian Dengying Formation and the Lower Cambrian Qiongzhusi Formation in the GS1 well area, Sichuan Basin, four aspects of internal multiples identification and suppression were studied. Firstly, a forward modeling method of internal multiple based on reflectivity method was developed. Through eight means such as post-stack and pre-stack forward modeling of internal multiple, and combined with VSP data, it was demonstrated that well-seismic mismatching in this area is mainly caused by the internal multiples. Secondly, the simulation results of internal multiple forward modeling using the stripping method combined with the internal multiple periodicity analysis showed that four groups of overburden layers with velocity inversion were the main sources of the internal multiples. Thirdly, by identifying internal multiples accurately and using suppression technology based on pattern recognition, an effective and replicable suppression scheme suitable for these formations was established, overcoming the difficulty of the small speed difference between internal multiple and primary reflection wave which makes the current methods ineffective. Fourthly, an evaluation index of internal multiple intensity was proposed, and the internal multiple intensity distribution diagram of the fourth member of Dengying Formation(Deng-4 Member) in Gaoshiti-Moxi area was compiled. This scheme greatly improved the well-seismic match, and the strata sedimentary features are clearer on the new seismic profiles with higher lateral resolution, with which smaller faults and geological anomalies can be identified and a series of the bead reflections in the Dengying Formation are first discovered. The coincidence rate of reservoir prediction based on seismic waveform classification was increased from 60% to 90%, and that of hydrocarbon detection based on dual phase medium theory was increased from 70% to 100%.
Aimed at the serious mismatch between the synthetic seismogram and the real data of the Sinian Dengying Formation and the Lower Cambrian Qiongzhusi Formation in the GS1 well area, Sichuan Basin, four aspects of internal multiples identification and suppression were studied. Firstly, a forward modeling method of internal multiple based on reflectivity method was developed. Through eight means such as post-stack and pre-stack forward modeling of internal multiple, and combined with VSP data, it was demonstrated that well-seismic mismatching in this area is mainly caused by the internal multiples. Secondly, the simulation results of internal multiple forward modeling using the stripping method combined with the internal multiple periodicity analysis showed that four groups of overburden layers with velocity inversion were the main sources of the internal multiples. Thirdly, by identifying internal multiples accurately and using suppression technology based on pattern recognition, an effective and replicable suppression scheme suitable for these formations was established, overcoming the difficulty of the small speed difference between internal multiple and primary reflection wave which makes the current methods ineffective. Fourthly, an evaluation index of internal multiple intensity was proposed, and the internal multiple intensity distribution diagram of the fourth member of Dengying Formation(Deng-4 Member) in Gaoshiti-Moxi area was compiled. This scheme greatly improved the well-seismic match, and the strata sedimentary features are clearer on the new seismic profiles with higher lateral resolution, with which smaller faults and geological anomalies can be identified and a series of the bead reflections in the Dengying Formation are first discovered. The coincidence rate of reservoir prediction based on seismic waveform classification was increased from 60% to 90%, and that of hydrocarbon detection based on dual phase medium theory was increased from 70% to 100%.
基金
Supported by the CNPC Geophysical Major Technology Field Examination and Integrated Support Project(2017D-3503)
Scientific Research and Technological Development Project of PetroChina Company Ltd.(kt2018-10-02)
