华南北部湾盆地的形成机制

从北部湾盆地的区域地质构造背景、几何形态、边界断裂及内部断裂特征解析,综合沉积中心迁移规律及盆内构造研究,提出北部湾盆地为右行右阶走滑拉分成因。盆地的形成和发展受控于合浦-北流、信宜-廉江、吴川-四会和阳江-河源4条深大断裂带,这4条主干断裂带构成右阶断裂格架。古近系出现的花状构造等表现出典型的张扭特征,新近系受到压扭作用改造发生挤压反转,该盆地构造演化过程与华南大陆中、新生代拉分盆地具有同期性和相似性。中生代基底中先存的深大走滑断裂带是新生代北部湾盆地形成的先决地质条件。而印度-澳大利亚板块对欧亚板块碰撞挤压作用的逐渐增强和太平洋板块俯冲方向的改变及俯冲作用的衰减是控制北部湾盆地形成演化的区域大地构造因素。     

走滑断裂古地震探槽选址范例

探槽选址直接决定了古地震研究的质量,且理想探槽位置具有稀缺性。在收集82篇国内外走滑断裂古地震研究论文的基础上,分析了长序列古地震探槽选址和同震位移探槽选址的构造地貌共性特征。长序列古地震探槽一般布设在断塞塘、拉分盆地、小型湖盆、闸门脊汇水一侧及比较平缓的冲洪积扇或大型冲洪积扇末端等地貌位置。这些地貌位置的共同特征是位于连续的、较高沉积速率的沉积环境,且通常富集14C测年物质。在探槽选址的过程中,不仅要对现今沉积环境,也要对其古地理环境进行评价。然而以发掘古地震同震位移为目的的探槽选址则不同,成功的研究点往往较频繁发生侵蚀和下切事件,其中冲沟是最常见的地貌标志。探槽布设以垂直和平行探槽为主,采取逐次掘进或相间平行排列的方式开挖。由于理想探槽位置的稀缺性,所以在这些优质的探槽点一定要开展长期详细地深入研究,最大限度获得古地震事件的长序列,同时也要注意新技术和新方法的应用。     

北京百年地质调查的传承与发展——《北京市区域地质志》修编

北京是中国地质调查的"摇篮",1913年成立地质调查所。1916年开始北京西山地质调查,1920年出版第一部地质调查专著《北京西山地质志》,民国时期"燕山运动"的提出和"北京猿人"的发现为影响世界的地质调查成果。解放后,北京率先实现1∶5万区域地质调查的全覆盖,1991年出版《北京市区域地质志》。2013—2015年修编第二版《北京市区域地质志》,为了突出北京的城市地质特色,增加"城市地质"篇章和"北京市基岩地质图",为京津冀协同发展服务。今后,北京的地质调查将以天安门为中心部署展开,建立8个监测预警预报系统,实现从地质找矿为中心向地质环境调查为中心转变,从资源调查向多参数调查转变,从平面地质调查向三维地质调查转变。     

北京密云放马峪基性岩墙群LA-MC-ICP-MS锆石U-Pb年龄及其意义

太古宙基性岩墙群在北京密云地区广泛分布,其年代学研究一直备受关注,多年来取得了一些数据,但对太古宙变质片麻岩控制作用未做论述。利用LA-MC-ICP-MS U-Pb同位素测定技术,测得放马峪石榴辉石岩中锆石的年龄为2497±15Ma,此年龄被解释为区内变质片麻岩成岩年龄应早于基性岩墙群成岩年龄,其为研究区内太古宙片麻岩成岩年龄提供了重要的参考数据。     

准噶尔盆地火山岩分类研究与应用

岩石分类一直是人们长期探索的课题。由于油气勘探的需要,针对储层研究的盆地火山岩分类已经成为火成岩研究的重要新方向。在综合分析国内外火山岩分类研究的基础上,根据3条野外剖面和21口钻井岩心岩屑的观察描述、岩石薄片鉴定和化学成分分析等资料,依照结构-成分-成因分类原则,建立了适于储层评价的准噶尔盆地石炭系火山岩及其他岩类的岩性分类体系和识别标准。将本区火山岩划分为火山熔岩、火山碎屑熔岩、火山碎屑岩、沉火山碎屑岩等4大类;同时将盆地内与火山岩共生的其他岩类也进行分类和识别,共划分出8大类45种基本类型。与我国东部的中新生代盆地火山岩不同,本区古生代火山岩普遍遭受较长期风化改造和蚀变作用,常见有绿泥石化、碳酸盐化和黏土矿化。这些蚀变会影响到TAS图解岩性判别的结果,因此笔者探讨了蚀变条件下的岩石识别和定名方法。本文分类体系针对火山岩储层研究,结果显示,基于该分类体系岩性与储集空间的类型及其变化规律呈现明显相关性,不同岩性的储集空间类型、组合方式和发育程度存在差异,由此可指导火山岩油气勘探。     

Beyond dual-porosity modeling for the simulation of complex flow mechanisms in shale reservoirs

The state of the art of modeling fluid flow in shale reservoirs is dominated by dual-porosity models which divide the reservoirs into matrix blocks that significantly contribute to fluid storage and fracture networks which principally control flow capacity. However, recent extensive microscopic studies reveal that there exist massive micro- and nano-pore systems in shale matrices. Because of this, the actual flow mechanisms in shale reservoirs are considerably more complex than can be simulated by the conventional dual-porosity models and Darcy’s law. Therefore, a model capturing multiple pore scales and flow can provide a better understanding of the complex flow mechanisms occurring in these reservoirs. This paper presents a micro-scale multiple-porosity model for fluid flow in shale reservoirs by capturing the dynamics occurring in three porosity systems: inorganic matter, organic matter (mainly kerogen), and natural fractures. Inorganic and organic portions of shale matrix are treated as sub-blocks with different attributes, such as wettability and pore structures. In kerogen, gas desorption and diffusion are the dominant physics. Since the flow regimes are sensitive to pore size, the effects of nano-pores and micro-pores in kerogen are incorporated into the simulator. The multiple-porosity model is built upon a unique tool for simulating general multiple-porosity systems in which several porosity systems may be tied to each other through arbitrary connectivities. This new model allows us to better understand complex flow mechanisms and eventually is extended into the reservoir scale through upscaling techniques. Sensitivity studies on the contributions of the different flow mechanisms and kerogen properties give some insight as to their importance. Results also include a comparison of the conventional dual-porosity treatment and show that significant differences in fluid distributions and dynamics are obtained with the improved multiple-porosity simulation.     

Two Methods for Predicting Reliability Index and Critical Probabilistic Slip Surface of Soil Slopes

Two methods of reliability analysis of soil slopes are studied, and the representative flow charts of both methods are illustrated. Method 1 can predict the reliability index and the critical probabilistic slip surface directly and it is computational efficient, but it needs the development of new codes for integrating the reliability analysis code and the slope stability code. Method 2 makes the reliability analysis code call the slope stability analysis code directly, and each code can be considered as an intact part. The main result of Method 2 is the reliability index of soil slope. Combined with the proposed method for locating the critical slip surface, Method 2 can also predict the probabilistic slip surface. Although Method 2 needs much more callings of the subprogram of slope stability analysis code, it needs not the developing of new computer program. Thus, Method 2 is easy to use and can be applied to different reliability analysis methods and slope stability analysis methods.