对沉积盆地被剥蚀地层超补偿沉积的新认识——以东濮凹陷为例

在沉积地层剥蚀厚度恢复方法中,被剥蚀地层后期所经历的“超补偿”或“欠补偿”问题,一直是制约这些方法选取和恢复结果正确与否的根本因素。以东濮凹陷为例,分析了利用泥岩压实法恢复东营组剥蚀厚度的合理性,并由此引发了对被剥蚀地层补偿沉积的新认识。研究认为,在利用泥岩压实法等与孔隙度变化趋势有关的剥蚀厚度恢复方法时,被剥蚀地层的补偿沉积应该理解为“重量”的补偿;在利用与温度指标相关的恢复方法时,被剥蚀地层的补偿沉积应该理解为“温度”的补偿,而非简单地理解为“厚度”的补偿。     

沉积盆地沉积－剥蚀过程定量研究的一种新方法──盆地波动分析应用之一

本文在概述了确定剥蚀厚度方法基础上，提出了沉积盆地沉积－剥蚀过程定量研究的一种新方法──盆地波动过程分析法。该方法从钻井地层厚度资料入手，通过沉降史的研究，恢复地层的原始沉积厚度，结合各组段地层年代框架的标定，计算出各组段的沉积速率，引入数理方法建立盆地演化的波动方程。并进行剖面和平面上的分析对比，实现对沉积－剥蚀过程的定量预测。     

沉积盆地沉积－剥蚀过程定量研究的一种新方法──盆地波动分析应用之一

本文在概述了确定剥蚀厚度方法基础上，提出了沉积盆地沉积－剥蚀过程定量研究的一种新方法──盆地波动过程分析法。该方法从钻井地层厚度资料入手，通过沉降史的研究，恢复地层的原始沉积厚度，结合各组段地层年代框架的标定，计算出各组段的沉积速率，引入数理方法建立盆地演化的波动方程。并进行剖面和平面上的分析对比，实现对沉积－剥蚀过程的定量预测。     

渤海湾盆地济阳坳陷沙四段和孔店组地层剥蚀厚度恢复

采用改进的声波时差法原理,将优化孔隙度法的算法引入到声波时差法,模拟计算地层剥蚀厚度。对于被剥蚀地层沉积规律被改造的区域,则结合地震剖面的地层对比法来恢复地层剥蚀厚度,通过多方位运用地层对比法,然后再将所得计算结果加权平均得到最终的地层剥蚀厚度,使计算误差降低。计算结果表明,沙四段和孔店组地层剥蚀现象普遍,但总的剥蚀鞋较小,且剥蚀厚度相对较大的区域集中在凹陷的边缘沙四段地层剥蚀量大多集中在50～300m,平均剥蚀量为150m左右,最大剥蚀区在鲁东隆起区西侧的凹陷边缘,最大剥蚀量近1000m,但范围较小;孔店组的剥蚀量相对于沙四段要大,一般在150～400m,平均剥蚀量存250m左右,最大剥蚀区在惠民凹陷东北缘,最大剥蚀量在800m左右。     

常用剥蚀厚度恢复法在东营凹陷南坡西部“红层”的应用

东营凹陷南坡西部是指东营凹陷南坡石村断层以西的部分,孔店期—沙四早期沉积主要是以红色陆相碎屑岩为主的地层,即为通常所说的"红层"。受后期构造活动的影响,沙四下—孔一地层遭受不同程度的剥蚀,根据剥蚀厚度恢复方法的应用结果及适用性分析认为:厚度趋势法主要用于对剥蚀厚度区间和剥蚀范围进行标定,选用泥岩声波时差法和沉积速率比值法对剥蚀厚度数值的区间进行厘定。在凸起区及纯化构造带"红层"顶部削截明显区可直接使用厚度趋势法;靠近林南断层上升盘和高青平南断层上升盘的区域,选用声波时差法恢复"红层"顶部剥蚀厚度的效果好于其他构造带;平方王潜山构造带优选沉积速率法恢复"红层"尤其是沙四下亚段剥蚀厚度;优化的孔隙度法适合对孔隙度较大凸起区的沙四下亚段剥蚀厚度进行较为准确的标定;包裹体测温法和镜质体反射率法的恢复结果为少数单井数据点,仅作为参考。     

根据泥岩压实探讨我国东部三个晚白垩—第三纪含油盆地类型

本文根据大量的泥岩孔隙度实测数据和声波测井资料,探讨了我国东部三水、泌阳和黄骅等晚白垩—第三纪含油盆地的类型。根据泥岩压实曲线计算的剥蚀厚度,可将三个盆地划分为具有严重剥蚀的三水压实型与仅有轻微剥蚀的黄骅压实型两类。前者必须根据剥蚀厚度恢复地层在地史时期的最大埋深,否则将会对油气的勘探带来不良影响。     

基于成因解释的剥蚀厚度求取新模型及应用

国内外用于估算地层剥蚀厚度的方法很多,但大都有较为严格的应用限制条件.介绍了4种解释模型,充分考虑了沉积地质学原理,利用相邻两层(剥蚀层、下伏未剥蚀层)沉积速率相近这个前提条件,通过在纵剖面上对剥蚀层残余厚度及下伏未剥蚀层厚度值进行坐标投点(或网格化取值投点),分析它们的图形分布规律,不仅可以量化求取剥蚀厚度值,还可以对剥蚀层的原始沉积环境及造成剥蚀的后期改造作用,做出重要的地质成因解释.这种解释模型不需要直接给出时间值,避免了在利用其它某些方法求取时所面临的时间难题,并且由于在求解过程中利用的是数据的统计规律,故其求解结果更为科学合理.     

沉积盆地波动过程分析方法在中国的应用

沉积盆地波动过程分析方法是一种较新的盆地分析方法。该方法以物理学中有关波的理论为基础，以波动的观点来分析地壳运动，从地层资料入手，通过恢复地层原始厚度，计算沉积速率，建立反映盆地沉积—剥蚀过程的波动方程。据此进行波动过程分析，借助方程的参数，得到波动周期；通过波动方程对时间积分，可以定量分析剥蚀量；对波动方程进行分析，得到波动过程。在综合分析的基础上，预测有利的油气聚集区和成藏旋回。此方法在国内的一些盆地已得到应用，并取得了较好的效果。     

柴达木盆地西部埋藏史分析与油气关系类型

地层埋藏史研究是借助计算机恢复地层古厚度,动态的再现盆地的沉积发育过程,同时也是含油气系统研究的核心内容,是研究油气的生、运、聚及成藏过程的基础和关键。本文在区域概况分析的基础上,应用EASY％R。方法定量计算了柴达木盆地西部31口重点探井的剥蚀量,并应用实例详细论证了该方法恢复的剥蚀量是合理可靠的,然后应用回剥技术恢复原始地层厚度,做出了单井埋藏史图。并划分出了连续沉积型、中期剥蚀型和晚期强烈剥蚀型3种埋藏史类型。埋藏史与油气的关系密切：未遭受强烈抬升、剥蚀影响的地区能够较好的保存原生油气藏;而后期的强烈剥蚀使得早期的原生油气藏遭到破坏,同时又导致了浅层次生油气藏的形成。这对正确认识该地区沉积沉降演化历史和成藏机制,指导油气勘探具有重要作用。     

含油气盆地剥蚀厚度恢复研究进展

地层剥蚀厚度恢复是含油气原始盆地恢复的重要内容,对定量研究盆地演化史意义重大.对利用地质判别推断、沉积速率与旋回、压实密度和热演化等四个方面的剥蚀厚度恢复方法进行了总结归纳,选取有代表性的方法进行探讨.通过比较,最后提出应根据研究区具体的地质特征选择适宜的方法,注意多种方法的综合运用来进行剥蚀厚度恢复.     

Impact of fractional vegetation cover change on soil erosion in Miyun reservoir basin,China

It is well known that soil erosion at the watershed scale is the result of interactions between various factors. Among these environmental factors, vegetation is the most important and plays a major role in the soil erosion process. The impact of fractional vegetation cover change (FVCC) on soil erosion in non-contributing areas is a heavily discussed topic. In this paper, the fractional vegetation cover (FVC) in 2002 and 2005 was calculated by using a backpropagation neural network based on remote sensing (RS) data. Then the impacts of FVCC on sediment loads at the outlets of two Miyun reservoir sub-basins were evaluated by integrating RS and geographic information system with statistical analysis. The Miyun reservoir basin (MRB) is characterized by hilly and mountainous topographies and seasonal rainy weather. The primary goal of this paper is to gain a better understanding of FVCC, its driving forces, and its impact on regional soil erosion. We discuss spatiotemporal variations in precipitation and soil erosion, identify which factors contribute to those variations, analyze the influences of FVCC on climate change and human activities and, finally, conclude that changes in FVC and climate regimes are primary factors for soil erosion in MRB. We also discuss how sediment loads may be used to quantitatively separate biophysical and anthropogenic influences and to identify critical thresholds that might have dramatic consequences for the watershed ecosystem. These findings should be quite helpful for sensible watershed development and management planning.     

Quantitative analysis of piping erosion micro-mechanisms with coupled CFD and DEM method

Piping, as one of the critical patterns of internal erosion, has been reported as a major cause for failures of embankment dams and levees. The fundamental mechanism of piping was traditionally investigated through experimental trials and simplified theoretical methods in macroscale. Nevertheless, the initiation and progressive evolution of piping is a microscale phenomenon in its essence. The current understanding of the micro-mechanism of piping erosion is limited due to a lack of quantitative analysis and visualized evidence. And in fact, seepage flows can affect the soil fabrics and the development of contact forces between particles. But how these fabrics and contact forces evolve under a critical hydraulic gradient is still not fully understood. In this paper, the detailed process of piping erosion is investigated by using a coupled computational fluid dynamics and discrete element method (CFD–DEM) approach. The treatment of soil–flow interactions in CFD–DEM is explained by exchanging the momentum between the two phases. During the simulation, the piping erosion process is initiated by incrementally ascending differential water head across the soil samples. The three main stages of piping erosion (initial movement, continuation of erosion and total heave) can be identified from monitoring the particle velocity and positions. In addition, the evolution of contact force, hydraulic force, coordination number and void fraction is inspected to provide insight into the micro-mechanism of piping erosion. Two cases are simulated, one with a uniform particle size and a relatively uniform porosity distribution and the other with specific particle size and porosity distributions. An interesting finding from this study is that piping does not always initiate from the free surface and the evolution of piping depends heavily on the particle size and porosity distribution.     

岩石侵蚀速率测算方法研究综述及展望

讨论了目前应用比较广泛的几种测算岩石侵蚀速率的方法,将目前岩石侵蚀速率测算方法分为2类:一类为质量测算法,包括公式计算法、流域水化学监测法、样品称重法;另一类为几何测算法,包括微侵蚀测量仪法、对比计算法和宇宙成因核素法.探讨了各方法的测算原理、优缺点及其适用领域,质量测算法中的公式计算法和流域水化学监测法比较适用于大面积的国土监测工作;样品称重法和3类几何测算法适用于尺度较小的地学研究和文物保护、工程技术等应用领域.最后,总结了岩石侵蚀速率研究者和案例地的地理分布规律,并对岩石侵蚀速率测算研究进行了展望.     

深部碎屑岩储层次生孔隙发育机理研究进展

从深部碎屑岩储层次生孔隙的溶蚀机理、溶蚀矿物的种类、影响因素3个方面总结了近年来深部储层次生孔隙的研究成果.深部储层次生孔隙的发育受众多因素影响,异常高压、断裂发育、深部热流体活动、流体交换等都会对次生孔隙的发育产生有益的影响.提出了应该重视深部与中浅层物理化学性质的差别,综合系统地研究深部碎屑岩储层次生孔隙的成因与分布,特别是模拟高温高压条件下矿物溶蚀和沉淀的机理及其影响因素,全面突破深部储层次生孔隙理论,指导勘探开发.     

Reservoir Porosity Measurement Uncertainty and its Influence on Shale Gas Resource Assessment

Reservoir porosity is a critical parameter for the process of unconventional oil and gas resources assessment. It is difficult to determine the porosity of a gas shale reservoir, and any large deviation will directly reduce the credibility of any shale gas resources evaluation. However, there is no quantitative explanation for the accuracy of porosity measurement. In this paper, measurement uncertainty, an internationally recognized index, was used to evaluate the results of porosity measurement of gas shale plugs, and its impact on the credibility of shale gas resources assessment was determined. The following conclusions are drawn:(1) the measurement uncertainty of porosity of a shale plug is 1.76%–3.12% using current measurement methods, the upper end of which is too large to be acceptable. It is suggested that the measurement uncertainty should be factored into the standard helium gas injection porosity determination experiment, and the uncertainty should be less than 2.00% when using a high-precision pressure gauge;(2) in order to reduce the risk for exploration and decision-making, attention should be paid to the large uncertainty(30% at least) of shale gas resource assessment results, sometimes with corrections being made based on the practical considerations;(3) a pressure gauge with an accuracy of 0.25% of the full scal cannot meet the requirements of porosity measurement, and a high-precision plug cutting method or high-precision bulk volume measurement method such as one using 3 D scanning, is recommended to effectively reduce porosity uncertainty;(4) the method and process for evaluating the measurement uncertainty of gas shale porosity could also be referred for assessment of experimental quality by other laboratories.     

泥沙异重流与环境物质交换经验式对比

采用异重流层平均水沙耦合数学模型,模拟开闸式和恒定入流式泥沙异重流水槽实验,对比分析异重流与环境之间物质交换经验式的适应性和不确定性。考虑4个水卷吸经验式:e_w59、e_w86、e_w87和e_w01,5个泥沙侵蚀经验式:E_s77、E_s86、E_s87、E_s93和E_s04。数值研究表明:水卷吸对于水槽异重流影响较小,应用综合考虑底床摩擦和剪切不稳定的e_w经验式时模拟结果较好;开闸式异重流对床面侵蚀能力有限。对于恒定入流式异重流,应用E_s87和E_s93侵蚀经验式计算所得淤积厚度与实测值吻合较好,可能是率定时综合考虑了异重流实验数据。     

Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine, and artificial neuronal networks

Sand production by soil erosion in small watershed is a complex physical process. There are few physical models suitable to describe the characteristics of the intense erosion in domestic loess plateau. Introducing support vector machine (SVM) oriented to small sample data and possessing good extension property can be an effective approach to predict soil erosion because SVM has been applied in hydrological prediction to some extent. But there are no effective methods to select the rational parameters for SVM, which seriously limited the practical application of SVM. This paper explored the application of intelligence-based particle swarm optimization (PSO) algorithm in automatic selection of parameters for SVM, and proposed a prediction model by linking PSO and SVM for small sample data analysis. This method utilized the high efficiency optimization property and swarm paralleling property of PSO algorithm and the relatively strong learning and extending capacity of SVM. For an example of Huangfuchuan small watershed, its intensive fragmentation and intense erosion earn itself the name of “worst erosion in the world”. Using four characteristics selection algorithms of correlation feature selection, the primary affecting factors for soil erosion in this small watershed were determined to be the channel density, ravine area, sand rock proportion, and the total vegetation coverage. Based on the proposed PSO–SVM algorithm, the soil erosion modulus in the small watershed was predicted. The accuracy of the simulation and prediction was good, and the average error was 3.85%. The SVM predicting model was based on the monitoring data of sand production. The construction of the SVM erosion modulus prediction model for the small watershed comprehensively reflected the complex mechanism of soil erosion and sand production. It had certain advantage and relatively high practical value in small sample prediction in the discipline of soil erosion.     

Coupled wellbore erosion and stability analysis

This paper extends earlier work on sand erosion and presents an attempt to couple sand erosion to mechanical damage of rock around a wellbore. Porosity which evolves in time and space as surface erosion progresses, is chosen as the coupling parameter. Both rock elasticity and strength (cohesion) are assumed to depend on porosity in such a way that the material becomes weaker with increasing porosity. The mathematical model, consists of erosion equations, mixture flow equations and stress equilibrium equations, is solved numerically by Galerkin finite element method. Numerical results suggest that erosion, resulting in sand production, is high close to the free surface. Erosion is accompained by changes in porosity and a significant permeability increase. Erosion in the vicinity of the wellbore induces alterations in the mechanical behaviour of the medium. Weakening of rock stiffness leads to severe alteration of both effective stresses and pore pressure near the cavity. Since cohesion decreases with increasing porosity, one can also identify the time instant at which rock mechanical failure starts. © 1998 John Wiley & Sons, Ltd.     

应用逐步回归建立岩石孔隙度图版

油层岩石具有在其空隙中储存流体(油、气、水)的性质,称为油层的孔隙性。油层的孔隙性是油层最重要的物理性质之一,油层孔隙性的好坏标志着油层本身储油能力的大小。为了衡量油层孔隙性好坏,把岩石的孔隙度作为油层孔隙性的定量指标。岩石的孔隙度是指岩石中孔隙的容积与该岩石体积的比值(以百分数表示)。