低流度油藏分类及开发特征研究

低流度油藏指流度低于30×10-3μm2/mPa.s的油藏。根据流度大小以及油藏在开发生产中表现出的特征,可将低流度油藏分为一般低流度油藏、特低流度油藏和超低流度油藏。对大港低流度油藏的研究表明,由于低流度油藏地质情况复杂,地层认识不清,因而开发特征复杂,表现为采油速度低,采出程度和采收率低;注水效果差,油藏井距不适应;油井见水后含水迅速上升,产量则急剧下降。     

3G用户细分研究

对3G用户的细分方法及3G目标市场的定位进行了初步的研究，提出了3G用户细分的体系框架和3G目标市场定位的考虑要素及初步的定位建议。     

地震属性解释技术的研究及确定性分析

随着油气勘探开发的不断发展，储层预测、属性分析等技术得到了广泛的应用，在生产中取得了较好的效果。但地震属性的概念，分类方法，地震属性之间、地震属性与储层参数之间的相互关系仍较模糊，各种地震属性解释方法虽然种类繁多，但缺乏系统的研究，这使得地震属性在应用中存在较大的不确定性。文章通过系统研究地震属性的概念、储层地震响应的基本特征，提出了地震响应的核心是反映地震波在介质传播过程中的能量变化，所有能引起地震能量发生变化的介质的不均质性均可导致地震属性的变化，由此提出了一种新的地震属性分类方法，并认为大部分地震属性只是基本属性在不同域中的不同表现形式。在分析现有地震解释技术的基础上，提出了对地震属性解释的新认识，强调了地震属性对地层岩性油气藏勘探的重要性，对属性间的关系及地震属性解释技术的确定性进行了研究，提出了岩性油气藏地震解释的工作流程及对地震资料采集和处理的要求。     

分类挖掘技术在高校人事管理中的应用

探讨分类挖掘技术在高校实际工作中的应用方式与应用领域。以高校人事管理为模型，应用数据挖掘中决策树算法，对高校人力资源数据源中的信息进行分析，发现其中有价值的数据模式，寻找其中存在的关系和规则，对高校人才规划提供比较客观的决策支持。     

Biotransformation of l‐menthol by twelve isolates of soil‐borne plant pathogenic fungi (Rhizoctonia solani) and classification of fungi

The microbial transformation of l‐menthol ( 1 ) was investigated by using 12 isolates of soil‐borne plant pathogenic fungi, Rhizoctonia solani (AG‐1‐IA Rs24, Joichi‐2, RRG97‐1; AG‐1‐IB TR22, R147, 110.4; AG‐1‐IC F‐1, F‐4, P‐1; AG‐1‐ID RCP‐1, RCP‐3, and RCP‐7) as a biocatalyst. Rhizoctonia solani F‐1, F‐4 and P‐1 showed 89.7–99.9% yields of converted product from 1 , RCP‐1, RCP‐3, and RCP‐7 26.0–26.9% and the other isolates 0.1–12.0%. In the cases of F‐1, F‐4 and P‐1, substrate 1 was converted to (?)‐(1S,3R,4S,6S)‐6‐hydroxymenthol ( 2 ), (?)‐(1S,3R,4S)‐1‐hydroxymenthol ( 3 ) and (+)‐(1S,3R,4R,6S)‐6,8‐dihydroxymenthol ( 4 ), which was a new compound. Substrate 1 was converted to 2 and/or 3 by RRG97‐1, 110.4, RCP‐1, RCP‐3 and RCP‐7. The structures of the metabolic products were elucidated on the basis of their spectral data. In addition, metabolic pathways of the biotransformation of 1 by Rhizoctonia solani are discussed. Finally, from the main component analysis and the differences in the yields of converted product from 1 , the 12 isolates of Rhizoctonia solani were divided into three groups based on an analysis of the metabolites. Copyright © 2003 Society of Chemical Industry     

川西新场气田JS_2~1气藏测井多参数评价方法

针对川西新场气田JS12气藏开展了多参数的综合评价研究,多参数包括储层参数、裂缝参数、储层分类厚度参数。首先,开展了储层参数及裂缝参数的计算,以岩心分析结果为依据,建立了孔隙度、渗透率、含水饱和度计算模型。利用铸体薄片进行了微裂缝识别,对样品的裂缝参数和测井曲线进行相关分析,建立了测井计算裂缝参数的数学模型;其次,对储层进行聚类分析,通过聚类将储层分为三类;最后,将计算的裂缝参数、储层分类中的Ⅰ类储层厚度、孔渗饱等参数进行正态化处理,拟和测试的无阻流量与这些参数建立多元回归计算模型,即综合含气指标的数学计算模型。以综合含气指标为主要依据,进行储层综合分类和评价,指出了有利的含气区域和可供挖潜井的建议。该方法在指导气藏开发中获得了良好效果。     

低温脂肪酶产生菌的筛选、鉴定及其部分酶学性质

从南极乔治王岛冻土来源的76株低温细菌中筛选到13株低温脂肪酶产生菌，对其中的BTsl0022菌株进行鉴定。通过生理生化特征、16s rDNA基因序列的同源性和系统发育分析发现，菌株RTsl0022属于假单胞菌属(Pseudomonas)，但与已定名的假单胞菌有一定的差异，与未定名的Pseudomonas sp．PsB的亲缘关系最接近，故将其暂定名为Pseudomonas sp．BTsl0022。对该菌脂肪酶的酶学性质初步研究表明，酶的最适作用温度为24℃，对热敏感，60℃处理30min仅残留25％酶活性，酶的适宜作用pH范围在7．0～9．0，最适pH为8．0。     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.