基于叠前反演的流体敏感属性实验研究及应用

提取叠前地震振幅信息的叠前反演技术已成为储层预测的重要手段,其能获得各种岩石弹性参数,丰富储层预测方法.因目标储层的差异性,优选并建立有利的流体敏感参数对储层流体检测尤为重要.本文基于岩石物理实验, 测量并分析了岩石弹性参数随流体饱和度的变化特征, 进一步根据岩石物理理论建立组合流体敏感参数, 达到对油气检测的最佳敏感效果.定义了流体敏感量,定量分析岩石弹性参数的流体敏感性.最后本文在X区块进行了叠前地震反演的应用, 结果表明通过岩石物理实验分析并建立获得的流体敏感参数能明显的提高储层的识别能力.     

基于岩石物理分析的叠前弹性反演预测含气砂岩分布(英文)

地震反演是当今最广泛应用于含油气储层预测的技术之一,取得了很多很好的预测效果,但也有失败的例子,达不到区分岩性和识别流体的目的。而本文介绍的建立在岩石物理建模和分析基础上的地震弹性反演,可将含油气储层预测由定性向(半)定量推进一步。根据岩石物理建模和正演扰动分析,可深刻理解岩石物性参数与地震弹性参数之间的内在关系,进而寻找储层岩性、物性和含油气性的敏感地震弹性参数,建立起理论岩石物理解释图版。岩石物理分析结果和所建立的岩石物理解释图版分别用以指导地震反演和反演结果的解释,实现油气储层分布预测和流体检测的目的。文中的含气砂岩分布预测实例研究应用结果表明,这种方法较叠后地震反演储层预测技术具有无可比拟的优越性,效果更佳,效率更高。     

岩石物理驱动下地震流体识别研究

地震流体识别指利用地震资料对储层含流体特征进行识别与描述.含流体储层地震岩石物理是地震流体识别的基础,是搭建储层弹性参数与物性参数的桥梁,是实现含油气储层流体定量表征的重要发展方向.岩石物理驱动下地震流体识别研究有助于认识地下油气储层含流体特征及分布规律.文章概述地震流体识别及相关基础研究中的关键科学问题,着重评述国内外岩石物理驱动下地震流体识别研究的主要进展,探究地震流体识别研究面临的机遇,挑战及未来的研究方向.理论研究和实际应用表明,地震流体识别要以岩石物理及数值模拟为理论基础,发展有效的流体敏感参数构建及评价方法;以地震资料为数据支撑,形成有效的地震资料品质评价方法;以地震反演为技术保障,发展可靠的地震反演策略.     

基于等效各向异性和流体替换的地下裂缝地震预测方法

地下裂缝是油气聚集和运移的重要通道,而裂缝岩石物理是裂缝参数与地震响应之间联系的桥梁.从裂缝岩石物理出发,探索利用地震数据预测地下裂缝的方法.首先通过构建裂缝岩石物理等效模型,弥补测井横波的缺失,并且实现裂缝岩石物理参数的预测;然后推导了裂缝岩石物理参数与地震响应之间的近似关系式,同时探索裂缝岩石弹性参数和岩石物理参数的地震直接反演方法;最后分别利用测井数据和实际工区地震数据对裂缝岩石物理等效模型的可靠性以及裂缝岩石物理参数直接反演方法的精度进行了验证.结果表明,构建的裂缝岩石物理等效模型可以实现裂缝岩石纵横波速度及岩石物理参数的可靠估测,而且裂缝岩石物理参数地震直接反演方法具有较高的抗噪性,在实际目标工区弹性参数和裂缝岩石物理参数的估算中具有较好的应用结果.     

基于方位各向异性弹性阻抗的裂缝岩石物理参数反演方法研究

裂缝储层岩石物理参数的准确获得对地下裂缝预测具有重要意义,而叠前地震反演是获得裂缝岩石物理参数的有效手段.本文从裂缝岩石物理等效模型的构建出发,从测井数据上估测了裂缝岩石物理参数,通过推导含裂缝岩石物理参数的方位各向异性弹性阻抗公式,探讨了基于方位各向异性弹性阻抗的裂缝岩石物理参数地震反演方法.实际工区地震数据应用表明,基于方位各向异性弹性阻抗的裂缝岩石物理参数反演方法合理、可靠,可以降低裂缝岩石物理参数估测的不确定性,为地下裂缝预测提供有力的依据.     

岩石物理驱动的储层物性参数非线性地震反演方法

叠前地震反演和岩石物理反演分别是获取弹性参数和物性参数的重要手段,两者结合有助于实现储层参数预测并精细刻画储层特征.储层物性参数的反演依赖于岩石物理模型,在进行物性参数反演时可以将复杂的岩石物理模型做泰勒展开,进而得到其一阶或高阶的近似表达式,然而这会降低模型的精确性并增加反演的误差.为了提高储层物性参数反演的稳定性和准确性,本文以碎屑岩储层为例,提出了岩石物理驱动的储层物性参数非线性地震反演方法.首先,基于贝叶斯框架和高斯分布约束条件,从叠前地震数据中实现纵、横波速度及密度等弹性参数的反演.其次,通过碎屑岩岩石物理模型建立起弹性参数与物性参数之间的联系.最后,利用粒子群算法进行全局寻优获得较为准确的孔隙度、泥质含量和含水饱和度等物性参数.合成数据和实际资料测试结果验证了所提方法的可行性和准确性,反演结果与测井数据吻合较好,可有效指示含气储层区域,本文方法在储层预测和评价方面具有广泛的应用前景.     

基于统计岩石物理模型的各向异性页岩储层参数反演

提出了各向异性页岩储层统计岩石物理反演方法.通过统计岩石物理模型建立储层物性参数与弹性参数的定量关系,使用测井数据及井中岩石物理反演结果作为先验信息,将地震阻抗数据定量解释为储层物性参数、各向异性参数的空间分布.反演过程在贝叶斯框架下求得储层参数的后验概率密度函数,并从中得到参数的最优估计值及其不确定性的定量描述.在此过程中综合考虑了岩石物理模型对复杂地下介质的描述偏差和地震数据中噪声对反演不确定性的影响.在求取最大后验概率过程中使用模拟退火优化粒子群算法以提高收敛速度和计算准确性.将统计岩石物理技术应用于龙马溪组页岩气储层,得到储层泥质含量、压实指数、孔隙度、裂缝密度等物性,以及各向异性参数的空间分布及相应的不确定性估计,为页岩气储层的定量描述提供依据.     

叠前反演和地震吸收技术在复杂天然气藏地震预测中的应用

针对某复杂断块天然气目标储层,在岩石物理分析的指导下,综合利用地质、地震、测井等资料,提出了一套面向复杂天然气藏的叠前地震预测技术.首先基于地震岩石物理分析得到的初始横波信息,采用叠前贝叶斯非线性三参数反演得到了井旁控制点处精确纵横波速度和密度信息,然后通过叠前/叠后联合反演技术实现了面向目标的弹性阻抗体反演及含气储层敏感参数直接提取,最后结合小波变换时频谱分析的方法从叠前地震资料中估算地层吸收参数值,提高天然气藏识别精度.实际应用表明,综合各种叠前地震预测技术,可以大大提高对复杂天然气藏的识别精度,降低勘探风险.     

流体饱和砂岩在高压下的物性参数

一、引言 岩石在高围压下的物性参数,包括岩石的体应变、裂纹孔隙度、密度、纵波速度、横波速度和波阻抗等,对石油地球物理勘探和其它许多地球物理学分支都具有十分重要的意义。当前岩性地震和油储地震的发展,为划分地下地层及油气层提供了可能性。由于地下地层构造和岩石物理性质都是未知因素,从而引起解释的不确定性。所以,在实验室内模拟地层环境,测量岩石的物性参数,具有重要参考价值。     

秦皇岛32-6油田明化镇组河流相储层地球物理参数影响因素研究

秦皇岛32-6油田明化镇组储层为河流相沉积,单砂体规模小,砂体间接触关系复杂,受到地震分辨率限制,对叠置砂体储层地震响应规律认识不明确,导致基于地震信息的储层砂体解释错误.通过数值正演模拟,可以正确认识砂体储层的地震响应规律,提高砂体储层预测的准确率.所以,在进行地震响应正演模拟前,非常有必要对该区储层地球物理参数影响因素展开研究,通过分析研究区14口测井资料,建立了岩石物理参数(孔隙度、泥质含量、合流体性)与地球物理参数(速度、密度、波阻抗等)之间的相关关系,分析了不同沉积微相对地球物理参数的控制作用,并给出了相应的数学表达式,为正确认识河流相储层砂体地震响应规律奠定了准确的地球物理参数基础.     

储层弹性与物性参数地震叠前同步反演的确定性优化方法

储层弹性与物性参数可直接应用于储层岩性预测和流体识别,是储层综合评价和油气藏精细描述的基本要素之一.现有的储层弹性与物性参数地震同步反演方法大都基于Gassmann方程,使用地震叠前数据,通过随机优化方法反演储层弹性与物性参数;或基于Wyllie方程,使用地震叠后数据,通过确定性优化方法反演储层弹性与物性参数.本文提出一种基于Gassmann方程、通过确定性优化方法开展储层弹性和物性参数地震叠前反演的方法,该方法利用Gassmann方程建立储层物性参数与叠前地震观测数据之间的联系,在贝叶斯反演框架下以储层弹性与物性参数的联合后验概率为目标函数,通过将目标函数的梯度用泰勒公式展开得到储层弹性与物性参数联合的方程组,其中储层弹性参数对物性参数的梯度用差分形式表示,最后通过共轭梯度算法迭代求解得到储层弹性与物性参数的最优解.理论试算与实际资料反演结果证明了方法的可行性.     

Well tie,fluid substitution and AVO modelling: a North Sea example

Accurate well ties are essential to practical seismic lithological interpretation. As long as the geology in the vicinity of the reservoir is not unduly complex, the main factors controlling this accuracy are the processing of the seismic data and the construction of the seismic model from well logs. This case study illustrates how seismic data processing to a near-offset stack, quality control of logs and petrophysical modelling improved a well tie at an oil reservoir. We demonstrate the application of a predictive petrophysical model in the preparation and integration of the logs before building the seismic model and we quantify our improvements in well-tie accuracy. The data for the study consisted of seismic field data from a 3D sail line through a well in a North Sea oilfield and a suite of standard logs at the well. A swathe of fully processed 3D data through the well was available for comparison. The well tie in the shallow section from first-pass seismic data processing and a routinely edited sonic log was excellent. The tie in a deeper interval containing the reservoir was less satisfactory: the phase errors within the bandwidth of the seismic wavelet were of the order of 20°, which we consider too large for subsequent transformation of the data to seismic impedance. Reprocessing the seismic data and revision of the well-log model reduced these phase errors to less than 10° and improved the consistency of the deep and shallow well ties. The reprocessing included densely picked iterative velocity analysis, prestack migration, beam-forming multiple attenuation, stacking the near-offset traces and demigration and remigration of the near-offset data. The petrophysical model was used to monitor and, where necessary, replace the P-wave sonic log with predictions consistent with other logs and to correct the sonic log for mud-filtrate invasion in the hydrocarbon-bearing sand. This editing and correction of the P-wave transit times improved the normal-incidence well tie significantly. The recordings from a monopole source severely underestimated the S-wave transit times in soft shale formations, including the reservoir seal, where the S-wave velocity was lower than the P-wave velocity in the drilling mud. The petrophysical model predicted an S-wave log that matched the valid recordings and interpolated between them. The subsequent seismic modelling from the predicted S-wave log produced a class II AVO anomaly seen on the CDP gathers around the well.     

孔隙结构对储层电性及测井解释评价的影响（英文）

在多个区块的测井评价工作发现,孔隙结构直接影响储集层的品质和油气层的电阻率,是测井准确评价流体性质的关键。岩石物理资料表明不同区块内影响储层孔隙结构的因素不同,但效果是一致的,即孔隙结构的复杂程度控制着储层的储集能力和渗透能力。孔隙结构的复杂程度影响了储层中导电流体的分布和含量,从而控制了储层的电阻率。储层出现低阻油气层的内因均为复杂的孔隙结构（骨架导电及工程原因除外）。测井储层评价在分析控制储层孔隙结构复杂程度的地质因素及储层分类的基础上,针对不同类型储层采用不同的模型、参数和标准,可以有效的认识储层品质和识别不同类型储层的流体性质。     

基于弹性阻抗的储层物性参数预测方法

储层物性参数是储层描述的重要参数,常规的基于贝叶斯理论的储层物性参数反演方法大多是通过反演获得的弹性参数进一步转换而获得物性参数,本文提出一种基于弹性阻抗数据预测储层物性参数的反演方法.该方法主要通过建立可以表征弹性阻抗与储层物性参数之间关系的统计岩石物理模型,联合蒙特卡罗仿真模拟技术,在贝叶斯理论框架的指导下,应用期望最大化算法估计物性参数的后验概率分布,最终实现储层物性参数反演.经过模型测试和实际资料的处理,其结果表明本文提出的方法具有预测精度高,稳定性强,横向连续性好等优点.     

多波时移地震AVO反演研究

数值模拟了油藏含油饱和度与有效压力变化时移地震AVO的响应,确定利用时移地震AVO区分油藏参数的变化、实现油藏定量解释的可行性.从Aki等 AVO近似方程出发,详细推导了P_P波和P_S转换波时移地震AVO计算公式.结合岩石物理近似关系和本文推导的时移地震AVO计算公式,推导了利用多波时移地震AVO反演油藏含油饱和度和压力变化的方程.数据试验表明,文中推导的多波时移地震AVO方程能较好地反演油藏含油饱和度变化和有效压力变化,实现油藏定量解释.     

用岩芯模拟测试参数做模型正演预测储层油、气、水边界

利用地震资料进行AVO油气检测,需要提供准确的储层岩石物性参数．通过对准噶尔盆地西部储层岩样实验室测定,得出合不同流体岩石在不同温度、压力下纵横波速度、速度比、泊松比的变化规律及差异．根据其差异性,用Zoeppritz方程做模型正演,以确定目的层有无AVO响应,以便对地震剖面做针对性的特殊处理．实际应用结果表明,用实验室测试的岩石物性参数做模型正演,可提高AVO检测的准确性,为用地震资料结合实验室岩芯测试参数预测地层油、气、水边界提供了有效手段．     

Multi‐dimensional analyses of the SEAM controlled source electromagnetic data—the story of a blind test of interpretation workflows

Using a subset of the SEG Advanced Modeling Program Phase I controlled‐source electromagnetic data, we apply our standard controlled‐source electromagnetic interpretation workflows to delineate a simulated hydrocarbon reservoir. Experience learned from characterizing such a complicated model offers us an opportunity to refine our workflows to achieve better interpretation quality. The exercise proceeded in a blind test style, where the interpreting geophysicists did not know the true resistivity model until the end of the project. Rather, the interpreters were provided a traditional controlled‐source electromagnetic data package, including electric field measurements, interpreted seismic horizons, and well log data. Based on petrophysical analysis, a background resistivity model was established first. Then, the interpreters started with feasibility studies to establish the recoverability of the prospect and carefully stepped through 1D, 2.5D, and 3D inversions with seismic and well log data integrated at each stage. A high‐resistivity zone is identified with 1D analysis and further characterized with 2.5D inversions. Its lateral distribution is confirmed with a 3D anisotropic inversion. The importance of integrating all available geophysical and petrophysical data to derive more accurate interpretation is demonstrated.     

PARAMETER ESTIMATION AND FAULT DETECTION BY THREE-COMPONENT SEISMIC AND GEOELECTRICAL SURVEYS IN A COAL MINE*

Three-component seismic and geoelectrical in-mine surveys were carried out in Lyukobanya colliery near Miskolc, Hungary to determine the in situ petrophysical parameter distributions and to detect inhomogeneities in the coal seam. The seismic measurements comprise an underground vertical seismic profile, using body waves, and an in-seam seismic amplitude-depth distribution and transmission survey, using channel waves. The geoelectrical measurements are based on the drift- and seam-sounding method. Interval traveltime-, amplitude-, multiple-filter- and polarization analysis methods are applied to the seismic data. They lead to a five-layer model for the strata including the coal seam. The coal seam and two underlying beds act as a seismic waveguide. The layer sequence supports the propagation of both normal and leaky mode channel waves of the Love- and Rayleigh type. A calculation of the total reflected energy for each interface using Knott's energy coefficients shows that the velocity ranges of high reflection energy and of normal and leaky mode wavegroups coincide. The excitation of wavegroups strongly depends on the seismic source. A simultaneous inversion of a geoelectrical drift- and seam-sounding survey prevents misinterpretations of the seismic data by clearly identifying the low-velocity coal seam as a high-resistivity bed. Calculations of dispersion and sounding curves improve the resolution of the slowness and resistivity in each layer. Both diminished amplitudes and distortions in the polarization of transmission seismo-grams and decreasing resistivities in a geoelectrical pseudosection of the coal seam are related to an inhomogeneity. A calculation of synthetic seismograms for Love and Rayleigh channel waves with the finite-difference and the Alekseev-Mikhailenko method agrees well with the field data for the main features, i.e., particular arrivals in the wave train, wavegroups, velocities and symmetries or asymmetries. This in-mine experiment demonstrates that the simultaneous acquisition, processing and interpretation of seismic and geoelectrical data improve the lithological interpretation of petrophysical parameter distributions. Coal seam inhomogeneities can also be detected more reliably by the two independent surveys than by one alone.     

Effects of pore aspect ratios on velocity prediction from well‐log data

We develop a semi‐empirical model which combines the theoretical model of Xu and White and the empirical formula of Han, Nur and Morgan in sand–clay environments. This new model may be used for petrophysical interpretation of P‐ and S‐wave velocities. In particular, we are able to obtain an independent estimation of aspect ratios based on log data and seismic velocity, and also the relationship between velocities and other reservoir parameters (e.g. porosity and clay content), thus providing a prediction of shear‐wave velocity. To achieve this, we first use Kuster and Toksöz's theory to derive bulk and shear moduli in a sand–clay mixture. Secondly, Xu and White's model is combined with an artificial neural network to invert the depth‐dependent variation of pore aspect ratios. Finally these aspect ratio results are linked to the empirical formula of Han, Nur and Morgan, using a multiple regression algorithm for petrophysical interpretation. Tests on field data from a North Sea reservoir show that this semi‐empirical model provides simple but satisfactory results for the prediction of shear‐wave velocities and the estimation of reservoir parameters.