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The meandering channel deposit of the upper member of Neogene Guantao Formation in Shengli Chengdao extra-shallow sea oilfield is characterized by rapid change in sedimentary facies. In addition, affected by surface tides and sea water reverberation, the double sensor seismic data processed by conventional methods has low signal-to-noise ratio and low resolution, and thus cannot meet the needs of seismic description and oil-bearing fluid identification of thin reservoirs less than 10 meters thick in this area. The two-step high resolution frequency bandwidth expanding processing technology was used to improve the signal-to-noise ratio and resolution of the seismic data, as a result, the dominant frequency of the seismic data was enhanced from 30 Hz to 50 Hz, and the sand body thickness resolution was enhanced from 10 m to 6 m. On the basis of fine layer control by seismic data, three types of seismic facies models, floodplain, natural levee and point bar, were defined, and the intelligent horizon-facies controlled recognition technology was worked out, which had a prediction error of reservoir thickness of less than 1.5 m. Clearly, the description accuracy of meandering channel sand bodies has been improved. The probability semi-quantitative oiliness identification method of fluid by prestack multi-parameters has been worked out by integrating Poisson's ratio, fluid factor, product of Lame parameter and density, and other prestack elastic parameters, and the method has a coincidence rate of fluid identification of more than 90%, providing solid technical support for the exploration and development of thin reservoirs in Shengli Chengdao extra-shallow sea oilfield, which is expected to provide reference for the exploration and development of similar oilfields in China.

In view of the problems of low signal-to-noise ratio and low resolution of seismic data in shallow-thin reservoir in Chunfeng Oilfield of Junggar Basin, stepped and detailed data processing and interpretation technologies are proposed for shallow-thin reservoir prediction. The overlapping type offset packet processing technology can increase seismic fold from 8 to 16 times and increase signal to noise ratio by 1.4 times at local low noise ratio area by fine processing including frequency upgrade and de-noise imaging techniques. This study established three-level prediction techniques including pre-stack improving resolution target processing, post-stack wavelet reconstruction frequency extension, pre-stack and post-stack joint inversion, which can increase sand resolution from 12 m to 2 m and improve the identification accuracy of reservoir efficiently. The shallow-thin reservoirs after frequency extension have continuous and defined reflections, which are well coincided with actual exploration. The seismic data have well ability of preserving amplitude, and achieve good application effects. Key words: Junggar Basin, Chunfeng oilfield, Miocene Shawan Formation, reservoir prediction, wavelet reconstruction, pre-stack and post-stack joint inversion

Elastic impedance (EI) inversion has been widely used in industry to estimate kinds of elastic parameters to distinguish lithology or even fluid. However, it is found that conventional three-term elastic impedance formula is unstable even with slight random noise in seismic data, due to the ill-conditioned coefficient matrix of elastic parameters. We presented two-term Fatti elastic impedance inversion method, which is more robust and accurate than conventional three-term elastic impedance inversion. In our method, density is ignored to increase the robustness of inversion matrix. Besides, P-impedance and S-impedance, which are less sensitive to random noise, are inverted instead of VP and VS in conventional three-term elastic impedance. To make the inversion more stable, we defined the range of K value as a constraint. Synthetic tests claim that this method can obtain promising results with low SNR (signal noise ratio) seismic data. With the application of the method in a 2D line data, we achieved λρ, μρ and VP/VS sections, which matched the drilled well perfectly, indicating the potential of the method in reservoir prediction.

A classical unit-cell slug model is constructed with mass balances and momentum balances for each phase. However these balances contain more unknowns than there are equations, so it is necessary to construct submodels to be able to solve the system of equations. The void fraction within the liquid slug is a critical unknown which is still poorly understood. In this work, a similarity analysis between a fixed bubble and a Taylor bubble was developed. This similarity analysis leads to application of the gas entrainment mechanism developed on a fixed bubble to predict void fraction in liquid slugs at different mixture velocities.