应用最小二乘支持向量机识别广利油田沙四段储层岩性

广利油田沙四段储层岩性以为粉砂岩、细砂岩和不等粒砂岩为主,岩性非均质性较强,应用常规测井交会图识别岩性难度大.本文介绍了最小二乘支持向量机的原理及实现流程,利用网格搜索法确定的参数对δ2(C,δ)=(2000,0.707)开展了广利油田沙四段储层岩性的测井识别.应用效果表明,测井识别岩性与岩心分析资料的符合率达到了86％,可满足广利油田沙四段储层岩性识别的需要.     

武山气氡脱气装置改进试验介绍

为了排除模拟采样的影响,对武山气氡的脱气装置进行了改进。通过对比脱气装置改进前后产出的数据,改进后的资料数据明显好于改进之前。     

延寿地震台水温梯度测量及结果分析

根据《szw-1A型数字式温度计（2004）}安装规范要求,对延寿台1号观测井进行水温梯度测量,找到该井温度波动最小部位,重新确定地热探头投放位置,以提高水温观测数据质量,得到信噪比较高的地热前兆信息。     

Spatial interpolation using MLP–RBFN hybrid networks

It is easy for a multi-layered perception (MLP) to fit a stratified spatial interpolation pattern whose form is close to open surface; while it is easy for a radial basis function network (RBFN) to fit a pocket (radial) spatial interpolation pattern whose form is close to closed surface. However, in the real world, the spatial interpolation pattern may consist of stratified and pocket patterns. Neither MLP nor RBFN can fit the pattern easily. To combine their advantages to fit the complex hybrid spatial interpolation patterns, in this article we propose a novel neural network, MLP–RBFN hybrid network (MRHN), whose hidden layer contains sigmoid and Gaussian units at the same time. Although there are two kinds of processing units in MRHN, in this study we used the principle of minimizing the error sum of squares to derive the supervised learning rules for all the network parameters. This research took rainfall distribution in Taiwan as a case study. The results show that (1) the prediction error of the testing dataset outside the training dataset demonstrated that MRHN was the most accurate among the three networks, RBFN was the next best, and MLP was the worst; (2) the MLP model seriously underestimated the values of high observed rainfall; (3) over-learning may be a serious shortcoming of using RBFN in spatial interpolation applications; (4) MRHN may have better generalization learning capacity than RBFN in spatial interpolation applications.     

Precipitation-runoff simulation for a Himalayan River Basin, India using artificial neural network algorithms

In the Himalayan regions, precipitation-runoff relationships are amongst the most complex hydrological phenomena, due to varying topography and basin characteristics. In this study, different artificial neural networks (ANNs) algorithms were used to simulate daily runoff at three discharge measuring sites in the Himalayan Kosi River Basin, India, using various combinations of precipitation-runoff data as input variables. The data used for this study was collected for the monsoon period (June to October) during the years of 2005 to 2009. ANNs were trained using different training algorithms, learning rates, length of data and number of hidden neurons. A comprehensive multi-criteria validation test for precipitation-runoff modeling has been undertaken to evaluate model performance and test its validity for generating scenarios. Global statistics have demonstrated that the multilayer perceptron with three hidden layers (MLP-3) is the best ANN for basin comparisons with other MLP networks and Radial Basis Functions (RBF). Furthermore, non-parametric tests also illustrate that the MLP-3 network is the best network to reproduce the mean and variance of observed runoff. The performance of ANNs was demonstrated for flows during the monsoon season, having different soil moisture conditions during period from June to October.     

Research on China’s high-grade highway design index of asphalt pavement with granular base

Based on the 2006 Chinese asphalt pavement deflection value design index, we used KENLAYER Pavement Analysis and Design software and 1stOpt statistical analysis software to carry on the nonlinear regression, this paper establish high-grade highway design equations for the compressive strain of soil sub-base top (CSSBT) and the radial compressive stress of semi-rigid base top (RCSRBT). The correlation coefficients inspection standard to get precise proof, which means that our granular base design equations have high credibility and can be used in the Chinese design index of asphalt pavement with granular base (APGB).     

镇泾油田红河105—1057井区微生物异常与含油气性关系

油气微生物勘探（MPOG）是地表油气藏评价的一个分支,主要研究近地表土壤层中微生物异常与地下深部油气藏的相关关系,它以成本低廉,简便快速,多解性小的特点,在镇泾油田得到充分的验证。在镇泾油田内,以微生物异常为基础,结合已有的区域油气地质、钻井和测井等资料,对微生物异常区域进行分析研究,重点解释说明了镇泾油田红河105—1057井区长821小层与油气异常的关系,并圈定出2个异常区,其异常连片分布,异常区的异常平均值较高,反映本工区内油藏控制面积较大。本工区天然气微生物异常整体水平较低,且基本上与油藏微生物异常区吻合,异常区零星分布,表明本工区的天然气多系原油溶解气。最后对工区油藏分级评价,并根据微生物勘探结果和地质特征等综合因素,在工区部署3口建议井。     

虚拟震源方法用于探测地下陡倾角地质构造

当常规地震勘探数据处理方法不能对地下存在的陡倾角地质构造获得满意的成像效果时,应用虚拟震源方法将垂直地震剖面( VSP,vertical seismic profile) 数据重构成单井剖面( SWP,single well profile) 数据,可以使得观测系统重构到更接近于井附近目标体的位置,震源和检波器都位于复杂的覆盖之下并避免井和地表之间未知速度信息的影响,从而获得对地下地质构造更好的成像分辨率。尤其是当地下存在诸如断层这样的陡倾角地质构造时,虚拟震源方法可以对目标体产生很好的成像效果。本文进行了声波数据和弹性波数据的模拟实验,将模型中的陡构造设置为断层,测试虚拟震源方法用于探测地下陡倾角地质构造的能力,对声波和弹性波介质都获得了满意的成像结果; 在应用稀疏观测系统进行测量时,仍然可以得到较好的成像效果。     

低孔低渗储层自然产能测井预测方法——-以朝阳沟油田为例

油气储层产能预测既是提高油气田勘探效果的关键环节,又可为开发的部署与规划提供重要的基础数据。本文以平面径向流理论为基础,从影响产能的各个参数入手,同时,充分考虑储层内的非均质性,将一个储层分成几个相对均匀的亚层,应用该方法对朝阳沟油田井的自然产能进行了预测,预测的产能级别与测试的产能级别符合,符合率为100%。这种方法不但适用于单层测试,也适用于多层合试。     

单井水文地质参数简化计算及意义

单井抽水试验是水文地质勘探中广泛实施的手段,其导水系数 K M 或渗透系数 K 值通常是从抽水结果的涌水量 Q 和井中水位降数据计算求得.但单井中水量的水位降深,多受井结构完善程度的制约,难以反映出井点处含水层真实的水位降.另外,在基岩含水层试验中,常以抽水井揭露所谓含水层的岩性岩层厚度作为含水层厚,这又给 K 值的确定带来一定的偏差.就日常工作中在对抽水成果整理方面所遇的一些问题提出见解,以共讨论