基于模拟退火粒子群优化算法的裂缝型储层各向异性参数地震反演

发育垂直定向排列裂缝的地下岩石可等效为具有水平对称轴的横向各向同性（horizontal transverse isotropic,HTI）介质。针对HTI介质模型,本文研究了裂缝型储层的各向异性参数地震振幅随方位角变化（amplitude variations with azimuth,AVAZ）的反演方法。首先,在地震AVAZ反演流程中,提出采用模拟退火粒子群优化算法实现裂缝型储层各向异性参数反演。之后,通过理论模型测试,验证了基于模拟退火粒子群优化算法的地震AVAZ反演的有效性。最后,将反演方法应用于四川盆地龙马溪组页岩气储层实际方位地震数据;在反演之前先利用傅里叶级数方法估计裂缝方位并对实际数据进行方位校正,以提供更准确的输入数据;通过计算得到的P波、S波各向异性参数可用于评价裂缝发育程度。反演结果表明,研究区域构造顶部裂缝较发育,与地质基本理论一致,验证了反演方法的合理性。     

基于岩石物理模型的页岩孔隙结构反演及横波速度预测

准确预测储层的等效孔隙纵横比对页岩储层岩石物理建模及横波速度预测具有重要意义。为分析页岩储层孔隙纵横比及预测横波速度,提出了基于岩石物理模型的页岩孔隙纵横比反演及横波速度预测方法。本文首先通过岩石物理模型建立岩石的纵、横波速度与孔隙纵横比、孔隙度和矿物组分等参数之间的定量关系,寻找最佳孔隙纵横比;然后通过使理论预测与实际测量的纵波速度之间误差达到最小的方式反演孔隙纵横比,并以此为约束预测横波速度。实际测井数据反演结果表明,龙马溪组页岩地层的孔隙纵横比稳定,而围岩的孔隙纵横比变化范围较大;说明与围岩相比,页岩的孔隙结构更为稳定。同时,预测得到的页岩横波速度与实测横波速度的误差较小,另外对于缺少矿物组分资料的页岩层段,用平均矿物组分预测得到的横波速度误差仍较小;说明与矿物组分相比,龙马溪组页岩的纵、横波速度对孔隙纵横比参数更敏感。综上所述,利用该方法可预测到较为准确的等效孔隙纵横比和横波速度。     

焦石坝地区页岩各向异性及其影响的分析研究

黑色富含有机质页岩由于沉积方式的特殊性,一般具有较强的各向异性。但现有大部分研究是基于各向同性理论开展页岩储层预测工作,这与页岩的实际地质特点不符。文中联合利用实验室岩石力学测试数据以及各向异性参数经验公式计算焦石坝地区页岩的各向异性参数,并进一步探讨了各向异性对页岩岩石速度、储层AVO响应特征以及岩石力学参数计算的影响。结果表明,各向异性对页岩速度、脆性指数预测结果影响较大,在地层倾角较大时进行速度分析及页岩可压性预测时应考虑各向异性参数的影响;同时,AVO正演结果表明地震反射入射角小于40°时,各向异性算法与各向同性算法计算的页岩AVO响应差异较小,利用各向同性叠前反演方法进行页岩储层预测具有可行性。     

页岩储层裂缝型孔隙定量预测的新方法

裂缝作为有效的储集空间和渗流通道,对于页岩储层具有重要的作用。裂缝研究的难点在于难以像井震结合波阻抗反演一样综合测井和地震信息开展裂缝预测。提出了一种综合测井和地震信息预测裂缝的新思路:首先拓展了SPM软孔模型,把孔隙空间分为硬孔隙、基质孔隙和裂缝型孔隙,建立岩石物理模型,并求取裂缝型孔隙度曲线,通过FMI成像测井曲线证实了裂缝型孔隙度结果的可靠性;然后综合裂缝型孔隙度测井曲线和地震数据,通过多属性神经网络反演预测实现了裂缝型孔隙度的定量预测。研究成果表明,通过和原始测井曲线以及和蚂蚁体等地震几何属性的对比,证明裂缝型孔隙度反演结果比较可靠,可以用来定量解释页岩储层的裂缝发育程度。本研究实现了综合测井曲线和地震信息定量预测页岩储层裂缝发育程度的方法,对于页岩储层定量解释具有重要的意义。     

济阳坳陷罗家地区各向异性页岩储层全波场地震响应模拟及分析

济阳坳陷罗家地区页岩储层的固有各向异性及水平层理发育使得储层呈现VTI（vertical transversely isotropy）各向异性,而VTI各向异性背景下垂直裂缝的发育使得储层进一步呈现等效正交各向异性特征。本文以正交各向异性介质作为罗家页岩油储层模型,在岩石物理建模中应用Backus平均理论将测井尺度的VTI各向异性粗化至地震尺度,并利用Schoenberg理论在VTI各向异性背景中引入垂直裂缝,进而得到正交各向异性等效介质模型,同时考虑裂缝尺度和流体填充等因素。之后,应用各向异性反射率法进行全波场地震正演模拟,计算正交各向异性页岩油储层AVAZ（amplitude versus azimuth）响应,通过振幅的方位特征进行储层裂缝识别。计算结果表明,PP波、PSV波和PSH波方位振幅响应各不相同,且方位振幅分布图的拟合形状可反映裂缝的发育方向,为页岩油储层中裂缝的地震识别提供依据。     

页岩储层岩石物理模型及地震AVO响应模拟

通过模拟并分析各向异性页岩岩石物理模型,研究页岩中干酪根含量变化对页岩弹性各向异性的影响,其结果为应用AVO方法定量解释页岩储层厚度和干酪根含量提供依据。岩石物理模型预测了页岩速度和各向异性参数随干酪根含量的变化规律。应用传播矩阵方法计算薄层页岩的AVO响应,得到反射系数关于页岩层厚度、入射波频率、干酪根含量和入射角的函数。数值模拟结果表明:在干酪根含量不变且在同一入射角下的反射系数在频率域呈周期变化,该周期与薄层厚度成反比;对同一频率入射波和相同入射角,反射系数幅值随干酪根含量增加而变大。     

基于改进Xu-White模型的富有机质页岩横波预测方法研究

横波速度是储层表征描述、AVO分析和流体识别的重要信息。干酪根是富有机质页岩的重要成分且具有非固体非流体的特殊弹性性质,常规处理方法是将其等效为流体。这里提出了一种基于变化孔隙纵横比Xu-White模型的富有机质页岩横波预测方法,首先将干酪根同时等效为基质矿物和孔隙流体引入模型中,然后利用模拟退火粒子群算法,在约束条件下,反演出变化孔隙纵横比,并以变化孔隙纵横比初始化,模型构建富有机质变化孔隙纵横比Xu-White模型,最后结合测井信息与岩石物理模型预测横波速度。将该方法应用于中国西南部四川盆地东南焦石坝地区某井位,通过对比固定孔隙纵横比Xu-White模型方法与干酪根流体等效的变化孔隙纵横比Xu-White模型预测结果,验证了该方法的适用性和准确性。本方法将为富有机质页岩的研究提供更准确的横波速度资料。     

基于等效介质模型和频变AVO反演的裂缝储层参数估算方法

由于裂缝性油气藏具有突出的资源潜力和经济效益,利用地震方法对裂缝储层进行精细的定量描述逐渐成为勘探地球物理的关键任务之一。为了克服以往数据驱动类反演方法无法直接获得裂缝参数、而基于静态等效介质模型驱动的反演方法无法描述孔隙内部结构和流体信息的缺点,笔者提出一种基于动态等效介质模型的储层定量描述新方法。该方法通过频变AVO(amplitude variation with offset)理论建立目标函数并使用全局最优化算法反演裂缝参数。一维和二维模型测试证实,由于充分利用了反射系数频变响应对裂缝密度和时间尺度因子的敏感性,反演方法可以对裂缝储层实现有效描述。     

页岩气地震识别与预测技术

依据页岩气的地质特点及当前地震勘查技术的水平,指出地震在识别和追踪页岩储层空间分布（包括埋深、厚度以及构造形态）方面具有明显的优势;综合利用测井及地质资料,对页岩储层有机质丰度、含气性等参数进行解释及优选敏感的地球物理参数,进而建立储层特征与地震响应的关系,以此反演预测页岩气储层有利区;运用相干与曲率等属性分析技术、宽方位甚至全方位地震各向异性分析技术、叠前反演技术、横波分裂技术等,可解决储层裂缝和岩石力学特征问题,直接为钻井和压裂工程技术服务;与压裂技术配套发展的微地震监测技术,可实时提供压裂过程中产生的裂缝位置、方位、大小以及复杂程度,并对储层改造方案的有效性作出准确评价。     

不同方位各向异性反演技术对比和总结

随着“两宽一高”地震采集技术的发展,基于方位各向异性理论的叠前P波裂缝反演技术的应用也越来越多。方位各向异性反演可以得到裂缝方位和裂缝强度,但是不同的反演技术得到的表征裂缝强度的参数各不相同,反演结果也常常有所差异,因此就造成了裂缝各向异性反演结果的不唯一性,进而造成谁才是“正确的”这种困惑和疑问。本文从Thomsen各向异性理论出发,通过裂缝模型(Hudson薄币模型、Schoenberg线性滑动模型)之间的相互关系,在不同的裂缝反演技术(VVAZ、Ruger近似和傅立叶级数)中建立各向异性的参数联系,给出了不同方位各向异性裂缝反演结果的真实含义和数学表达,并对不同反演技术与裂缝模型参数之间的联系进行了总结,进一步深化了方位各向异性裂缝反演的研究,为基于“两宽一高”数据开展大规模的裂缝检测奠定坚实的理论和技术基础。     

基于自适应粒子群优化算法的波阻抗反演方法

针对地震勘探资料依赖线性优化方法进行波阻抗反演不易得到全局极值的问题,提出一种改进的粒子群优化算法-自适应粒子群优化算法进行波阻抗反演。自适应粒子群优化算法是以群智能优化理论为基础,通过3种可能移动方向的带权值组合进行全局寻优。该方法搜索速度较快,且具有较强的全局寻优能力。通过函数测试和波阻抗反演的应用,结果表明,自适应粒子群优化算法是一种适应能力较强的全局优化算法,用该方法进行波阻抗反演是可行有效的。      

Bayesian inversion of synthetic AVO data to assess fluid and shale content in sand-shale media

Reservoir characterization of sand-shale sequences has always challenged geoscientists due to the presence of anisotropy in the form of shale lenses or shale layers. Water saturation and volume of shale are among the fundamental reservoir properties of interest for sand-shale intervals, and relate to the amount of fluid content and accumulating potentials of such media. This paper suggests an integrated workflow using synthetic data for the characterization of shaley-sand media based on anisotropic rock physics (T-matrix approximation) and seismic reflectivity modelling. A Bayesian inversion scheme for estimating reservoir parameters from amplitude vs. offset (AVO) data was used to obtain the information about uncertainties as well as their most likely values. The results from our workflow give reliable estimates of water saturation from AVO data at small uncertainties, provided background sand porosity values and isotropic overburden properties are known. For volume of shale, the proposed workflow provides reasonable estimates even when larger uncertainties are present in AVO data.     

S-wave velocity profiling from refraction microtremor Rayleigh wave dispersion curves via PSO inversion algorithm

The refraction microtremor method has been increasingly used as an appealing tool for investigating near surface S-wave structure. However, inversion, as a main stage in processing refraction microtremor data, is challenging for most local search methods due to its high nonlinearity. With the development of data optimization approaches, fast and easier techniques can be employed for processing geophysical data. Recently, particle swarm optimization algorithm has been used in many fields of studies. Use of particle swarm optimization in geophysical inverse problems is a relatively recent development which offers many advantages in dealing with the nonlinearity inherent in such applications. In this study, the reliability and efficiency of particle swarm optimization algorithm in the inversion of refraction microtremor data were investigated. A new framework was also proposed for the inversion of refraction microtremor Rayleigh wave dispersion curves. First, particle swarm optimization code in MATLAB was developed; then, in order to evaluate the efficiency and stability of proposed algorithm, two noise-free and two noise-corrupted synthetic datasets were inverted. Finally, particle swarm optimization inversion algorithm in refraction microtremor data was applied for geotechnical assessment in a case study in the area in city of Tabriz in northwest of Iran. The S-wave structure in the study area successfully delineated. Then, for evaluation, the estimated Vs profile was compared with downhole data available around of the considered area. It could be concluded that particle swarm optimization inversion algorithm is a suitable technique for inverting microtremor waves.     

Optimization of well placement by combination of a modified particle swarm optimization algorithm and quality map method

Determining the optimum placement of new wells in an oil field is a crucial work for reservoir engineers. The optimization problem is complex due to the highly nonlinearly correlated and uncertain reservoir performances which are affected by engineering and geologic variables. In this paper, the combination of a modified particle swarm optimization algorithm and quality map method (QM + MPSO), modified particle swarm optimization algorithm (MPSO), standard particle swarm optimization algorithm (SPSO), and centered-progressive particle swarm optimization (CP-PSO) are applied for optimization of well placement. The SPSO, CP-PSO, and MPSO algorithms are first discussed, and then the modified quality map method is discussed, and finally the implementation of these four methods for well placement optimization is described. Four example cases which involve depletion drive model, water injection model, and a real field reservoir model, with the maximization of net present value (NPV) as the objective function are considered. The physical model used in the optimization analyses is a 3-dimensional implicit black-oil model. Multiple runs of all methods are performed, and the results are averaged in order to achieve meaningful comparisons. In the case of optimizing placement of a single producer well, it is shown that it is not necessary to use the quality map to initialize the position of well placement. In other cases considered, it is shown that the QM + MPSO method outperforms MPSO method, and MPSO method outperforms SPSO and CP-PSO method. Taken in total, the modification of SPSO method is effective and the applicability of QM + MPSO for this challenging problem is promising     

基于HPSO算法的岩石非定常蠕变本构模型辨识

复合微粒群优化(HPSO)是一类随机全局优化技术,具有搜索能力强、收敛速度快、搜索精度高的优点.针对岩石蠕变本构模型非定常参数的辨识问题,利用FLAC软件自带的fish语言实现了HPSO算法对非定常参数的辨识.该方法从非定常参数的随机值出发,以蠕变过程中试件变形的实验值与计算值的误差大小作为适应度函数来评价参数的品质,...     

双侧向测井反演的微粒群方法

双侧向测井反演中,采用微粒群优化算法进行3参数(原状地层电阻率、侵入带电阻率、侵入半径)反演,反演结果精确度较高。由于微粒群优化算法对初值依赖性较小,从而算法也较为稳定,增加了反演结果的可信度。     

煤矿井下无线传感器网络三维定位算法研究

针对煤矿无线传感器网络定位存在节点模型不合理、定位精度差、算法不稳定等问题,提出一种新的煤矿井下巷道节点模型,并在此基础上提出基于粒子群算法的煤矿井下无线传感器网络节点三维定位算法。该算法将三维空间中未知节点与相邻的信标节点之间的估算距离和测量距离的均方误差作为优化的目标函数,通过粒子群优化来提高定位精度。理论分析和仿真结果表明,该算法具有定位精度高以及稳定性较好等特点,适用于煤矿井下无线传感器网络节点定位。      

Exploratory Factor Analysis of Wireline Logs Using a Float-Encoded Genetic Algorithm

In the paper, a novel inversion approach is used for the solution of the problem of factor analysis. The float-encoded genetic algorithm as a global optimization method is implemented to extract factor variables using open-hole logging data. The suggested statistical workflow is used to give a reliable estimate for not only the factors but also the related petrophysical properties in hydrocarbon formations. In the first step, the factor loadings and scores are estimated by Jöreskog’s fast approximate method, which are gradually improved by the genetic algorithm. The forward problem is solved to calculate wireline logs directly from the factor scores. In each generation, the observed and calculated well logs are compared to update the factor population. During the genetic algorithm run, the average fitness of factor populations is maximized to give the best fit between the observed and theoretical data. By using the empirical relation between the first factor and formation shaliness, the shale volume is estimated along the borehole. Permeability as a derived quantity also correlates with the first factor, which allows its determination from an independent source. The estimation results agree well with those of independent deterministic modeling and core measurements. Case studies from Hungary and the USA demonstrate the feasibility of the global optimization based factor analysis, which provides a useful tool for improved reservoir characterization.