川中雷口坡组四~3亚段地震相分析——应用波形分类技术研究沉积相的实例

在工区面积大、钻井少的情况下开展地震相研究,可以有效绘出沉积相展布图。对四川盆地中部雷口坡组四3亚段利用GS1和YS104两口井进行地震地质标定,建立了膏盐湖、白云岩坪及颗粒滩等几种不同沉积微相的地震响应模式。膏盐湖相表现为强振幅、高连续的地震响应,而颗粒滩及白云岩坪主要表现为中振幅、低连续的地震响应特征。在地质标定基础上,对全区利用波形分类技术开展地震相研究,恢复了古沉积环境,获得了颗粒滩相及白云岩坪的展布图,这些分布区是有利储层的发育区。龙岗—营山—南充—蓬溪一带处于颗粒滩、白云岩坪有利相带,且处于雷四3亚段地层剥蚀线附近,受岩溶改造强烈,储层较为发育,是川中地区的重要勘探对象。     

珠江口盆地流花油田新近系生物礁灰岩储层特征及成因分析

南海珠江口盆地东沙隆起流花油田新近系灰岩储层为典型的台地边缘生物礁储层。岩石类型包括皮壳状珊瑚藻黏结灰岩、缠绕状珊瑚藻-珊瑚骨架灰岩、泥晶有孔虫-珊瑚藻颗粒灰岩、亮晶有孔虫-珊瑚藻颗粒灰岩、含红藻石灰岩、泥晶珊瑚颗粒灰岩、生屑泥晶灰岩7类。孔缝类型丰富,包括泥晶基质溶孔、粒间溶孔、藻架溶孔、粒内溶孔、铸模孔、体腔孔、晶间微孔7类孔隙及溶蚀缝、珊瑚藻皮壳间隙缝、似缝合线溶蚀缝、构造缝4类裂缝。划分了孔洞-网状裂缝型、孔隙型、裂缝-孔洞型、致密裂缝型4种储集类型。皮壳状珊瑚藻灰岩属于孔洞-网状裂缝型储层,泥晶颗粒灰岩、含红藻石灰岩和红藻石灰岩属于孔隙型,珊瑚骨架灰岩属于裂缝-孔洞型,亮晶生屑灰岩和胶结作用较强的皮壳状藻灰岩属于致密裂缝型。沉积微相和成岩作用控制了不同储集类型的分布,生物礁形成过程中的多期暴露对沉积微相和储集空间有重要影响。沉积-成岩演化过程划分为早期成礁与早期暴露、晚期成礁与次级暴露、埋藏压实与泥岩再造水改造阶段、两期成藏与地下水溶蚀4个阶段。根据沉积-成岩演化分析,预测了研究区储集类型空间分布。     

矿物中4He同位素提取和含量测定研究

(U-Th)/He定年是一种有效的低温热年代学定年技术,现已被广泛应用于地质研究的各个领域,而矿物中4He同位素的有效提取和含量准确测定是该技术的关键。磷灰石和锆石是(U-Th)/He定年最常用的矿物,其4He提取条件及铀钍含量测定方法都较为成熟;而其他矿物(如磁铁矿、橄榄石、针铁矿、石榴子石等)的研究则相对较少。文章介绍了当前国内外(U-Th)/He研究中采用的4He同位素提取方法———真空炉加热法和激光加热法,激光加热法因具有低4He背景值和耗时短的优点而成为主要的提取方法。以磷灰石样品测试为例,介绍了成都地质矿产研究所建立的采用激光加热法和四极杆质谱提取4He同位素及其含量测量过程、含量计算和校正方法。指出未来(U-Th)/He测试技术除继续改进现有分析方法外,应加强对更多不同矿物的测试研究。     

Environmental geochemistry of an active Hg mine in Xunyang,Shaanxi Province,China

The Xunyang Hg mine (XMM) situated in Shaanxi Province is an active Hg mine in China. Gaseous elemental Hg (GEM) concentrations in ambient air were determined to evaluate its distribution pattern as a consequence of the active mining and retorting in the region. Total Hg (HgT) and methylmercury (MeHg) concentrations in riparian soil, sediment and rice grain samples (polished) as well as Hg speciation in surface water samples were measured to show local dispersion of Hg contamination. As expected, elevated concentrations of GEM were found, ranging from 7.4 to 410 ng m⁻³. High concentrations of HgT and MeHg were also obtained in riparian soils, ranged from 5.4 to 120 mg kg⁻¹ and 1.2 to 11 μg kg⁻¹, respectively. Concentrations of HgT and MeHg in sediment samples varied widely from 0.048 to 1600 mg kg⁻¹ and 1.0 to 39 μg kg⁻¹, respectively. Surface water samples showed elevated HgT concentrations, ranging from 6.2 to 23,500 ng L⁻¹, but low MeHg concentrations, ranging from 0.022 to 3.7 ng L⁻¹. Rice samples exhibited high concentrations of 50–200 μg kg⁻¹ in HgT and of 8.2–80 μg kg⁻¹ in MeHg. The spatial distribution patterns of Hg speciation in the local environmental compartments suggest that the XMM is the source of Hg contaminations in the study area.     

Effect of source water quality on arsenic (V) removal from drinking water by coagulation/microfiltration

The effect of the source water quality on As(V) removal by coagulation/microfiltration (C/MF) was investigated systematically using a jar test and a lab-scale test; the results of the lab-scale test coincide with those of the jar test. It showed that the increase of dissolved organic carbon (DOC), HPO₄ ²⁻ and HCO₃ ⁻ concentrations would moderately decrease As(V) removal, and the residual As(V) concentration in treated water is no more than 10 μg L⁻¹ when the concentration of DOC, HPO₄ ²⁻ and HCO₃ ⁻ in raw water is not beyond 9.22 mg L⁻¹, 1.6 mg L⁻¹ and 300 mg L⁻¹, respectively. Other co-ions, such as Cl⁻, NO₃ ⁻, F⁻, SO₄ ²⁻, and counter-ions including K⁺, Ca²⁺ and Mg²⁺, have little effect on As(V) removal. Both results of the jar test and the lab-scale test demonstrate that Fe³⁺ dose of 6 mg L⁻¹ is enough to eliminate the influence of HCO₃ ⁻ whose concentration changes from 350 mg L⁻¹ to 450 mg L⁻¹. In the lab-scale test, As(V) removal ranged from 92.8 to 98.2% at Fe³⁺ dose of 4 mg L⁻¹ and 5 mg L⁻¹ when As(V) concentration in raw water was about 100 μg L⁻¹. The results obtained in this study suggest that As(V) removal by C/MF process is sensitive to the quality of raw water, and a jar test needs to be done before C/MF process is used for arsenic removal from drinking water.     

The Tilt between Acretion Disk and Stellar Disk

The orientations of the accretion disk of active galactic nuclei (AGN) and the stellar disk of its host galaxy are both determined by the angular momentum of their forming gas, but on very different physical environments and spatial scales. Here we show the evidence that the orientation of the stellar disk is correlated with the accretion disk by comparing the inclinations of the stellar disks of a large sample of Type 2 AGNs selected from Sloan Digital Sky Survey (SDSS, York et al. 2000) to a control galaxy sample. Given that the Type 2 AGN fraction is in the range of 70–90 percent for low luminosity AGNs as a priori, we find that the mean tilt between the accretion disk and stellar disk is ~ 30 degrees (Shen et al. 2010).     

阵风及其谱模拟

作用于海洋工程结构物的风力一般可分为恒定分量和变动分量两部分，认为前者是由于稳定风速所致，后者是由于风速脉动引起的。在实验室中为了恰当地模拟海上环境，不但要实现稳定风速的模拟，而且变动风速也要满足一定要求，这即是阵风谱的模拟。本文在风速的脉动是随机的，并服从高斯分布，且是由无限多正弦风叠加而成的基础上，叙述了海洋工程模型试验中如何实现阵风谱的模拟，并给出了模拟实例。     

6000m深海拖曳系统动力响应计算

本文数值计算了６０００ｍ深海单拖体和双拖体拖曳系统的动态响应，特别是研究了拖船的垂荡运动对拖体定高性的影响。对于双拖体系统，本文对第二拖体在水中的重量、联接两拖体的缆索长度及其在水中重量等因素对第二拖体定高性的影响进行了详细的计算、分析。计算表明，采用双拖体系统可大大提高系统的定高性。     

Neural networks and their derivatives for history matching and reservoir optimization problems

In geosciences, complex forward problems met in geophysics, petroleum system analysis, and reservoir engineering problems often require replacing these forward problems by proxies, and these proxies are used for optimizations problems. For instance, history matching of observed field data requires a so large number of reservoir simulation runs (especially when using geostatistical geological models) that it is often impossible to use the full reservoir simulator. Therefore, several techniques have been proposed to mimic the reservoir simulations using proxies. Due to the use of experimental approach, most authors propose to use second-order polynomials. In this paper, we demonstrate that (1) neural networks can also be second-order polynomials. Therefore, the use of a neural network as a proxy is much more flexible and adaptable to the nonlinearity of the problem to be solved; (2) first-order and second-order derivatives of the neural network can be obtained providing gradients and Hessian for optimizers. For inverse problems met in seismic inversion, well by well production data, optimal well locations, source rock generation, etc., most of the time, gradient methods are used for finding an optimal solution. The paper will describe how to calculate these gradients from a neural network built as a proxy. When needed, the Hessian can also be obtained from the neural network approach. On a real case study, the ability of neural networks to reproduce complex phenomena (water cuts, production rates, etc.) is shown. Comparisons with second polynomials (and kriging methods) will be done demonstrating the superiority of the neural network approach as soon as nonlinearity behaviors are present in the responses of the simulator. The gradients and the Hessian of the neural network will be compared to those of the real response function.     

Gold enrichment characteristics and exploration prospects in Zambia: Based on 1∶1000000 geochemical mapping

The 1∶1000000 geochemical mapping of Zambia provides catchment sediment geochemical data for 58elements including Au from 746 sediment samples at 736 sampling sites,corresponding to a sampling density of about one site per 1000 km2.Under strict quality control using field duplicates,certified reference materials,and analytical replicate samples,the Au was determined by Inductively Coupled Plasma Mass Spectrometry(ICP-MS).The detection limit of Au was 0.20×10^-9.The 95%range(2.5%–97.5%)of Au concentrations was from 0.24×10^-9 to 1.36×10^-9,and the median value was 0.40×10^-9.The most noticeable Au distribution patterns shown on the map are mainly located between Lusaka and Ndola(Lufilian Arc Belt).In addition,several high Au value areas occurred in Mansa,Muyombe,Chipata,and Livingstone.The spatial distribution patterns of Au in tectonic units,drainage basins,and geomorphological landscapes could be related to the Lufilian Arc Belt and Bangweulu Block.The Au concentrations show metallogenic belts between Muyombe and Mbala areas,between Mansa and Ndola areas,and between Lusaka and Kasempa areas.