P-SV wave elastic impedance and fluid identification factor in weakly anisotropic media

The P-SV wave reflection coefficients in VTI and HTI media were obtained by approximation of the Jilek（2002a and b）equation in orthotropic anisotropic media.An approximate equation for P-SV wave elastic impedance can be derived from the combination of the new coefficients with S-wave elastic impedance（Duffaut et al.,2000）.On this basis, the fluid identification factor in weakly anisotropic media was constructed and used to identify the Castagna and Smith（1994）lithologic combination and achieved good results. Finally,we specifically analyzed the anisotropic parameter impacts P-SV wave elastic impedance and fluid factor trends.

Numerical simulation of(T_2,T_1) 2D NMR and fluid responses

One-dimensional nuclear magnetic resonance （1D NMR） logging technology is limited for fluid typing, while two-dimensional nuclear magnetic resonance （2D NMR） logging can provide more parameters including longitudinal relaxation time （71） and transverse relaxation time （T2） relative to fluid types in porous media. Based on the 2D NMR relaxation mechanism in a gradient magnetic field, echo train simulation and 2D NMR inversion are discussed in detail. For 2D NMR inversion, a hybrid inversion method is proposed based on the damping least squares method （LSQR） and an improved truncated singular value decomposition （TSVD） algorithm. A series of spin echoes are first simulated with multiple waiting times （Tws） in a gradient magnetic field for given fluid models and these synthesized echo trains are inverted by the hybrid method. The inversion results are consistent with given models. Moreover, the numerical simulation of various fluid models such as the gas-water, light oil-water, and vicious oil-water models were carried out with different echo spacings （TEs） and Tws by this hybrid method. Finally, the influences of different signal-to-noise ratios （SNRs） on inversion results in various fluid models are studied. The numerical simulations show that the hybrid method and optimized observation parameters are applicable to fluid typing of gas-water and oil-water models.

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.

蒸汽双向流的计量实践与讨论

炼油与化工生产中，常有一条管道蒸汽双向输送现象，造成了计量以及装置经济技术指标核算的困难。用双向孔板或用弯管流量计配套流向识别装置，均可测量蒸汽双向流，后者可利用蒸汽管道膨胀弯压损识别流向，但要避免把变送器零点与测量系统零点混淆。从准确度与能耗看，弯管流量计配套流向识别装置，测量性价比要优于双向孔板。