The constructing of pore structure factor in carbonate rocks and the inversion of reservoir parameters

With a more complex pore structure system compared with clastic rocks, carbonate rocks have not yet been well described by existing conventional rock physical models concerning the pore structure vagary as well as the influence on elastic rock properties. We start with a discussion and an analysis about carbonate rock pore structure utilizing rock slices. Then, given appropriate assumptions, we introduce a new approach to modeling carbonate rocks and construct a pore structure algorithm to identify pore structure mutation with a basis on the Gassmann equation and the Eshelby-Walsh ellipsoid inclusion crack theory. Finally, we compute a single well＇s porosity using this new approach with full wave log data and make a comparison with the predicted result of traditional method and simultaneously invert for reservoir parameters. The study results reveal that the rock pore structure can significantly influence the rocks＇ elastic properties and the predicted porosity error of the new modeling approach is merely 0.74%. Therefore, the approach we introduce can effectively decrease the predicted error of reservoir parameters.

Prediction method of physical parameters based on linearized rock physics inversion

A linearized rock physics inversion method is proposed to deal with two important issues, rock physical model and inversion algorithm, which restrict the accuracy of rock physics inversion. In this method, first, the complex rock physics model is expanded into Taylor series to get the first-order approximate expression of the inverse problem of rock physics;then the damped least square method is used to solve the linearized rock physics inverse problem directly to get the analytical solution of the rock physics inverse problem. This method does not need global optimization or random sampling, but directly calculates the inverse operation, with high computational efficiency. The theoretical model analysis shows that the linearized rock physical model can be used to approximate the complex rock physics model. The application of actual logging data and seismic data shows that the linearized rock physics inversion method can obtain accurate physical parameters. This method is suitable for linear or slightly non-linear rock physics model, but may not be suitable for highly non-linear rock physics model.

THE APPLICATION OF GENETIC ALGORITHM IN NON-LINEAR INVERSION OF ROCK MECHANICS PARAMETERS

The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is presented at the end of this paper. From the satisfied result, quick, convenient and practical new approach is developed to solve this kind of problems.

A New Inversion Method of Sedimentary Strata For Deriving Double Parameters and Its Applications

The inverse problem of wave equation is the importance of study not only in seismic prospecting but also in applied mathematics. With the development of the research, the inverse methods of 1 - D wave equations have been trending towards the multiple parameters inversion . We have obtained an inverse method with double -parameter, in which medium density and wave velocity can be derived simultaneously. In this paper, to increase the inverse accuracy, the method is improved as follows. Firstly, the formula in which the Green Function is omitted are derived and used. Secondly, the regularizing method is reasonable used by choosing the stable function. With the new method, we may derive elastic parameter and medium density or medium density and wave velocity. Thus, lithology parameters for seismic prospecting may be obtained.After comparing the derived values from the new method with that from previous method, we obtain the new method through which substantially improve the derived accuracy . The new method has been applied to real depths inversion for sedimentary strata and volcanic rock strata in Chaoyanggou Terrace of Songliao Basin in eastern China. According to the inverse results,the gas - bearing beds are determlned.

Gas sand distribution prediction by prestack elastic inversion based on rock physics modeling and analysis

Seismic inversion is one of the most widely used technologies for reservoir prediction. Many good results have been obtained but sometimes it fails to differentiate the lithologies and identify the fluids. However, seismic prestack elastic inversion based on rock physics modeling and analysis introduced in this paper is a significant method that can help seismic inversion and interpretation reach a new quantitative （or semi-quantitative） level from traditional qualitative interpretation. By doing rock physics modeling and forward perturbation analysis, we can quantitatively analyze the essential relationships between rock properties and seismic responses and try to find the sensitive elastic properties to the lithology, porosity, fluid type, and reservoir saturation. Finally, standard rock physics templates （RPT） can be built for specific reservoirs to guide seismic inversion interpretation results for reservoir characterization and fluids identification purpose. The gas sand distribution results of the case study in this paper proves that this method has unparalleled advantages over traditional post-stack methods, by which we can perform reservoir characterization and seismic data interpretation more quantitatively and efficiently.

Rock physics inversion based on an optimized MCMC method

Rock physics inversion is to use seismic elastic properties of underground strata for predicting reservoir petrophysical parameters.The Markov chain Monte Carlo(MCMC)algorithm is commonly used to solve rock physics inverse problems.However,all the parameters to be inverted are iterated simultaneously in the conventional MCMC algorithm.What is obtained is an optimal solution of combining the petrophysical parameters with being inverted.This study introduces the alternating direction(AD)method into the MCMC algorithm(i.e.the optimized MCMC algorithm)to ensure that each petrophysical parameter can get the optimal solution and improve the convergence of the inversion.Firstly,the Gassmann equations and Xu-White model are used to model shaly sandstone,and the theoretical relationship between seismic elastic properties and reservoir petrophysical parameters is established.Then,in the framework of Bayesian theory,the optimized MCMC algorithm is used to generate a Markov chain to obtain the optimal solution of each physical parameter to be inverted and obtain the maximum posterior density of the physical parameter.The proposed method is applied to actual logging and seismic data and the results show that the method can obtain more accurate porosity,saturation,and clay volume.

准东地区云质岩储层叠前反演及脆性预测

脆性是一种评价致密储层可压裂性的重要指标,对油气勘探与开发有着重要的意义。准东地区勘探程度低、探井少,二叠系中下部云质岩致密储层的精细描述和脆性的准确预测均有着很大的难度。为此,根据岩石物理实验结果,利用叠前地震角度域数据体,开展储层叠前反演及脆性预测。通过AVO反演,获取P(截距)+G(梯度)和P×G属性;通过Y(杨氏模量,E)P(泊松比,σ)D(密度,ρ)和L(拉梅常数,λ)M(μ)R(ρ)反演,获取弹性参数的变化率(反射系数);通过测井约束反演,获取弹性参数的实际值,并用多种表征公式计算脆性,分析其物理含义及应用效果。研究结果表明:①研究区横波速度与纵波速度有较好的拟合关系,杨氏模量、泊松比和密度对泥岩、非泥岩区分度明显;②云质岩具有高速特征,研究区储层呈双“甜点”结构;③用三个小角度数据体直接反演弹性参数反射系数,YPD反演方法相较LMR的储层识别效果更好、分辨率更高,密度反射系数数据体可见比较明显的扇体及河道冲积特征;④脆性表征结果与弹性参数特征有一定的区别,脆性表征的云质岩有利区呈条带状分布,而弹性参数预测的云质岩有利区基本呈连续、成片分布;⑤ρE/σ为研究区最佳的脆性表征公式,可为类似油田云质岩储层脆性预测提供借鉴。

基于压痕试验的岩石力学参数的卡尔曼滤波反演

压痕试验是间接评估岩体力学特性的重要方法,为了更好地利用压痕试验评估岩体力学参数,本研究采用一种基于扩展卡尔曼滤波算法的参数反演方法。基于室内压痕试验获得的荷载-压入深度曲线数据,通过弹塑性数值仿真模型构建岩体弹塑性参数与压痕曲线的响应关系,进而利用卡尔曼滤波算法实现对岩石弹塑性参数的反演和优化分析。结果表明,本研究方法具有较好的收敛性和较高的识别精度,反演得到的岩石弹性参数均值的相对误差在10%左右,塑性参数的反演结果相对误差小于20%。较传统单一经验公式方法,本研究方法具有更高的预测精度,对多种不同岩性岩体都有较好的适用性。

基于岩石物理模型的碳酸盐岩物性参数替换方法

碳酸盐岩的物性参数在油气勘探和开发的过程中起着重要的作用,且碳酸盐岩为具有多重孔隙类型的岩石,复杂的孔隙类型使得孔隙度与弹性参数之间的关系非常离散。本文基于岩石物理模型,提出一种碳酸盐岩物性参数替换的方法,首先对碳酸盐岩储层进行岩石物理建模,对模型中的等效孔隙纵横比进行反演,在进行替换时,保持其他参数不变,只改变孔隙度、方解石含量、含水饱和度和孔隙形状的体积分数中的一项。再结合AVO理论,进行正演模拟,正演模拟揭示孔隙度与孔隙形状的变化对地震响应的影响更为强烈,远远大于方解石含量、含水饱和度变化的影响,方解石含量和含水饱和度的变化对于地震响应的影响较微弱。实际资料应用表明,文章提出的碳酸盐岩物性参数的替换方法可以有效地分析物性参数及孔隙形状变化的影响,表征岩石的物理性质并判断岩石的孔隙类型。

超声共振谱法岩石弹性参数测量研究进展

岩石弹性参数的准确测量对于地下资源的可靠评价和勘探开发具有重要意义。超声共振谱法基于共振频率与岩石弹性性质相关的原理,较传统方法如超声脉冲穿透法、应力应变法等,具有可重复测量、测量精度高、一次测量可获得全部弹性参数的优点,并且可用于不规则样品的测量,因此在岩石弹性参数测量方面拥有广阔的应用前景。该文主要综述了20世纪60年代以来超声共振谱方法的测量、正演方法和反演方法的发展历程,并分析了样品制备、测量方式和计算方法等因素对测量结果的影响,最后对近些年来该方法在测量方法、正演方法、反演方法、非线性测量及极端环境下的测量等方面的进展进行了总结及展望,对未来应重点关注的方向给出了建议,以期对于常规岩石以及页岩和海底矿物等非常规岩石的弹性参数测量方法的发展提供帮助。

基于BP神经网络的隧道围岩参数反演分析

以张家口市东太平山隧道工程项目为背景,基于MIDAS软件建立有限元模型,利用退火算法优化GA-BP神经网络,设计正交试验并构建GASA-BP神经网络,通过隧道拱顶沉降、水平收敛和仰拱隆起3个参数的模拟值对围岩的弹性模量、黏聚力、内摩擦角进行反演分析。结果表明:使用GASA-BP神经网络反演所得到隧道拱顶沉降、水平收敛和仰拱隆起的模拟值,与实测值的最大相差分别为6.60%、19.80%、2.16%;与BP神经网络相比,GASA-BP神经网络整体反演精度更高,反演所得围岩参数精度处于合理区间,在此参数下建立的有限元模型可以对工程实际进行较好地模拟。

基于遗传算法和FLAC 3D的隧道围岩参数反演研究

隧道围岩参数对于隧道工程的稳定性而言至关重要。传统参数确定方法耗时长、成本高,且难以反映围岩实际状况。研究提出一种基于遗传算法和FLAC 3D的隧道围岩参数反演方法,以提高参数反演的效率和准确性。遗传算法通过模拟自然选择和遗传机制,优化全局搜索过程,而FLAC 3D则利用其三维连续介质数值模拟功能,精确模拟岩土材料行为。通过西南某山区隧道工程案例分析,反演得到最优围岩力学参数,模拟结果与实测值的最大误差仅为1.98%,验证了方法的准确性。

Sweet spot prediction in tight sandstone reservoir based on well-bore rock physical simulation

To establish the relationship among reservoir characteristics and rock physical parameters,we construct the well-bore rock physical models firstly,considering the influence factors,such as mineral composition,shale content,porosity,fluid type and saturation.Then with analyzing the change rules of elastic parameters along with the above influence factors and the cross-plots among elastic parameters,the sensitive elastic parameters of tight sandstone reservoir are determined,and the rock physics template of sweet spot is constructed to guide pre-stack seismic inversion.The results show that velocity ratio and Poisson impedance are the most sensitive elastic parameters to indicate the lithologic and gas-bearing properties of sweet spot in tight sandstone reservoir.The high-quality sweet spot is characterized by the lower velocity ratio and Poisson impedance.Finally,the actual seismic data are selected to predict the sweet spots in tight sandstone gas reservoirs,so as to verify the validity of the rock physical simulation results.The significant consistency between the relative logging curves and inversion results in different wells implies that the utilization of well-bore rock physical simulation can guide the prediction of sweet spot in tight sandstone gas reservoirs.

Nonlinear simultaneous inversion of pore structure and physical parameters based on elastic impedance

Carbonate reservoirs have complex pore structures,which not only significantly affect the elastic properties and seismic responses of the reservoirs but also affect the accuracy of the prediction of the physical parameters.The existing rockphysics inversion methods are mainly designed for clastic rocks,and the inversion objects are generally porosity and water saturation.The data used are primarily based on the elastic parameters,and the inversion methods are mainly linear approximations.To date,there has been a lack of a simultaneous pore structure and physical parameter inversion method for carbonate reservoirs.To solve these problems,a new Bayesian nonlinear simultaneous inversion method based on elastic impedance is proposed.This method integrates the differential effective medium model of multiple-porosity rocks,Gassmann equation,Amplitude Versus Offset(AVO)theory,Bayesian theory,and a nonlinear inversion algorithm to achieve the simultaneous quantitative prediction of the pore structure and physical parameters of complex porous reservoirs.The forward modeling indicates that the contribution of the pore structure,i.e.,the pore aspect ratio,to the AVO response and elastic impedance is second only to that of porosity and is far greater than that of water saturation.The application to real data shows that the new inversion method for determining the pore structure and physical parameters directly from pre-stack data can accurately predict a reservoir's porosity and water saturation and can evaluate the pore structure of the effective reservoir.

鸡冠咀矿区岩体力学参数的确定及数值模拟反演分析

岩体力学参数作为矿井工程设计和施工的基础和依据,其选取的合理性对于预测矿山稳定性及后续支护都有重要意义。以鸡冠咀矿山巷道为主要研究对象,在室内外试验既得数据的基础上,根据正交试验与拉格朗日差分法(FLAC^(3D)),计算模拟出不同组合下的巷道变形,并开展水平极差分析确定各参数对巷道沉降影响的显著性,最后进行参数反演得出其力学响应规律。结果表明:内摩擦角、弹性模量和黏聚力对巷道围岩变形的影响依次减弱;采空区底板的上浮位移、浅部岩体的下沉位移均随开采深度增加逐步增大。

红层地区基坑锚杆抗拔承载力及计算参数时变特性

针对锚杆在服役过程中承载力可能下降的问题,对红层地区某基坑工程开展多批次锚杆拉拔试验,得到了红层软岩锚杆的荷载-位移曲线(P-S曲线),揭示了红层软岩基坑锚杆抗拔承载力随时间衰减的特征。首先基于锚固体-岩土界面的三折线本构模型,推导出锚杆端部(锚头)的荷载-位移关系式;然后基于现场试验数据,采用鲸鱼优化算法进行计算参数反演,得到关系式的计算参数值及其随时间的变化特性。结果表明:红层软岩基坑锚杆抗拔承载力随时间衰减,且承载力衰减幅度随着时间的增长并不收敛,荷载-位移关系式的计算参数也随时间变化:服役3年后,锚固界面极限强度降低了46.8%,侧摩阻力传递系数降低了60.6%,极限荷载下锚固体的脱黏段长度降低了16.7%,而极限剪切位移增加了36.9%;与服役近1年相比,锚杆抗拔承载力与计算参数随时间的变化没有稳定趋势,因此对基坑支护结构延长使用年限应谨慎对待。

基于岩屑分形理论的岩石力学参数实时评价方法

现有的岩石力学参数获取方法依赖于钻井取心和测井数据,具有较大的时效限制。基于分形理论构建岩屑分形几何参数与岩石本体力学参数的数学模型,并建立录井岩屑的分形维数测试方法,形成一种基于岩屑分形理论的岩石力学参数评价方法。首先,利用完钻井深层岩心开展力学测试,建立岩石力学参数与多项测井物理量多元统计学模型。然后,开展上返岩屑颗粒的分形维数测试,并依据测井岩石力学模型计算出岩屑原位地层的岩石力学参数,将这些力学参数与岩屑分形维数建立统计学关系式。以准噶尔盆地玛湖油田Ma123井为例,通过测试上返岩屑的分形维数,进而实时计算出岩石的力学参数。完钻测井后基于测井反演模型绘制出该井岩石力学参数剖面。两种方法对比认为,基于岩屑分形维数计算的岩石力学参数具有较高的精度(误差小于10.9%)。同时,通过实践总结出一套基于录井岩屑分形理论的岩石力学参数的实时预测方法。研究成果拓展了分形岩石力学的应用领域,为岩石力学参数的实时预测提供了新思路。

基于SGD算法优化的BP神经网络围岩参数反演模型研究

为充分利用现场监测数据所反馈的围岩变形信息,对岩体力学参数进行反演,以贵州省剑河至黎平高速公路TJ-1标段牛练塘隧道为工程背景,选择围岩弹性模量、黏聚力、泊松比及内摩擦角为影响因素,通过设计正交试验及有限元模拟,获取25组围岩物理力学参数组合及其对应的拱顶沉降值和拱腰收敛模拟值。基于随机梯度下降算法(stochastic gradient descent algorithm,简称SGD算法)对传统BP神经网络模型进行改进,建立以拱顶沉降值和拱腰收敛值为输入参数,以围岩弹性模量、黏聚力、泊松比及内摩擦角为输出值的基于SGD算法优化的BP神经网络模型,实现围岩参数的反演分析。将反演所得的围岩参数代入有限元模型,验证优化BP神经网络模型的可行性和准确性。最后,分析围岩变形及初期支护受力特性并给出施工建议。结果表明:1)基于SGD算法优化的BP神经网络模型计算得出的拱顶沉降值、拱腰收敛值、拱肩收敛值与现场实测值的相对误差率在2.50%~24.01%,均低于传统BP神经网络模型计算得出的误差率(11.51%~93.71%),验证优化BP神经网络模型的可行性和优越性;2)上、下台阶拱脚处的喷层和锚杆有应力集中现象,有破坏风险,建议施工中加强拱脚支护,防止发生工程事故。

基于BP神经网络的深部隧道蠕变模型多参数智能反演分析

为研究某高速公路深部隧道施工开挖后蠕变现象,需获取准确的蠕变本构参数并进一步推演未来围岩蠕变对隧道的影响。基于隧道某断面施工36 d实测位移数据,将BP神经网络多参数智能算法与FLAC^(3D)有限差分软件相结合,反演构建与实际沉降值相符合的4个参数Burgers蠕变本构模型并预测隧道后续100 d拱顶沉降。研究结果表明:拱顶沉降预测与实测值较为吻合,该隧道断面位移均经过快速、缓慢和稳定变形阶段,建议在快速变形、缓慢变形阶段内加强支护措施及监控频率。研究结果可为蠕变参数确定和隧道拱顶沉降预测提供相关理论参考。

基于改进粒子群算法的土石坝动力参数反演研究

【目的】针对现有土石坝本构模型参数较多,参数反演分析工作量大,且在动力参数反演模型算法中存在的智能算法容易陷入局部极值、神经网络计算精度依赖于样本的训练效率、反演参数精度低等问题,【方法】基于正交试验法,以坝体顺河向、坝轴向和竖直向三向加速度反应作为试验指标,对紫坪铺面板堆石坝进行动力本构模型参数的敏感性分析,定量分析各参数对坝体加速度分布的敏感性,为土石坝动力有限元分析时空间赋值模型参数的选择以及后续反演分析提供依据。将粒子群算法与云理论中的正向高斯云算法相结合,通过高斯云模型对陷入局部最优的粒子进行变异操作,以避免算法早熟收敛的问题,并利用地震实测响应信息对坝料动力参数进行反演。【结果】结果表明:紫坪铺面板堆石坝的坝料动力参数中k2、n、λmax是对坝体加速度反应敏感性最强的3个参数,基于反演参数对坝体进行动力分析结果与实际地震响应较为吻合,三个方向的最大绝对加速度均发生在坝顶中部附近,在坝轴向最为剧烈,顺河向次之,而竖直向反应最弱。【结论】通过定量分析参数敏感性以确定待反演参数,可在保证计算精度的前提下提高计算效率;通过验证分析可知,反演模型计算的动力参数能够较为准确地反映土体的动力特性,可满足实际工程精度要求;所提出的动力参数反演方法以及有关动力响应分析规律,可为土石坝工程动力参数的确定与大坝安全鉴定提供技术借鉴。