钻井废物的固化处理

本文在分析钻井废物特性的基础上,选用水泥基和石灰基两种固化体系,以烟灰、蒙脱土为添加剂进行固化试验。实验表明,经处理后的钻井废物,重金属铬和COD的浸出量都降低了95%以上,产物稳定性好,从而能减少它对环境的污染。文中还讨论了影响固化效果的一些因素。     

A class of adaptive directional image smoothing filters

The gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions. The idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates a line segment in that direction. Hence, the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance. In this paper we describe a class of adaptive directional image smoothing filters based on generalized Gaussian distributions. We propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized Gaussian distribution. Several experimental results indicate a significant improvement compared to some standard filters.     

小井眼钻井技术

介绍了小井眼钻井技术的工艺概况。钻井方式分转盘、井底马达、连续取心和盘管钻井等4种。并介绍了发展中遇到的问题及解决办法。主要有打捞、机械钻速、井控和水力学问题,地层评价及试油、作业问题等的解决办法。此项技术是降低石油成本的新途径。     

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用测井资料计算泥浆侵入深度主要是基于电阻率径向分布特征进行的,传统的方法主要有图版法、经验公式法。用测井资料计算泥浆侵入油气层深度是十分有效而直接的手段,用其间接地评价钻井泥浆污染油(气)层的程度则是一项经济可行的新技术。文章在分析比较了传统的泥浆侵入校正方法的基础上,经过理论推导和应用介绍了一种直接利用深、中、浅电阻率组合测井资料计算泥浆侵入深度的新方法。通过对研究工区10多口井的实际资料进行处理,泥浆侵入深度的解释结果与测试评价基本相符,应用表明,该方法不但可以求出钻井泥浆侵入深度,还可求出地层真电阻率,它优于传统的方法,对低侵、高侵和无侵三种情况都实用,不失为一种计算泥浆侵入油气层深度的新方法。     

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随着油气勘探开发难度的提高,钻井的复杂程度也相应地增加。为了获得良好的钻井效益,应加强钻井、地质和地球物理学科的交流,尽可能多地使用钻井、地质、地球物理测井的相关资料。文章描述了三维可视化技术在钻井工程中的应用,即可以将已钻井数据、待钻井设计数据及其与之相关的力学、地质和地球物理数据紧密地结合,在三维可视化环境中形象直观地展示数据之间的相互关系,在钻前设计、钻进监测、钻井事故预测与处理及钻后分析中,有效提高钻井人员分析复杂数据和提取有效信息的能力,加速他们与相关学科的交流和协作,从而提高钻井效益。     

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海上钻探和陆上钻探存在诸多差异,设计方式亦有所不同。文章针对海上钻探大量采用定向井的实际情况,详细讨论了如何利用海上定向井测井资料预测所钻地区地层压力的方法,主要包括测井资料的井斜校正和海水深度、平台高度对计算地层压力的影响等内容。如果不考虑这些因素来计算地层压力,其结果就会远远偏离真实值。通过对上述因素的分析,建立了适用于海上钻探地层压力计算的方法。将此法应用于某海上油田地层压力预测中效果良好。文章在对所研究区块进行地层压力计算时分别使用了等效深度法和伊顿法,计算结果说明在研究区块伊顿法计算结果优于等效深度法。其原因为该区含有大量粉砂岩地层而纯泥岩层少的缘故。     

A fault detection and isolation filter for discrete linear systems

The problem of fault and/or abrupt disturbances detection and isolation for discrete linear systems is analyzed in this work. A strategy for detecting and isolating faults and/or abrupt disturbances is presented. The strategy is an extension of an already existing result in the continuous time domain to the discrete domain. The resulting detection algorithm is a Kalman filter with a special structure. The filter generates a residuals vector in such a way that each element of this vector is related with one fault or disturbance. Therefore the effects of the other faults, disturbances, and measurement noises in this element are minimized. The necessary stability and convergence conditions are briefly exposed. A numerical example is also presented.     

Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear

This study compares the performance of backpropagation neural network (BPNN) and radial basis function network (RBFN) in predicting the flank wear of high speed steel drill bits for drilling holes on mild steel and copper work pieces. The validation of the methodology is carried out following a series of experiments performed over a wide range of cutting conditions in which the effect of various process parameters, such as drill diameter, feed-rate, spindle speed, etc. on drill wear has been considered. Subsequently, the data, divided suitably into training and testing samples, have been used to effectively train both the backpropagation and radial basis function neural networks, and the individual performance of the two networks is then analyzed. It is observed that the performance of the RBFN fails to match that of the BPNN when the network complexity and the amount of data available are the constraining factors. However, when a simpler training procedure and reduced computational times are required, then RBFN is the preferred choice.     

Microstructure and stress rupture properties of single crystal superalloy CMSX-2 under high thermal gradient directional solidification

The interface morphologies of single crystal superalloy CMSX-2 were studied over a range of cooling rate with large variations in withdrawal speeds in directional solidification. A superfine cellular structure was obtained under both high thermal gradient up to 1000 K/cm and fast withdrawal rate up to 1 mm/s. The high rate directional solidification results in reduction in primary and secondary dendrite arm spacing, refinement of γ′ phase, reduced microsegregation of alloying elements and smaller size of γ-γ′ eutectics. The rupture life and plasticity of fine structure samples produced in high thermal gradient directional solidification increase significantly than that in conventional directional solidification process at 1323 K.     

阵列误差影响下的RBF神经网络波达方向估计

针对阵列误差(阵元间互耦、通道失配)影响下波达方向估计问题,提出一种新型的基于径向基神经网络侦察测向系统。这种系统采用直接数据域补偿算法对输入数据进行误差补偿,从而获得正确的基函数中心。在无需对RBF神经网络测向系统作任何改进的情况下,可获得对波达方向的准确估计。为了减少输入,利用信号协方差矩阵的对称性以及对角线元素不包含信号方向信息的特点,仅考虑协方差矩阵中的上三角部分元素作为网络输入。给出了应用该方法的具体步骤。仿真实验表明,基于这种RBF网络的侦察测向系统达到了很高的精度。