站在历史与现代之间的抒写——解读哈萨克族作家夏依木拉提的中篇小说《神井》

中篇小说《神井》从民族历史的追忆中反思民族文化的选择取向。在历史的文学性叙写中,传达了对主流文化的追思——期望融入主流但却被现实疏离的尴尬与无奈,书写了对本土文化的深深眷恋以及在新的历史条件下对民族文化积极建构的理想。

水基成膜钻井液在神北6井的应用

吐哈油田神北地区第三系及白垩系富含盐膏,侏罗系含有易坍塌的硬脆性泥页岩和大段煤层,裸眼段较长,在钻井施工过程中经常发生大段划眼或卡钻等复杂情况。针对神北区块的地层情况,在神北6井侏罗系以下井段采用水基成膜钻井液。该钻井液具有半透膜性能,抑制能力强,在井壁上能形成一层(多层)隔离膜,在井壁外围形成保护层,阻止水及钻井液进入地层,有效地防止地层水化膨胀,封堵地层层理裂隙,防止地层内粘土颗粒的运移,防止井壁坍塌,保护油气层。现场应用表明,水基成膜钻井液性能稳定,井壁稳定能力强,顺利完成了吐哈油田裸眼井段(3751m)最长的钻井施工,并电测、下套管和固井顺利。神北6井平均井径扩大率和油层井径扩大率比邻井分别降低50%和65%,复杂事故次数平均减少了57%,复杂损失率平均降低了42%。

Application of multiple attributes fusion technology in the Su-14 Well Block

In this study area the geological conditions are complicated and the effective sandstone is very heterogeneous.The sandstones are thin and lateral and vertical variations are large.We introduce multi-attribute fusion technology based on pre-stack seismic data, pre-stack P-and S-wave inversion results,and post-stack attributes.This method not only can keep the fluid information contained in pre-stack seismic data but also make use of the high SNR characteristics of post-stack data.First,we use a one-step recursive method to get the optimal attribute combination from a number of attributes.Second,we use a probabilistic neural network method to train the nonlinear relationship between log curves and seismic attributes and then use the trained samples to find the natural gamma ray distribution in the Su-14 well block and improve the resolution of seismic data.Finally,we predict the effective reservoir distribution in the Su-14 well block.

Neural network forecasting model based on phase space re-construction in water yield of mine

The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example, the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.

Backpropagation neural network method in data processing of ultrasonic imaging logging-while-drilling

The existing methods for extracting the arrival time and amplitude of ultrasonic echo cannot eff ectively avoid the local interference of ultrasonic signals while drilling,which leads to poor accuracy of the echo arrival time and amplitude extracted by an ultrasonic imaging logging-while-drilling tool.In this study,a demodulation algorithm is used to preprocess the ultrasonic simulation signals while drilling,and we design a backpropagation neural network model to fit the relationship between the waveform data and time and amplitude.An ultrasonic imaging logging model is established,and the finite element simulation software is used for forward modeling.The response under diff erent measurement conditions is simulated by changing the model parameters,which are used as the input layer of the neural network model;The ultrasonic echo signal is considered as a low-frequency signal modulated by a high-frequency carrier signal,and a low-pass fi lter is designed to remove the high-frequency signal and obtain the low-frequency envelope signal.Then the amplitude of the envelope signal and its corresponding time are extracted as an output layer of the neural network model.By comparing the application eff ects of the various training methods,we fi nd that the conjugate gradient descent method is the most suitable method for solving the neural network model.The performance of the neural network model is tested using 11 groups of simulation test data,which verify the eff ectiveness of the model and lay the foundation for further practical application.

Analysis on damage and rheological characteristics of deep surrounding rock of shaft engineering

According to the characteristics of deep engineering surrounding rock main shaft of No.3 mining district in Jinchuan, electron microscope scanning and rock mechanics test were adopted to analyze the damage features of rock. The software of FLAG3D and Burgers body （Kelvin-Maxwell model） were used to research on rheological theory, and rheological model was modified. The results indicate that the damage of rock mass is very serious, and the rheological characteristics also outstanding; rheological behavior of deep surrounding rocks of the shaft can be taken as superposition of transient and stable rheology; and there exist the most dangerous zone on 100 m higher than 1 063 m level, so it is necessity that works of monitor and corresponding reinforcement should strengthen.

我登上了蒙山

2005年10月4日,有着一个晴朗的好天气。这天,我跟姥爷、姥姥、舅舅、妗子和爸爸一起,登上了全国闻名的蒙山。在去蒙山森林公园的路上,我就好奇地问爸爸:“蒙山有多高?”

Productivity matching and quantitative prediction of coalbed methane wells based on BP neural network

It is a great challenge to match and predict the production performance of coalbed methane (CBM) wells in the initial production stage due to heterogeneity of coalbed, uniqueness of CBM production process, complexity of porosity-permeability variation and difficulty in obtaining some key parameters which are critical for the conventional prediction methods (type curve, material balance and numerical simulation). BP neural network, a new intelligent technique, is an effective method to deal with nonlinear, instable and complex system problems and predict the short-term change quantitatively. In this paper a BP neural model for the CBM productivity of high-rank CBM wells in Qinshui Basin was established and used to match the past gas production and predict the futural production performance. The results from two case studies showed that this model has high accuracy and good reliability in matching and predicting gas production with different types and different temporal resolutions, and the accuracy increases as the number of outliers in gas production data decreases. Therefore, the BP network can provide a reliable tool to predict the production performance of CBM wells without clear knowledge of coalbed reservoir and sufficient production data in the early development stage.

Application of the HSAB principle for the quantitative analysis of nucleophilicity/basicity of organic compounds with lone-pair electrons

Based on the density functional theory,we described here a method to investigate the quantitative relationship between nucleophilicity/basicity and HSAB-theory-based properties of compounds with lone-pair electrons.Descriptors including global softness,Fukui function,local softness and local mulliken charge were calculated at SVWN/DN~* level of DFT with PC Spartan Pro.Nucleophilicity and basicity of 28 selected compounds were classified based on intensity.BP algorithm of artificial neural network(ANN) was employed to study the relationship between the descriptors and nucleophilicity/basicity.Cross-validation was carried out to avoid the over-fitting in training of ANN.A BP network was trained to quantify the relationship between HSAB-theory-based properties and nucleophilicity/basicity of compounds with lone-pair electrons.The results show that the prediction based on the network matches with the experimental results well.The local softness and Fukui function have a better relationship with nucleophilicity and local mulliken charge than with the basicity.The trained BP network could be utilized for predicting the nucleophilicity/basicity of compounds or functional groups with lone-pair electrons.