Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.     

基于查询的新闻多文档自动摘要技术研究

针对新闻文本领域,该文提出一种基于查询的自动文本摘要技术,更加有针对性地满足用户信息需求。根据句子的TF-IDF、与查询句的相似度等要素,计算句子权重,并根据句子指示的时间给定不同的时序权重系数,使得最近发生的新闻内容具有更高的权重,最后使用最大边界相关的方法选择摘要句。通过与基于TF-IDF、Text-Rank、LDA等六种方法的对比,该摘要方法ROUGE评测指标上优于其他方法。从结合评测结果及摘要示例可以看出,该文提出的方法可以有效地从新闻文档集中摘取核心信息,满足用户查询内容的信息需求。     

Automated network management and configuration using Probabilistic Trans-Algorithmic Search

Online configuration of large-scale systems such as networks requires parameter optimization within a limited amount of time, especially when configuration is needed as a response to recover from a failure in the system. To quickly configure such systems in an online manner, we propose a Probabilistic Trans-Algorithmic Search (PTAS) framework which leverages multiple optimization search algorithms in an iterative manner. PTAS applies a search algorithm to determine how to best distribute available experiment budget among multiple optimization search algorithms. It allocates an experiment budget to each available search algorithm and observes its performance on the system-at-hand. PTAS then probabilistically reallocates the experiment budget for the next round proportional to each algorithm’s performance relative to the rest of the algorithms. This “roulette wheel” approach probabilistically favors the more successful algorithm in the next round. Following each round, the PTAS framework “transfers” the best result(s) among the individual algorithms, making our framework a trans-algorithmic one. PTAS thus aims to systematize how to “search for the best search” and hybridize a set of search algorithms to attain a better search. We use three individual search algorithms, i.e., Recursive Random Search (RRS) (Ye and Kalyanaraman, 2004), Simulated Annealing (SA) (Laarhoven and Aarts, 1987), and Genetic Algorithm (GA) (Goldberg, 1989), and compare PTAS against the performance of RRS, GA, and SA. We show the performance of PTAS on well-known benchmark objective functions including scenarios where the objective function changes in the middle of the optimization process. To illustrate applicability of our framework to automated network management, we apply PTAS on the problem of optimizing link weights of an intra-domain routing protocol on three different topologies obtained from the Rocketfuel dataset. We also apply PTAS on the problem of optimizing aggregate throughput of a wireless ad hoc network by tuning datarates of traffic sources. Our experiments show that PTAS successfully picks the best performing algorithm, RRS or GA, and allocates the time wisely. Further, our results show that PTAS’ performance is not transient and steadily improves as more time is available for search.     

宁夏宁东供水工程主机设备形式参数选择与国际招标

宁东供水工程供水水泵共有卧式和立式离心泵2种方案可供选择,也是工程论证时争论的焦点,经综合比较及主机设备的国际招标,最终确定了奥地利安德里兹公司的卧式双吸中开式离心泵为宁东供水工程的供水水泵。     

现场总线在冶炼烟气制酸系统中的应用实践

分析了冶炼烟气制酸工艺的特点，介绍了由PROFIBUS-DP现场总线构建的冶炼厂烟气制酸监控系统，详细介绍了其硬件配置及相应的软件设计。     

测井资料在优选压裂层位中的应用

压裂是低孔、低渗油层改造的主要增产措施。根据常规测井资料、岩石物理实验分析数据及试油成果。研究储层四性关系，确定区块的低孔、低渗储层评价标准。进一步对油层进行分类，有针对性地为油层压裂提供施工层位和准确的储层参数。通过商543区块实例，阐述了选择压裂层位的方法。     

数字化蓄热式钢包烘烤器及自动控制系统

蓄热式燃烧技术和脉冲燃烧技术是新型的节能环保燃烧技术。数字化蓄热式钢包烘烤器控制系统利用数字化脉冲控制把这两项技术结合在一起。这大大降低了能耗，减少环境污染。本文针对数字化蓄热式钢包烘烤器，提出升温阶段采用自寻优算法，不断调整换向时间，并能记忆最优参数，保证经过几次烘烤后系统整体最优；保温阶段即采用脉冲控制的方法。     

DFIT测试在长庆气田开发中的应用

与常规的小型测试压裂不同，流体注入诊断测试（DFIT）是通过关井阶段出现的拟线性流或拟径向流阶段求取储层压力，采用改进的梅耶霍夫方法（M-method）解释储层渗透率。以长庆榆林气田一口开发井为例，依据DFIT的技术原理和测试步骤，对该井测试压降数据进行解释分析，结果可信度较高，证明了该项工艺在长庆低压低渗油气田的可行性。     

横泉水库大坝土料碾压试验

在取得土料场土料物理力学性质参数的情况下，遵循碾压试验基本原理、方法及参数组合，进行了现场碾压试验，通过试验成果的分析，得出了大坝碾压试验结论，为大坝土方填筑提供了技术依据。     

焊膏印刷工艺参数的选择与优化

表面贴装工艺流程的关键工序之一就是焊膏印刷,其工艺控制的好坏直接影响着装配的线路板的质量。通过对印刷工艺参数的分析,利用试验设计的方法来找出关键工艺参数,并加以优化设定,从而为控制工序参数奠定基础。