叠后地震属性分析在油气田勘探开发中的应用

地震属性分析技术一直是地震特殊处理和解释的主要研究内容.随着油气勘探开发的发展,地震属性分析技术已经成为油藏地球物理研究的核心内容,是勘探地震与开发地震之间纽带.本文针对鄂尔多斯盆地的低幅度构造、低孔隙、低渗透率、致密性隐蔽油气藏的特点,综合应用相干数据体分析、地震相自动分类定性识别砂体厚度、地震振幅属性分析、频谱分解、多井约束的储层叠后反演等叠后属性分析技术,探索了一套适合该区油气特征的储层横向预测及油气识别模式.为该区油气勘探开发,储量计算提供可靠依据.同时也为隐蔽性油气藏的勘探开发积累了经验.     

奇异性理论在弱异常信息提取中的应用—以冀北隐伏中-酸性侵入岩体为例

冀北地区是河北省矿产资源重要聚集地之一,成矿条件优越,其中中-酸性侵入岩体与银铅锌多金属矿床成矿密切相关。然而,大量的第四系覆盖压制了一些深部及隐伏弱异常成矿信息,使得常规化探方法在该覆盖区提取效果微弱。本文运用奇异性分析及主成分分析对冀北1：20万水系沉积物常量元素化探数据进行分析处理,提取区内成矿元素弱异常,并将其与未进行奇异性处理的结果进行对比。结合区域地质背景、物化探资料圈定覆盖区内与中-酸性侵入岩体有关的异常区,对成矿作用、成矿规律进行分析、讨论,以期揭示区内元素富集规律。结果显示,上述组合方法可以有效提取元素弱异常并进行成矿有利区圈定,并且圈定位置与已知岩体位置、区域构造条件相吻合,共圈出隐伏异常点7个,推测其为成矿远景区。     

环保型弱凝胶护壁护心钻井液研究与应用

海域浅层气地质调查和盐湖环境科学钻探在松散砂岩钻探取样工程中,存在孔壁易坍塌、成孔率低等技术难题,为此开展了松散砂岩地层环保型弱凝胶护壁护心钻井液技术研究。通过弱凝胶结构研究及成胶剂、交联剂及抗盐降滤失剂等材料优选,研发了一套环保型弱凝胶护壁护心钻井液,评价了钻井液的流变性、滤失性、抑制性、护心性能、环保性能等性能,并在山西某盐湖环境科学钻探工程进行现场应用。室内及现场应用结果表明：研制的弱凝胶护壁护心钻井液具有良好的流变性、降滤失性、抑制性（岩屑回收率96.3%）、护壁护心性能及环保性能（BOD5/COD比值24.6%,EC50值47950 mg/L）,能有效解决松散砂岩类地层孔壁坍塌、岩屑不易携带等问题,在松散地层钻探工程中具有广阔的应用前景。     

核电站前期建设工程项目中的综合防雷技术与应用

根据湖北省防雷中心对我国已建核电站前期工程中雷击灾害所作的调查及分析,指出了核电厂选址的基本要求与防雷之间的关系;概述了核电厂厂址专用气象观测站建设项目及设备布设情况;重点从直击雷防护、地网建设、弱电设备雷电防护这三方面对核电站前期建设工程项目中防雷系统建设进行了探讨;最后,联系实际,简要分析了核电厂前期工程综合防雷的效果。     

南海东北部C型内孤立波的观测与分析

基于布放在南海东北部陆坡海域的5套潜标观测到的内孤立波波列数据和孤立波扰动KdV(PKdV)理论,研究内孤立波在趋浅陆架上的传播特征。得出如下结果:1)观测到的内孤立波属于C型内孤立波,即平均重现周期为(23.41±0.31)h。2)内孤立波在西传爬坡过程中,其振幅表现为先增大后减小再增大,与该海域温跃层深度的变化趋势一致;由观测数据和理论计算得到的孤立波振幅增长率(SAGR)数值接近,表明该海域的内孤立波的振幅变化可以采用由孤立波PKdV方程导出的趋浅温跃层理论来描述。3)随着水深变浅,内孤立波传播方向向北偏移,传播速度减小,即在A,B和D站位,传播方向分别为279°,296°和301°,偏转角度达22°;传播速度分别为2.36,2.23和1.47 m/s,减小38%。     

2114号台风“灿都”东海北部海域曲折路径成因分析

利用国家气象中心实时台风路径资料和NCEP/FNL逐6 h再分析资料,从大尺度环境背景场、受力作用、引导气流等角度出发,对2021年第14号台风“灿都”的移动路径变化进行诊断分析。结果表明：(1)“灿都”前期移动路径主要受副热带高压(简称副高)影响,副高减弱东退后“灿都”转向东北偏北移动进入我国东海海域,“灿都”向北越过30°N之后逐步进入一个相对较弱的环境场中,路径变得复杂。(2)“灿都”后期路径发生了3次突变,13日08时之后随着强度减弱,“灿都”突然西折;13日20时在弱环境场中“灿都”为顺时针旋转路径,可以用3力近似平衡定性解释“灿都”突然西折和顺时针旋转现象;15日14时后“灿都”逆时针旋转。(3)对流层整层的引导气流可以解释“灿都”路径3次突变的成因,它的转向在“灿都”完成路径突变过程中发挥了重要的作用。     

基于新型探测资料对西安一次弱降水预报失误的原因分析

利用微波辐射计、激光测风雷达、多普勒雷达、相控阵雷达等新型探测资料、地面加密观测资料、ERA5再分析及多模式数值预报结果对2022年4月24日西安城区一次弱降水预报出现明显失误的原因进行分析,结果表明：(1)全球数值预报模式和中尺度数值预报模式对本次过程西安城区均报有明显降水,主要原因为模式对低层相对湿度预报明显偏大;(2)多种新型探测数据分析认为近地层湿度条件较差及中层的绝对水汽含量低,中层的干暖空气不利于成云致雨,垂直上升运动不强,且由于低层非常干燥,使水滴在下沉过程中蒸发,从而无法形成雨滴下落,这些原因共同造成西安城区无降水,低层相对湿度预报偏大是造成这次西安城区降水预报失误的主要原因之一;(3)造成西安城区近地层湿度条件差的原因是城市干热岛效应和低层干暖平流输送,且降水云团翻越秦岭后其湿空气绝热下沉至城区后出现增温降湿,使得城区形成较为深厚的干层,即使有雨滴在下落过程中也会造成更强的蒸发,这也是城区没有降水的重要原因之一;(4)预报员主观预报订正出现空报主要是源于对边界层水汽、抬升条件等关键降水要素缺乏订正能力,且对大城市的干热岛效应和秦岭山区地形影响研究不足。     

基于直线感知增强的边缘提取算法

根据视觉识别边缘的感知特性,提出了一种基于直线感知增强的弱边缘的边缘提取算法。借鉴视觉的直线感知方式,针对弱边缘整体上呈直线分布的特点将其筛选出来,进行编组、连接和扩展。与Edison边缘提取算子的比较实验证明,本算法在对弱边缘的提取和虚假边缘的抑制上有显著增强。     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.