横波预测技术在延川南地区储层预测中的应用

在延川南地区,通过开展测井横波模拟研究,获得测井的横波速度;采用叠前弹性参数反演技术计算纵、横波阻抗;利用砂泥岩在纵横波阻抗交汇图上的差异进行石盒子组砂岩储层预测,利用煤层与围岩在密度和阻抗方面的差异预测煤层的分布空间。通过横波资料可以有效地获得其它弹性参数,提高叠前反演的可靠性,降低叠后储层预测的多解性,提高储层预测精度。     

随机模拟技术在储层预测中的应用

随机模拟是一种基于地质统计学的储层预测方法,将井资料和波阻抗反演结果联合进行模拟,可以提高预测的精度,适用于滚动勘探开发初期及中后期。本次应用在对储层测井响应特征和物性参数分析的基础上,通过直方、变差分析,选择合理的反演参数,进行随机模拟,进而预测储层的分布规律,实际应用表明,随机模拟方法可提高储层反演的分辨率。     

复合相控砂体预测技术在潜江凹陷潜江组的应用

沉积相与物探技术相结合,是对盐湖盆地潜江凹陷特殊地质条件以及实际精细勘探的需要。可用成熟的复合相控砂体预测技术在潜江组储层预测中区分多种复杂岩性、识别不同厚度砂体。     

Exploration of artificial neural network to predict morphology of TiO2 nanotube

Artificial neural network (ANN) was developed to predict the morphology of TiO₂ nanotube prepared by anodization. The collected experimental data was simplified in an innovative approach and used as training and validation data, and the morphology of TiO₂ nanotube was considered as three parameters including the degree of order, diameter and length. Applying radial basis function neural network to predict TiO₂ nanotube degree of order and back propagation artificial neural network to predict the nanotube diameter and length were emphasized in this paper. Some important problems such as the selection of training data, the structure and parameters of the networks were discussed in detail. It was proved in this paper that ANN technique was effective in the prediction work of TiO₂nanotube fabrication process.     

Prediction error method for identification of LPV models

This paper is concerned with identification of linear parameter varying (LPV) systems in an input-output setting with Box-Jenkins (BJ) model structure. Classical linear time invariant prediction error method (PEM) is extended to the LPV PEM. Under the new LPV framework, identification of two types of input-output LPV models is considered: one is based on parameter interpolation and the other is based on model interpolation. The effectiveness of the proposed solution is validated by comparison with other existing LPV identification approaches through simulation examples and demonstrated by experiment studies.     

Probabilistic characterisation of model error using Gaussian mixture model—With application to Charpy impact energy prediction for alloy steel

A novel approach to characterise the model prediction errors using a Gaussian mixture model is proposed. The motivation for this work lies behind many data models that are developed through prediction error minimisation with the assumption of a normal noise distribution. When the noise is non-normal, which may often be the case in complicated data modelling scenarios, the model prediction errors may contain rich information, which can be further exploited for model refinement and improvement. The key contents presented in this paper include: choosing the relevant variables to form the error data, optimising the number of Gaussian components required for the error data modelling, and fitting the Gaussian mixture parameters using an expectation-maximisation algorithm. Application of the proposed method for further model improvement, within the framework of hybrid deterministic/stochastic modelling, is also discussed. Preliminary results on the real industrial Charpy impact energy data for heat-treated steels show its effectiveness for model error characterisation, and the potential for model performance improvement in terms of prediction accuracy as well as providing accurate prediction confidence intervals.     

一种电子系统的贮存可靠性预计算法

本文给出了一种电子系统的贮存可靠性预计的算法,并通过实例说明了算法的步骤。     

基于FPGA的AVS编码器帧内预测实现

针对帧内预测的快速算法,由于DSP架构软件顺序执行的局限性难以满足实时性要求,而FPGA以其高速的计算速度和强大的并行处理能力成为H.264和AVS编解码的理想平台。本文在FPGA平台上采用资源共享、高并行和多流水线结构实现了亮度帧内预测算法。该方法在分析AVS帧内亮度5种预测模式的基础上,将像素预测与模式判决在一个模块中完成,并且利用各模式预测的相似性,实现运算单元共享和多种模式并行执行,兼顾了处理速度和实现代价。仿真及综合结果表明该设计能够完全满足标清(704×576,30f/s)数字视频的实时处理要求。     

人工神经网络在注塑参数预测中的应用

应用人工神经网络技术建立了注塑工艺参数值快速预测的双层BP网络模型。利用CAE软件进行模拟，获取训练样本。用Matlab语言编制了应用程序，对网络的训练及参数预测的过程进行求解。将网络预测结果与模拟所得验证数据进行比较和误差分析，显示出BP网络的稳定性和可靠性。经实例验证，表明人工神经网络技术应用于注塑参数值的快速预测和选取方面是可行的。     

基于支持向量机的压裂井层优选

压裂是油田重要的增产措施，合理选择压裂井层是一项十分复杂的工作。在对影响压裂效果的各种因素综合分析的基础上，提出了基于支持向量机技术的压裂效果预测方法。采用该方法，利用油田开发动、静态数据，构建了压裂井层优选的支持向量机模型，对大庆油田采油八厂样本进行处理，符合率达89％以上，并在不同数目学习样本的情况下同模糊神经网络作了比较，性能远优于模糊神经网络，可很好地克服过学习问题。