砂泥岩储层测井产能预测方法研究

Productivity prediction is very important in the exploration and development of oilfields. Using well log data to predict productivity is a front-line technology， which is key issue in petroleum exploration phase. The essential factors of productivity prediction is building practical models and correcting various causes to improve precision of prediction parameters. Any errors of parameters selections can affect the calculation of productivity prediction; therefore， how to improve research means and calculation accuracy is an important task of productivity prediction. Theory and case examples are deeply and comprehensively studied in the paper. Based on the theory of mud-filtrate invasion and experimental results， the damage of drilling， cementing， perforating，acidizing and fracturing were investigated. The damage depth was quantitatively evaluated by log data， based on this， the processing results of reservoir sensitivity were used to analysis quantitatively the damage of reservoir. The productivity prediction and reservoir damage were initiatively incorporated according to well logging， and the precision of productivity prediction was effectively improved. The method of NMR was explored to calculate the fluid viscosity on the basis of reservoir physical method， and the differences between the two methods were compared in the paper. From the theory fluid flow in porous media， various of theoretical models of production prediction were explored and several practical models were consided， such as productivity index method， improved productivity index method， improved Bearder method， SVM and so on. The characteristic and the application scope of these methods were studied. The inflow productivity and outflow productivity were incorporated and nodal analysis method was used to forecast wellhead yield， thus achieved scientifically production. On the applied background of conventional logging suite， the applying of special items or new logging method which is practical in the research area were studied， the logging suite was further optimized， and the precision of forecast was improved. On the basis of the modeling and the calculation of parameters， these methods were verified and analyzed， and the reconstruct principle was also built for block reservoir. The research block was processed by these methods and compared with testing data. Based on above the research， a technological system which is practical for shaly sand profiles in Shengli Oilfield was built. The system can reach commercialized degree，and satisfied the need of exploration and development of the oilfield.

反假频重建理论及其在储层预测中的应用

To extend the cross-hole seismic 2D data to outside 3D seismic data, reconstructing the low frequency data to high frequency data is necessary. Blind deconvolution method is a key technology. In this paper, an implementation of Blind deconvolution is introduced. And optimized precondition conjugate gradient method is used to improve the stability of the algorithm and reduce the computation. Then high-frequency retrieved Seismic data and the cross-hole seismic data is combined for constraint inversion. Real data processing proved the method is effective. To solve the problem that the seismic data resolution can’t meet the request of reservoir prediction in the river face thin-layers in Chinese eastern oil fields, a high frequency data reconstruction method is proposed. The extrema of the seismic data are used to get the modulation function which operated with the original seismic data to get the high frequency part of the reconstruction data to rebuild the wide band data. This method greatly saves the computation, and easy to adjust the parameters. In the output profile, the original features of the seismic events are kept, the common feint that breaking the events and adding new zeros to produce alias is avoided. And the interbeded details are enhanced compared to the original profiles. The effective band of seismic data is expended and the method is approved by the processing of the field data. Aim to the problem in the exploration and development of Chinese eastern oil field that the high frequency log data and the relative low frequency seismic data can’t be merged, a workflow of log data extrapolation constrained by time-phase model based on local wave decomposition is raised. The seismic instantaneous phase is resolved by local wave decomposition to build time-phase model, the layers beside the well is matched to build the relation of log and seismic data, multiple log info is extrapolated constrained by seismic equiphase map, high precision attributes inverse sections are produced. In the course of resolve the instantaneous phase, a new method of local wave decomposition --Hilbert transform mean mode decomposition(HMMD) is raised to improve the computation speed and noise immunity. The method is applied in the high resolution reservoir prediction in Mao2 survey of Daqing oil field, Multiple attributes profiles of wave impedance, gamma-ray, electrical resistivity, sand membership degree are produced, of which the resolution is high and the horizontal continuous is good. It’s proved to be a effective method for reservoir prediction and estimation.

Thermodynamics of blends of poly(ethylene oxide) with poly(methyl methacrylate) and poly(vinyl acetate): prediction of miscibility based on Flory solution theory modified by Hamada

Flory solution theory modified by Hamada et al. (Macromolecules, 1980, 13, 729) was used to predict the miscibility of blends of poly(ethylene oxide) with poly(methyl methacrylate) (PEO-aPMMA) and with poly(vinyl acetate) (PEO-PVAc). Interaction parameters of a PEO-aPMMA blend with the weight ratio of PEO/aPMMA = 50/50 at the temperature range of 393-433 K and PEO-PVAc blends with different compositions and temperatures were calculated from the determined equation-of-state parameters based on Flory solution theory modified by Hamada ed al. Results show that interaction parameters of the PEO-aPMMA blend are negative and can be comparable with values obtained from neutron-scattering measurements by Ito et al. (Macromolecules, 1987, 20, 2213). Also, interaction parameters and excess volumes of PEO-PVAc blends are negative and increase with enhancing the content of PEO and the temperature. (C) 1998 Elsevier Science Ltd. All rights reserved.

Size Effects in Micro-bending Deformation of MEMS Devices Based on the Discrete Dislocation Theory

In the present research, the discrete dislocation theory is used to analyze the size effect phenomena for the MEMS devices undergoing micro-bending load. A consistent result with the experimental one in literature is obtained. In order to check the effectiveness to use the discrete dislocation theory in predicting the size effect, both the basic version theory and the updated one are adopted simultaneously. The normalized stress-strain relations of the material are obtained for different plate thickness or for different obstacle density. The prediction results are compared with experimental results.

Fast Computing For Lurr Of Earthquake Prediction

The LURR theory is a new approach for earthquake prediction, which achieves good results in earthquake prediction within the China mainland and regions in America, Japan and Australia. However, the expansion of the prediction region leads to the refinement of its longitude and latitude, and the increase of the time period. This requires increasingly more computations, and the volume of data reaches the order of GB, which will be very difficult for a single CPU. In this paper, a new method was introduced to solve this problem. Adopting the technology of domain decomposition and parallelizing using MPI, we developed a new parallel tempo-spatial scanning program.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

Prediction and suppression of strong dispersive coupling in microracetrack channel drop filters

For a four-port microracetrack channel drop filter, unexpected transmission characteristics due to strong dispersive coupling are demonstrated by the light tunneling between the input-output waveguides and the resonator, where a large dropping transmission at off-resonance wavelengths is observed by finite-difference time-domain simulation. It causes a severe decline of the extinction ratio and finesse. An appropriate decrease of the coupling strength is found to suppress the dispersive coupling and greately increase the extinction ratio and finesse, a decreased coupling strength can be realized by the application of an asymmetrical coupling waveguide structure. In addition, the profile of the coupling dispersion in the transmission spectra can be predicted based on a coupled mode theory analysis of an equivalent system consisting of two coupling straight waveguides. The effects of structure parameters on the transmission spectra obtained by this method agree well with the numerical results. It is useful to avoid the strong dispersive coupling region in the filter design. (c) 2007 Optical Society of America.