Main Controlling Factors on Hydrocarbon Accumulation and Distribution in Marine Sedimentary Sequences in South China

Multiple source rock assemblages were deposited in the sedimentary provinces in South China in geologic history,and some of them were destructed by and some survived against multiple tectonic movements.Therefore,multiple sources,mixed sources,and uneven distribution of sources occurred in the marine sedimentary basins in South China during the late stage of hydrocarbon pooling.Epidiagenesis of the marine carbonate reservoirs and its modification to reservoir poroperm characteristics determined the formation and the scale of natural gas pools.The exploration practices show that the large to medium gas fields mainly occur in areas with high-quality reservoirs.Detailed study of the paleo-oil accumulations and typical oil and gas reservoirs reveals that the basins experienced multiphase superimposition and modification,leading to the distribution of the Paleozoic paleo-oil accumulations and bitumen in the peripheral areas.The phenomenon that oil and gas production concentrates in the Sichuan basin indicates that the overall sealing conditions of a basin determine the oil/gas potentials and the scale of oil and gas production.This is a critical factor controlling the accumulation and distribution of gas in the marine sequences in South China.The early oil and gas pools in the Yangtze platform left billions of bitumen in the peripheral areas due to the destruction of seals.Since the Himalayan,"late-generation and late-accumulation" gas pools represented by the gas pools in the Sichuan (四川) basin were formed in the marine sedimentary sequences in South China as a result of the change of the sealing conditions.Current gas discoveries appear to be "paleo-generation and paleo-accumulation" gas pools but actually are "late-generation and late-accumulation" gas pools.These patterns of hydrocarbon pooling clearly depict themselves in western Sichuan basin and Weiyuan (威远)gas field.It is revealed that the gas pools in the Sichuan basin were mainly formed as a result of hydrocarbon phase change (thermal cracking of oil to gas),miscible migration,and dynamic equilibration since the Himalayan.A large number of gas pools were formed in the Himalayan and the gas pools in the marine sequences are characterized by late pooling; this kind of gas fields/pools are controlled by:(1) effectiveness of modification and superimposition of the marine basins,(2) effectiveness of the source rocks,(3) effectiveness of the overall preservation conditions,and (4) effectiveness of plays.     

BP神经网络模型在澜沧江流域径流量模拟中的应用

河川径流等水文时间序列属于复杂的非线性系统,使用回归分析等传统的分析方法,难以获取和描述其内在关联和变化规律。利用现有的相关站点的径流量历史数据和输沙量、降水量数据,在进行规格化处理和主成分分析的基础上,利用三层BP人工神经网络模型,对澜沧江流域上游昌都站径流量与各关联因子之间复杂的非线性映射关系进行模拟,采用拟牛顿算法对模型进行训练,模拟结果达到期望精度要求,并利用1982年~1985年实测数据进行模型验证。结果证明利用BP神经网络模型对澜沧江流域站点的月径流量序列进行模拟、预测和数据补缺处理具有可行性。     

四川汶川大地震的构造分析

2008年5月12日在汶川映秀(北纬31.0°,东经103.4°)发生8级大地震,而后发生万余次余震,其最大震级为6.4级.此次地震属主震-余震型地震.通过构造分析认为.汶川大地震是构造地震,主要受龙门山断裂带的强烈活动控制.它是一种板内地震,其动力来源来自印度板块与欧亚板块的碰撞.而成都平原处于稳定地块中,尽管离震中较近,然受地震的影响有限,是比较安全的.     

国内部分IGS站点汶川地震前后的时变特征

在介绍各种GPS静态方法和动态定位方法的基础上,对我国武汉及昆明IGS站点在汶川地震时的时变特征进行了分析,通过数据处理可以发现,IGS跟踪站点在地震前后的动态结果无论是采用双差结果还是精密单点定位结果,都显示其有明显的变化,而采用动态定位更能显示这种变化。同时,IGS跟踪站点长期变化还无法反映地震的前兆影响。     

矿井涌水量混沌时间序列分析与预测

矿井地下水系统是个非线性系统,由于地下水系统的复杂性,很难完全确定影响矿井涌水量的因素,因此传统的矿井涌水预测表现出较大的局限性。而混沌时间序列分析能够利用单变量处理方法,分析矿井地下水系统中,由于多因素耦合作用所产生的矿井涌水量时间序列。通过对某矿区1985年9月至2005年2月矿井涌水量时间序列资料的分析,显示矿井涌水量时间序列具有混沌特征;通过对比预测值和实测值,表明预测精度高。因此,混沌时间序列分析方法用于矿井涌水量预测是可行的。     

"5·12" 汶川大地震极重灾区地震堰塞湖应急遥感调查

5·12汶川大地震发生后,中国国土资源航空物探遥感中心在空军、总参等大力支持和协助下,快速获取了北川、汶川等极重灾区的航空遥感资料.本文介绍了以航空遥感资料为主,结合北京1号小卫星、CBERS-02B等卫星数据快速调查地震堰塞湖的成果.     

水文时间序列周期识别的新思路与两种新方法

针对水文序列周期识别的困难,提出首先对原序列处理,再识别周期的新思路,同时提出两种新方法:一种是模拟延长序列法,即通过建模延长原序列,再应用最大熵谱分析法(MESA)对延长序列识别周期;另一种方法是构建主频序列法,应用小波重构法重构原序列主频部分,然后应用MESA对重构序列进行周期识别。结合实例,运用多种方法对同一序列进行周期识别。分析结果表明:由于受序列长度偏短、偏态性、复杂随机成分等因素的影响,传统单一处理方法(周期图法、FFT、MESA、小波分析)周期识别效果并不理想,而使用两种新方法可以有效地减小或消除上述因素的影响,周期识别效果有明显改善。     

四川汶川县大地震引发的次生地质灾害

2008年5月12日14时28分在四川省汶川县发生了8级大地震,同时诱发了一系列地质灾害。大批地质工作者奔赴灾区对灾情进行调查、“会诊”。本期照片为成都理工大学及中国科学院水利部成都山地灾害与环境研究所的数名教授及研究员所摄。     

在学习调研阶段总结暨分析检查阶段工作部署大会上的讲话

5月12日汶川大地震发生后,按照党中央、国务院的部署,部里把抗震救灾作为当前压倒一切的任务,做了大量工作。刚才,小苏同志通报了部抗震救灾工作情况。下面,我就学习实践活动谈三点意见。     

动态最小二乘支持向量机学习算法

针对大样本集的训练问题和动态训练样本的模型更新问题,提出了动态最小二乘支持向量机学习算法.该算法充分利用已建好的模型,逐渐加入新样本,并可删除位于任何位置的非支持向量,避免了矩阵求逆运算,保证了算法的高效率.大坝变形及电离层延迟两个时间序列的预报实例表明,该算法具有计算时间短、预报精度高的特点.