Correlation methods of base-level cycle based on wavelet neural network

The authors discussed the method of wavelet neural network(WNN) for correlation of base-level cycle.A new vectored method of well log data was proposed.Through the training with the known data set,the WNN can remenber the cycle pattern characteristic of the well log curves.By the trained WNN to identify the cycle pattern in the vectored log data,the ocrrelation process among the well cycles was completed.The application indicates that it is highly efficient and reliable in base-level cycle correlation.     

樊家川油田河流相储层高分辨率层序地层特征

针对河流相储层标志层缺乏、砂体叠置掘蚀严重、难以精细等时对比的难点，应用基准面旋回原理，重点解剖典型取心井及其测井响应模式，将樊家川油田主要含油层系中侏罗世延安组Y8～Y10油组划分为7个短期、3个中期、2个长期基准面旋回层序，对应于不同级次的储层成因单元，同时建立以中期旋回为年代地层格架。短期旋回为等时地层对比单元的层序地层格架，对河流相多期叠覆储层进行了精细等时划分与对比，详细分析了河流相储层短期基准面旋回类型及其叠加式样。探讨了各级基准面旋回对储层分布的控制作用。     

Base-level Chang and Sequence Stratigraphy of Lishu Fault Lacustrine Basin

ＩｎｔｒｏｄｕｃｔｉｏｎＴｈｅｔｈｅｏｒｙｏｆｓｅｑｕｅｎｃｅｓｔｒａｔｉｇｒａｐｈｙｈａｓｂｅｅｎｕｓｅｄｉｎｔｈｅｒｅｓｅａｒｃｈｏｆｃｏｎｔｉｎｅｎｔａｌｂａｓｉｎｆｏｒｍａｎｙｙｅａｒｓ,ｂｕｔａｕｎｉｖｅｒｓａｌｌｙａｃｃｅｐｔｅｄｔｈｅｏｒｅｔ     

米兰科维奇旋回识别与天文标尺的建立:以莫索湾地区莫21井三工河组一段为例

依据米兰科维奇理论识别划分旋回，是对传统层序地层学的有力补充，具备高等时约束性，可实现高分辨率旋回识别与高精度等时地层格架的建立。莫索湾地区是准噶尔盆地油气勘探的有利区域之一，其中三工河组一段发育了有效的、成规模的致密气储集层。利用自然伽马测井数据，通过频谱分析等方法，对研究区目标层位进行了米兰科维奇信号识别与旋回划分。以405 ka周期滤波曲线作为基准，借助三工河组底界地质年龄，建立了浮动天文标尺。研究表明，三工河组一段存在米兰科维奇旋回。短期偏心率125 ka周期控制厚度约为29.73 m的旋回；斜率54 ka周期控制厚度约为12.47 m的旋回；岁差24 ka周期控制厚度约为5.62 m的旋回。三工河组一段地质年龄为189.951~190.800 Ma，共识别出了7个短期基准面旋回，由此划分出了7个五级层序，沉积速率从下到上，虽呈现出“减小-增大-持续减小-持续增大”的趋势，但整体变化不大；发育了B1、B2、C1三种类型的短期基准面旋回。      

测井技术在泰安姚庄油页岩勘探中的识别应用

油页岩是一种很有前景的油气资源,测井技术是油页岩矿勘探开发的关键技术。该文依托姚庄地区油页岩矿钻孔的测井工作,通过对典型钻孔zk002的测井资料进行分析对比,得出测井曲线的变化与岩层有机质含量的关系,识别出地层岩性,并定出含油页岩层厚度,其结果与地质岩心钻探结果基本一致,因此,测井技术能较好地识别含油页岩层。     

冰后期长江河口段古河谷地层层序特征

末次盛冰期,由于基面大幅度下降,长江河口段形成了深切古河谷。河流携带的部分粗砂砾在河床上形成滞留沉积。冰后期海平面上升引发的海侵造成了长江古河谷的充填和河床相、河漫滩河口湾相和部分河口湾浅海相的形成。尔后的进积产生了部分河口湾浅海相及三角洲相。根据钻孔资料绘制的古河谷地质剖面图显示,冰后期海侵旋回出现在三角洲顶点到现在河口口门地区,溯河而上受海洋影响的程度逐渐减弱。古河谷的中部,靠近主泓线附近,沉积层序完整。古河谷的边缘地带,往往缺失底部的河床相砂砾层。底部的厚层河床相砂砾层是由河床滞留沉积和溯源加积堆积形成的,在古河谷沉积层序中存在着多种沉积间断。     

一种基于BDS-3四频数据的周跳探测与修复方法

为充分发挥BDS-3四频观测信息的优势,提出一种周跳探测与修复方法。首先对无几何相位组合和无几何无电离层组合系数进行优选,联合3个无几何相位组合和1个无几何无电离层组合对不同周跳进行探测,然后使用最小二乘法估计周跳浮点解,最后采用LAMBDA方法进一步得到周跳整数解。经过BDS-3四频观测数据验证可知,本文方法可有效、快速地识别各类周跳,并对其进行修复。     

������������Ķ����ջ��ַ�������̽�����޸�

???????μ?????????????????????????????÷????????????????????????????????????????????ü???????????????????????????????????????￡??÷???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????180s?????????????????????Ч????????????????????????     

一种基于双频非差GNSS相位数据的周跳实时探测与修复方法

在TurboEdit方法的基础上,提出一种双频非差GNSS相位数据周跳实时探测与修复算法。在MW组合中,根据卫星高度角进行定权并结合移动窗口递推计算宽巷模糊度。在电离层残差组合的基础上进行历元间二次差分,以更好地减弱电离层残差对周跳检验量的影响。当探测出存在周跳的历元之后,采用搜索法进行修复。通过采用30 s的静态数据和1 s的动态数据,并在观测数据中模拟加入小周跳、大周跳对该方法分别进行测试。结果表明,该方法可正确实现周跳的实时探测与修复。     

基于TECR的GPS三频数据周跳探测方法研究

基于三频伪距相位和无几何相位组合周跳探测与修复的基本原理,分析电离层活跃期电子含量变化率的特点,提出削弱电离层影响的三频组合周跳探测与修复方案。实验表明,本文方法能够有效探测和修复电离层活跃观测条件下观测数据中的所有周跳,且周跳探测阈值较小,不存在敏感周跳。     

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.     

太北开发区井震结合精细构造描述及应用

太北开发区河道砂体发育规模小,钻遇率低,井网控制程度低,现井网条件下砂体预测精度低,断层认识精度低,单砂体注采关系不完善,开发效果差,开展井震结合精细油藏描述研究.井震结合构造认识成果表明:断层较以往认识结果相比变化较大,利用各类动静态资料分析断层的描述精度,认为井震结合断层描述精度高;井震结合微幅度构造描述更加精细,通过油水井的生产动态变化,研究微幅度构造对开发效果的影响;应用井震结合成果及多学科油藏研究成果,分析断层变化井区的剩余油,指导断层变化井区的补充井布井、注采系统调整及油田外扩调整,均取得较好效果.     

Fishery stock assessment of Kiddi shrimp (Parapenaeopsis stylifera) in the Northern Arabian Sea Coast of Pakistan by using surplus production models

Pakistani marine waters are under an open access regime.Due to poor management and policy implications,blind fishing is continued which may result in ecological as well as economic losses.Thus,it is of utmost importance to estimate fishery resources before harvesting.In this study,catch and effort data,1996-2009,of Kiddi shrimp Parapenaeopsis stylifera fishery from Pakistani marine waters was analyzed by using specialized fishery software in order to know fishery stock status of this commercially important shrimp.Maximum,minimum and average capture production of P.stylifera was observed as 15 912 metric tons(mt)(1997),9 438 mt(2009) and 11 667 mt/a.Two stock assessment tools viz.CEDA(catch and effort data analysis) and ASPIC(a stock production model incorporating covariates) were used to compute MSY(maximum sustainable yield) of this organism.In CEDA,three surplus production models,Fox,Schaefer and Pella-Tomlinson,along with three error assumptions,log,log normal and gamma,were used.For initial proportion(IP) 0.8,the Fox model computed MSY as 6 858 mt(CV=0.204,R~2=0.709) and 7 384 mt(CV=0.149,R~2=0.72) for log and log normal error assumption respectively.Here,gamma error produced minimization failure.Estimated MSY by using Schaefer and Pella-Tomlinson models remained the same for log,log normal and gamma error assumptions i.e.7 083 mt,8 209 mt and 7 242 mt correspondingly.The Schafer results showed highest goodness of fit R~2(0.712) values.ASPIC computed MSY,CV,R~2,F_(MSY)and B_(MSY) parameters for the Fox model as 7 219 mt,0.142,0.872,0.111 and 65 280,while for the Logistic model the computed values remained 7 720 mt,0.148,0.868,0.107 and 72 110 correspondingly.Results obtained have shown that P.stylifera has been overexploited.Immediate steps are needed to conserve this fishery resource for the future and research on other species of commercial importance is urgently needed.     

克拉玛依油田八区克上组砾岩油藏测井描述

克拉玛依油田八区克上组储层发育,油气资源丰富,为克拉玛依油田八区的主力油藏.在该油藏研究中,充分利用已有的测井资料,首先研究了测井资料的标准化方法和以测井曲线为基础的岩心归位方法,进而采用神经网络技术将测井信息转化为地质信息,通过该区117口井资料的综合解释和评价处理,为储层研究提供了基本地质参数和油气评价结论,并采用克里金估计模型绘制了砂体分布、油层有效厚度、孔隙度、渗透率及含油饱和度的平面空间分布图,全面分析了储层在空间的分布规律,为区块油藏综合治理和开发提供了可靠依据.     

小波变换在高分辨率层序地层划分中的应用

该文秉承前人运用测井数据的连续小波变换进行层序地层划分的依据和思路,详细论述了测井数据连续小波变换进行高分辨率层序地层划分的具体步骤。以济阳坳陷某钻井C-P地层的测井数据为研究实例,重点探讨了测井数据连续小波变换中参数选取(小波基、最大变换尺度值和不同尺度下小波系数尺度值)的不同得出的连续小波变换波谱图和系数曲线对层序地层划分的影响;依据优选出的各种相关参数,对消除噪声以后的测井数据(自然伽马)进行连续小波变换及高分辨率层序地层划分,共划分为5个长期基准面旋回,35个短期基准面旋回。增加了应用测井数据连续小波变换进行高分辨率层序地层划分的可靠性。     

地形面方法在渭河盆地活断层研究中的应用

本文主要依据地形图和航空照片解译，并经已有地质、钻孔资料验证及野外实地考察的方法来研究地形面及其变形特征，由此确定渭河盆地活断层的分布、最新活动特征及活动规律，为地震预报及地震危险性分析提供依据。     

Extraction method of component waves in full waveform acoustic data and its application

In conventional slowness-time coherence(STC)method,slowness and time need to be searched at the same time,which limits the precision and lowers the efficiency.The dichotomy method combined with slowness-time coherence algorithm aims to enhance the efficiency of data processing and to improve the precision.The algorithm changes the searching pattern of conventional slowness-time coherence method to acquire the slowness of component waves in array acoustic logging data.Based on energy ratio of short time window versus long time window and slowness-time coherence method,the algorithm first acquires the arrivals of the component waves using energy ratio of short time window versus long time window method.It then uses the calculated results as the arrivals in conventional slowness-time coherence method,so the slowness-time two-dimensional searching process is simplified to slowness searching process.Based on dichotomy method,the searching pattern is further optimized in replace of the ergodic searching pattern in conventional slowness-time coherence method,which means that as the iteration proceeds,the current searching interval is reduced to half of the former,so the number of searching times is decreased.The dichotomy method combined with slowness-time coherence algorithm is applied to well L in the data processing.Compared with conventional slowness-time coherence method,for compressional wave,the searching efficiency of the algorithm is 4.53 times better,while for Stoneley wave,the searching efficiency is 1.85 times better.Compared with conventional logging data,the average absolute error of the results of the dichotomy method combined with slowness-time coherence algorithm is 1.14μs/ft smaller than that of the conventional method,while the average relative error is 1.2 percent lower.The dichotomy method combined with slowness-time coherence algorithm shows good results in its application,which can enhance the processing efficiency remarkably while getting reliable results at the same time.     

����һ��֧��������̽��GPS�ز���λ������

???????????????С??????????????????????????????????????????????????????????????????????????????????GPS?????λ????????е?С????????????????????????????????????????????????????????????GPS?????λ??0.5???С??С???????÷??????????????????????Ч??????????????????????     

地质科学大数据统合应用的基本问题

进行地质科学大数据统合利用,涉及一系列理论、方法和技术问题,包括:多源多类异质异构地质数据的采集、存储和管理问题;数据汇聚、集成和结构化、可视化转换问题;数据同化、融合问题和深度挖掘、广度聚联问题;地质大数据有效利用的方式、方法和途径问题,以及云服务的模式和系统架构问题。所面对的挑战是实现结构化-半结构化-非结构化数据、静态勘查数据与动态监测数据的一体化存储、管理、处理和应用,以及大数据与小数据、混杂性数据与精确性数据、模型与数据、有模型与无模型、关联关系和因果关系的矛盾与统一问题。      

Kalman�˲�������̽�����޸��е�Ӧ��

????Kalman??????????????????????е??????????????????????????裬??????????????з??????????????Kalman?????????????λ??????????????????????????????Χ??С????????????????????????С????, Kalman?????????????????????????????????????????????