一种基于GN算法的动态图划分方法

随着图规模的急剧增长，对动态图进行实时处理的需求日益增加。大多现有的算法针对静态图划分是有效的，直接用其处理动态图会带来较大的通信开销。针对该问题，提出一种基于GN算法的动态图划分方法。首先收集一段时间内加入动态图中的顶点；然后，利用GN算法对这些新加入的顶点进行预划分，产生若干个内部联系紧密的社区；最后，将预划分产生的社区结果插入到已经划分好的当前图中。实验从交叉边数和负载均衡度两方面将该方法与传统流式划分方法进行比较，结果表明， 在公开数据集上，该方法的交叉边数降低了13%，负载均衡度减少了42.3%。由此可见，该方法的划分质量明显优于传统的流式划分方法。     

Experimental study on prediction model of wet gas pressure drop across single-orifice plate in horizontal pipes in the low gas phase Froude number region

The pressure drop prediction of wet gas across single-orifice plate in horizontal pipes had been solved satisfactorily under an annular-mist flow in the upstream of orifice plates. However, this pressure drop prediction is still not clearly determined when the upstream is in an intermittent flow or stratified flow, which is corresponding to a region of low Fr_G (gas phase Froude number) in the flow pattern map of wet gases. In this study, the wet gas pressure drop across a single-orifice plate was experimentally investigated in the low Fr_G region. By the experiment, the flow pattern transition in the downstream of single-orifice plates, as well as the effects of Fr_G and Fr_L (liquid phase Froude number) on Φ_G (gas phase multiplier), were determined and compared when the upstream is in the flow pattern transition and the stratified flow region, respectively. Prediction performances were examined on the available pressure drop models. It was found that no model could be capable of jointly predicting the wet gas pressure drop in the low Fr_G region with an acceptable accuracy. With a new method of correlating Fr_G and Fr_L simultaneously, new correlations were proposed for the low Fr_G region. Among which the modified Chisholm model shows the best prediction accuracies, with the prediction deviations of Φ_G being within 7% and 3% when the upstream is in flow pattern transition and stratified flow region, respectively.     

波形驱动下多参数约束高分辨率反演方法——以四川盆地渝西地区龙马溪组页岩气为例

基于叠后地震波形指示反演在薄储层预测方面的优势，以及叠前弹性参数较叠后弹性参数信息更丰富、对储层的敏感性更高的特点，根据岩石物理资料，应用地震驱动+储层构型约束的高精度叠前随机反演方法，探寻定量表征优质薄页岩的技术，以期为深层页岩气地质甜点预测提供技术支撑。首先，基于道集波形相似性、AVO特征和空间距离的三变量优选方法提取结构相似的井数据作为空间估值样本，然后建立待判别道集初始模型；其次，以统计的弹性阻抗作为先验信息，应用“基于叠前道集特征指示的马尔科夫链-蒙特卡洛随机模拟算法”进行叠前地震波形指示反演，最终得到高精度的叠前弹性参数反演成果。实际应用表明，应用所提方法有效预测了龙一段一亚段1-2小层优质页岩厚度，基于特征参数的井震高频模拟精确模拟了龙一段一亚段1-2小层地质甜点参数，为页岩气勘探提供了技术支撑。     

川东南丁山地区燕山期—喜马拉雅期差异构造-热演化与页岩气保存

中国的海相富有机质页岩经历了多期构造改造，其含气性具有明显的差异。页岩气在不同构造演化阶段的保存条件是揭示页岩气差异富集机理的关键科学问题之一，开展构造-热演化研究可以明确其热演化史和构造隆升-剥蚀过程，为其评价提供演化格架。研究以丁山地区下古生界页岩为对象，联合磷灰石裂变径迹、磷灰石（U-Th）/He和锆石（U-Th）/He等多个古温标反演热演化史，结合镜质体反射率重建的最高古地温剖面，对丁山地区燕山期以来的差异构造隆升过程和剥蚀量进行了恢复，并在此基础上结合流体包裹体分析对丁山地区龙马溪组页岩的压力演化过程进行了模拟；根据页岩在埋藏—抬升过程中的温、压演化特征，定量表征了不同抬升阶段页岩含气量的变化，建立了龙马溪组页岩"埋藏—生烃—抬升"的演化格架。分析表明，丁山地区在燕山期和喜马拉雅期经历了不同的构造隆升过程。燕山期表现为"早期快速隆升—晚期缓慢隆升"的分段隆升，具有自NW向SE递进隆升且隆升幅度逐渐增大的特征；喜马拉雅期表现为整体快速隆升。燕山期是丁山地区产生差异构造隆升的主要时期。受这种差异构造隆升-剥蚀作用的影响，龙马溪组页岩的降温、降压过程和页岩气的散失过程具有明显的差异。燕山期的差异构造隆升是造成丁山地区龙马溪组页岩含气性呈平面分带的主要原因。     

基于Prophet人工智能算法的网络潮汐效应预测研究

本文使用Prophet人工智能算法研究与预测移动通信网络“潮汐效应”现象，探索网络“潮汐效应”在优化网络资源配置实现网络降本增效的作用。Prophet人工智能算法是一种简单、有效，且易于实现的人工智能算法。通过facebook的人工智能开源框架fbprophet，研究4G网络PRB利用率等网络资源指标的“潮汐效应”，并预测这些网络资源指标在未来的变化趋势，用来指导当前4G网络减容、扩容和4/5G节电节能等，实现优化网络资源配置达到降本增效的目的。     

仪器仪表产品脉冲耐压试验要求和保护器件的选择

仪器仪表产品的脉冲耐压试验是产品型式试验、例行试验中的基本内容。通过对GB4793.1标准的整理,归纳了仪器仪表产品的脉冲耐压试验要求。通过对试验方法和设备特性的梳理,总结了特性参数,并介绍了应对脉冲耐压试验可采取的保护器件的类别及选用方法。     

Neural network-based hybrid models developed for free radical polymerization of styrene

In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.     

气藏型储气库注气期试井分析探讨

针对气藏型储气库注采井注采过程中储层物性参数影响因素不明确、注采能力不对称的问题。基于相国寺储气库井下连续油管试井测试结果,提出储气库注气期“温降效应”、“变表皮效应”的概念,分析了储气库注采过程中温降效应、变表皮效应以及储层应力敏感对注采的影响。通过气藏型储气库注气期试井分析技术,研究各因素在试井曲线上的响应特征以及对试井解释参数的影响。结果表明:①相对于采气期试井测试,注气期测试得到的储层物性参数具有同样的参考价值;②储气库温降效应对于试井解释结果的影响可忽略不计,而在不同注采运行周期内,变表皮效应以及应力敏感效应影响差异较大;③编制储气库注采运行方案时应充分考虑变表皮效应与应力敏感的影响,在不同注采运行周期内开展试井测试获取准确的储层参数值。研究成果为储气库试井测试与解释提供了重要的研究依据和理论指导。