基于流体替换技术的地震AVO属性气藏识别(英文)

传统上,油藏地球物理工程师是基于测井数据进行流体替换,计算油藏饱和不同流体时的弹性参数,并通过地震正演模拟分析油藏饱和不同流体时的地震响应,从而进行油气藏识别研究。该研究方案为油藏研究提供了重要的弹性参数和地震响应信息,但这些信息仅限于井眼位置。对于实际油藏条件,地下储层参数都是随位置变化而变化的,如孔隙度、泥质含量和油藏厚度等,因此基于传统流体替换方案得到的流体变化地震响应信息对于油气藏识别具有很大的局限性。研究通过设定联系油藏弹性参数与孔隙度、矿物组分等参数的岩石物理模型,并基于三层地质模型,进行地震正演模拟与AVO属性计算。得到油藏孔隙度、泥质含量和储层厚度变化时地震AVO属性,并建立了饱和水储层和含气储层对应AVO属性(包括梯度与截距)之间的定量关系。建立的AVO属性之间的线性关系可以实现基于地震AVO属性直接进行流体替换。最后,应用建立的流体替换前后AVO属性之间线性方程,对模拟地震数据直接进行流体替换,并通过流体替换前后AVO属性交汇图分析实现了气藏识别。     

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.     

A generalized Biot–Gassmann model for the acoustic properties of shaley sandstones1

We obtain the wave velocities of clay-bearing sandstones as a function of clay content, porosity and frequency. Unlike previous theories, based simply on slowness and/or moduli averaging or two-phase models, we use a Biot-type three-phase theory that considers the existence of two solids (sand grains and clay particles) and a fluid. The theory, which is consistent with the critical porosity concept, uses three free parameters that determine the dependence of the dry-rock moduli of the sand and clay matrices as a function of porosity and clay content.
Testing of the model with laboratory data shows good agreement between predictions and measurements. In addition to a rock physics model that can be useful for petrophysical interpretation of wave velocities obtained from well logs and surface seismic data, the model provides the differential equation for computing synthetic seismograms in inhomogeneous media, from the seismic to the ultrasonic frequency bands.     

基于常规测井数据的纵波本征衰减估计

地震波本征衰减反映了地层及其所含流体的一些特性,对油气勘探开发有重要意义.已有的理论研究与实验发现,地震频带内的衰减主要与中观尺度(波长与颗粒尺度之间)的斑状部分饱和、完全饱和岩石弹性非均匀性情况下波诱导的局部流体流有关.这种衰减与岩石骨架、孔隙度及充填流体的性质密切相关.本文着重讨论均匀流体分布、斑状或非均匀流体分布两种情况下部分饱和岩石的纵波模量差异.以经典岩石物理理论和衰减机制认识为基础,通过分析低频松弛状态、高频非松弛状态岩石的弹性模量,讨论储层参数(如孔隙度、泥质含量以及含水饱和度等)与纵波衰减之间的确定性关系.上述方法与模型在陆相砂泥岩地层与海相碳酸盐岩地层中的适用性通过常规测井资料得到了初步验证.     

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.     

Prediction of effective porosity from seismic attributes using locally linear model tree algorithm

In this study, a locally linear model tree algorithm was used to optimize a neuro‐fuzzy model for prediction of effective porosity from seismic attributes in one of Iranian oil fields located southwest of Iran. Valid identification of effective porosity distribution in fractured carbonate reservoirs is extremely essential for reservoir characterization. These high‐accuracy predictions facilitate efficient exploration and management of oil and gas resources. The multi‐attribute stepwise linear regression method was used to select five out of 26 seismic attributes one by one. These attributes introduced into the neuro‐fuzzy model to predict effective porosity. The neuro‐fuzzy model with seven locally linear models resulted in the lowest validation error. Moreover, a blind test was carried out at the location of two wells that were used neither in training nor validation. The results obtained from the validation and blind test of the model confirmed the ability of the proposed algorithm in predicting the effective porosity. In the end, the performance of this neuro‐fuzzy model was compared with two regular neural networks of a multi‐layer perceptron and a radial basis function, and the results show that a locally linear neuro‐fuzzy model trained by a locally linear model tree algorithm resulted in more accurate porosity prediction than standard neural networks, particularly in the case where irregularities increase in the data set. The production data have been also used to verify the reliability of the porosity model. The porosity sections through the two wells demonstrate that the porosity model conforms to the production rate of wells. Comparison of the locally linear neuro‐fuzzy model performance on different wells indicates that there is a distinct discrepancy in the performance of this model compared with the other techniques. This discrepancy in the performance is a function of the correlation between the model inputs and output. In the case where the strength of the relationship between seismic attributes and effective porosity decreases, the neuro‐fuzzy model results in more accurate prediction than regular neural networks, whereas the neuro‐fuzzy model has a close performance to neural networks if there is a strong relationship between seismic attributes and effective porosity. The effective porosity map, presented as the output of the method, shows a high‐porosity area in the centre of zone 2 of the Ilam reservoir. Furthermore, there is an extensive high‐porosity area in zone 4 of Sarvak that extends from the centre to the east of the reservoir.     

A semi-empirical velocity-porosity-clay model for petrophysical interpretation of P- and S-velocities

We design a velocity–porosity model for sand-shale environments with the emphasis on its application to petrophysical interpretation of compressional and shear velocities. In order to achieve this objective, we extend the velocity–porosity model proposed by Krief et al., to account for the effect of clay content in sandstones, using the published laboratory experiments on rocks and well log data in a wide range of porosities and clay contents. The model of Krief et al. works well for clean compacted rocks. It assumes that compressional and shear velocities in a porous fluid-saturated rock obey Gassmann formulae with the Biot compliance coefficient. In order to use this model for clay-rich rocks, we assume that the bulk and shear moduli of the grain material, and the dependence of the compliance on porosity, are functions of the clay content. Statistical analysis of published laboratory data shows that the moduli of the matrix grain material are best defined by low Hashin–Shtrikman bounds. The parameters of the model include the bulk and shear moduli of the sand and clay mineral components as well as coefficients which define the dependence of the bulk and shear compliance on porosity and clay content. The constants of the model are determined by a multivariate non-linear regression fit for P- and S-velocities as functions of porosity and clay content using the data acquired in the area of interest. In order to demonstrate the potential application of the proposed model to petrophysical interpretation, we design an inversion procedure, which allows us to estimate porosity, saturation and/or clay content from compressional and shear velocities. Testing of the model on laboratory data and a set of well logs from Carnarvon Basin, Australia, shows good agreement between predictions and measurements. This simple velocity-porosity-clay semi-empirical model could be used for more reliable petrophysical interpretation of compressional and shear velocities obtained from well logs or surface seismic data.     

Seismic ground-roll noise attenuation using deep learning

We propose to adopt a deep learning based framework using generative adversarial networks for ground-roll attenuation in land seismic data. Accounting for the non-stationary properties of seismic data and the associated ground-roll noise, we create training labels using local time–frequency transform and regularized non-stationary regression. The basic idea is to train the network using a few shot gathers such that the network can learn the weights associated with noise attenuation for the training shot gathers. We then apply the learned weights to test ground-roll attenuation on shot gathers, that are not a part of training input to obtain the desired signal. This approach gives results similar to local time–frequency transform and regularized non-stationary regression but at a significantly reduced computational cost. The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Tests on field-data examples verify the effectiveness of the proposed approach.     

海洋含水合物沉积层的速度频散与衰减特征分析

随着水合物含量的增加,往往会引起纵、横波速度的增加,同时也会引起衰减的变化.针对含水合物沉积层的速度频散与衰减特征分析,有助于水合物含量的估计.本文以有效介质理论模型(EMT)为基础,研究了海洋未固结含水合物沉积层的纵、横波速度的非线性变化趋势.同时采用BISQ模型替代有效介质模型中的Gassmann方程,具体分析了全频带范围内海洋含水合物沉积层的速度频散与衰减特征.采用该模型,速度与衰减均随着水合物含量的增加而增加,且岩石孔隙度与泥质含量对衰减系数的影响较小.针对大洋钻探计划(ODP)164航次的实际数据,运用该模型方程计算采用声波测井数据(20kHz)与VSP数据(100Hz),分别获取了水合物稳定带的饱和度数据,平均在5%~7%之间,由于速度频散的影响,VSP估算结果要弱低于声波测井估算数据,均与实测保压取芯的甲烷含量数据、他人研究成果以及神经网络趋势预测结果均有着较好的一致性.对南海神狐海域三口钻位开展了水合物含量预测,与保压取芯结果有着较好的吻合关系.同时基于层剥离法提取该区域某地震测线BSR层的等效Q值,采用本文方法估算了该区域的等效天然气水合物含量15%~30%.数值模拟与实际应用结果表明:含水合物沉积层的速度频散与衰减特征均随着水合物含量的变化而变化,联合利用这一些变化特征,有助于天然气水合物含量的估计.     

基于自适应杂交遗传算法的CO_2地质封存的储层参数反演研究

二氧化碳地质封存是减少温室气体排放和减缓温室效应的重要手段.二氧化碳封存的一个重要组成部分是地震监测,即用地震的方法监测封存后的二氧化碳的分布变化.为了实现这个目标,需要建立储层参数与地震性质之间的关系(岩石物理模型)和从地震监测数据中反演获得储层流体的饱和度等参数.首先,本文以Biot理论为基础,结合多相流模型研究了多个物理参数(孔隙度、二氧化碳饱和度、温度和压力等)对同时含有二氧化碳和水的孔隙介质的波速和衰减等属性的影响.结果表明:孔隙度和二氧化碳饱和度对岩石的频散和衰减属性影响强烈,而温度和压力通过孔隙流体性质对岩石的波速产生影响.然后,本文基于含多相流的Biot理论,应用抗干扰能力强、且具有更好的局部搜索能力和抗早熟能力的自适应杂交遗传算法对实际数据进行了反演研究.对岩心实验数据的反演研究表明了算法的有效性,而且表明含多相流的Biot理论能够很好地解释水和二氧化碳饱和岩石的波速特征.最后,我们将自适应杂交遗传算法应用于实际封存项目的地震监测数据,获得了封存后不同时期的二氧化碳饱和度,达到了用地震方法监测二氧化碳分布的目的.     

岩石物理弹性参数规律研究

根据辽东湾凹陷某区在地层条件和不同流体相态(气饱和、水饱和等)下岩石纵波速度、横波速度及密度等岩心测试数据,以及岩石矿物成分、孔隙度等常规岩心分析数据,统计分析了岩石弹性参数变化规律.采用有效流体模型、斑块饱和模型进行了纵、横波速度理论计算,并和实验测量结果比较,认为高孔、高渗岩石可以看作有效流体模型,低孔、低渗岩石更接近斑块饱和模型.这些规律和认识对于指导储层预测和油气检测及地震振幅综合解释有重要的意义.     

Committee neural networks for porosity and permeability prediction from well logs

Neural computing has moved beyond simple demonstration to more significant applications. Encouraged by recent developments in artificial neural network (ANN) modelling techniques, we have developed committee machine (CM) networks for converting well logs to porosity and permeability, and have applied the networks to real well data from the North Sea. Simple three‐layer back‐propagation ANNs constitute the blocks of a modular system where the porosity ANN uses sonic, density and resistivity logs for input. The permeability ANN is slightly more complex, with four inputs (density, gamma ray, neutron porosity and sonic). The optimum size of the hidden layer, the number of training data required, and alternative training techniques have been investigated using synthetic logs. For both networks an optimal number of neurons in the hidden layer is in the range 8–10. With a lower number of hidden units the network fails to represent the problem, and for higher complexity overfitting becomes a problem when data are noisy. A sufficient number of training samples for the porosity ANN is around 150, while the permeability ANN requires twice as many in order to keep network errors well below the errors in core data. For the porosity ANN the overtraining strategy is the suitable technique for bias reduction and an unconstrained optimal linear combination (OLC) is the best method of combining the CM output. For permeability, on the other hand, the combination of overtraining and OLC does not work. Error reduction by validation, simple averaging combined with range‐splitting provides the required accuracy. The accuracy of the resulting CM is restricted only by the accuracy of the real data. The ANN approach is shown to be superior to multiple linear regression techniques even with minor non‐linearity in the background model.     

地震黄土滑坡滑距预测的BP神经网络模型

地震滑坡的滑距与重力滑坡的滑距有着显著的不同,科学预测地震发生时黄土地区滑坡的滑动距离是合理评估黄土地区滑坡风险和减轻滑坡灾害的有效方式之一。基于海原特大地震诱发黄土滑坡的400组野外调查数据,通过引入BP神经网络算法,论证了BP神经网络模型用于预测黄土地震滑坡滑距的适宜性和可行性;建立了地震诱发黄土滑坡滑距的BP神经网络预测模型,并通过67组数据进行了验证。BP神经网络算法和传统多元线性回归、多元非线性回归结果的对比显示,BP神经网络的预测更接近真实情况,具有较为理想的预测效果,可以用于黄土地震滑坡滑距的预测,并为圈定较为可靠的致灾范围提供依据。     

致密砂岩衰减岩石物理图板分析:储层微裂隙预测

致密砂岩储层普遍具有孔隙度低、微裂隙发育的特点,岩石内部常含有强烈的结构非均质性.致密砂岩发育的微裂隙使储层具有良好的连通性,促成高饱和气的天然气成藏.针对川西某探区须家河组高含气饱和度致密砂岩,本文选取致密砂岩岩心样本,进行了不同围压下的超声波实验测量.考虑储层完全饱气情况下的粒间孔隙、微裂隙双重孔隙结构,采用Biot-Rayleigh双重孔隙方程,构建致密砂岩岩石物理模型,进而分析了裂隙含量对纵波频散和衰减的影响.基于地震波衰减,构建了致密砂岩多尺度岩石物理图板.采用谱比法和改进频移法估算致密砂岩样本及储层衰减,对超声和地震频带下的图板进行校正.将校正后的图板应用到研究工区,选取二维测线和三维区块,进行储层孔隙度和裂隙含量的定量预测.对比实际资料进行分析,结果显示,本文预测的孔隙度和裂隙含量与三口测井的孔隙度曲线和实际产气情况基本吻合,基于孔隙-裂隙衰减岩石物理模型有效地预测了优质储层的分布区域.     

含泥质低孔渗各向异性黏弹性介质中的波频散和衰减研究

本文定义了各向异性黏弹性参数修正因子,并将其引入到黏弹性模型中以体现泥质含量对黏弹性机制的影响,同时将波传播过程中孔隙介质骨架黏弹性力学机制与两种孔隙流体流动力学机制（Biot流动和喷射流动机制）有机地统一起来处理,从而给出了描述含泥质低孔渗孔隙各向异性介质中波传播规律的黏弹性Biot/squirt (BISQ)模型.数值计算结果表明,入射波的方位角、各向异性渗透率以及泥质含量等对含流体复杂孔隙介质中波频散和衰减的影响具有显著的方位各向异性特征,在低频范围内（地震波勘探频率）黏弹性力学机制对波传播能量的衰减起主导作用.     

Common image gather conditioning using cycle generative adversarial networks

Seismically derived amplitude-versus-angle attributes along with well constraints are the base inputs into inverting seismic into subsurface properties. Conditioning the common image gathers is a common workflow in quantitative inversion and leads to a more accurate inversion product due to the removal of post-migration artefacts. Here, we apply a neural network to condition the post-migration gathers. The network is a cycle generative adversarial network, CycleGAN, which was designed for image-to-image translation. This can be considered the same problem as translating an artefact rich seismic gather to an artefact free seismic gather. To assess the feasibility of applying the network to pre-stack conditioning, synthetic data sets were generated to train different networks for different tasks. The networks were trained to remove white noise, residual de-multiples, gather flattening and a combination of the above for conditioning. The results show that a trained network was able remove white noise providing a more robust amplitude-versus-offset calculation. Another network trained using synthetic gathers with and without multiples assisted in multiple removal. However, instability around primary preservation has been observed so the network works better as a residual de-multiple method. For gather conditioning, a network was trained with the unpaired artefact-rich and artefact-free training data where the artefacts included complex moveout, noise and multiples. When applied to the test data sets, the networks cleaned the artefact-rich test data and translated complex moveout into flat gathers whilst preserving the amplitude response. Finally, two networks are applied to real data where a gather based on the well logs is used to quantify the match between the conditioned gathers and the raw gathers. The first network used synthetic data to train the network and, when applied to real data, provided a better tie with the well. The second network was trained with synthetic gathers whose properties were constrained by real seismic gathers from near the well. As anticipated, the network trained on the representative training data outperforms the network trained using the unconstrained data. However, the ability of the first network to condition the gather indicates that a sweep of networks can be trained without the need for real data and applied in a manner analogous to the way parameters are adjusted in traditional geophysical methods. The results show that the different neural networks can offer an alternative or augmentation to the existing geophysical workflow for conditioning pre-stack seismic gathers.     

裂隙参数对衰减各向异性影响的数值模拟

Propagation through stress-aligned fluid-filled cracks and other inclusions have been claimed to be the cause of azimuthal anisotropy observed in the crust and upper mantle.This paper examines the behavior of seismic waves attenuation caused by the internal structure of rock mass,and in particular,the internal geometry of the distribution of fluid-filled openings Systematic research on the effect of crack parameters,such as crack density,crack aspect ratio(the ratio of crack thickness to crack diameter),pore fluid properties(particularly pore fluid velocity),VP/VS ratio of the matrix material and seismic wave frequency on attenuation anisotropy has been conducted based on Hudson’s crack theory.The result shows that the crack density,aspect ratio,material filler,seismic wave frequency,and P-wave and shear wave velocity in the background of rock mass,and especially frequency has great effect on attenuation curves.Numerical research can help us know the effect of crack parameters and is a good supplement for laboratory modeling.However,attenuation is less well understood because of the great sensitivity of attenuation to details of the internal geometry.Some small changes in the characteristics of pore fluid viscosity,pore fluids containing gas and liquid phases and pore fluids containing clay can each alter attenuation coefficients by orders of magnitude.Some parameters controlling attenuation are therefore necessary to make reasonable estimations,and anisotropic attenuation is worth studying further.     

Joint elastic‐electrical properties of reservoir sandstones and their relationships with petrophysical parameters

We measured in the laboratory ultrasonic compressional and shear‐wave velocity and attenuation (0.7–1.0 MHz) and low‐frequency (2 Hz) electrical resistivity on 63 sandstone samples with a wide range of petrophysical properties to study the influence of reservoir porosity, permeability and clay content on the joint elastic‐electrical properties of reservoir sandstones. P‐ and S‐wave velocities were found to be linearly correlated with apparent electrical formation factor on a semi‐logarithmic scale for both clean and clay‐rich sandstones; P‐ and S‐wave attenuations showed a bell‐shaped correlation (partial for S‐waves) with apparent electrical formation factor. The joint elastic‐electrical properties provide a way to discriminate between sandstones with similar porosities but with different clay contents. The laboratory results can be used to estimate sandstone reservoir permeability from seismic velocity and apparent formation factor obtained from co‐located seismic and controlled source electromagnetic surveys.     

Petrophysical inversion of AVA data

We investigate the interactions between the elastic parameters, V_P, V_S and density, estimated by non-linear inversion of AVA data, and the petrophysical parameters, depth (pressure), porosity, clay content and fluid saturation, of an actual gas-bearing reservoir. In particular, we study how the ambiguous solutions derived from the non-uniqueness of the seismic inversion affect the estimates of relevant rock properties. It results that the physically admissible values of the rock properties greatly reduce the range of possible seismic solutions and this range contains the actual values given by the well. By means of a statistical inversion, we analyse how approximate a priori knowledge of the petrophysical properties and of their relationships with the seismic parameters can be of help in reducing the ambiguity of the inversion solutions and eventually in estimating the petrophysical properties of the specific target reservoir. This statistical inversion allows the determination of the most likely values of the sought rock properties along with their uncertainty ranges. The results show that the porosity is the best-resolved rock property, with its most likely value closely approaching the actual value found by the well, even when we insert somewhat erroneous a priori information. The hydrocarbon saturation is the second best-resolved parameter, but its most likely value does not match the well data. The depth of the target interface is the least-resolved parameter and its most likely value is strongly dependent on a priori information. Although no general conclusions can be drawn from the results of this exercise, we envisage that the proposed AVA–petrophysical inversion and its possible extensions may be of use in reservoir characterization.     

A new fluid factor and its application using a deep learning approach

Amplitude interpretation for hydrocarbon prediction is an important task in the oil and gas industry. Seismic amplitude is dominated by porosity, the volume of clay, pore-filled fluid type and lithology. A few seismic attributes are proposed to predict the existence of hydrocarbon. This paper proposes a new fluid factor by adding a correct item based on the J attribute. The algorithm is verified through stochastic Monte Carlo modelling that contains various rock physical properties of sand and shale. Both gas and oil responses are separated by the new fluid factor. Furthermore, an approach based on the neural network model is trained using the deep learning method to predict the new fluid factor. The confusion matrix shows that this model performs well. This model allows the application of the new fluid factor in the seismic data. In this study, the Marmousi II data set is used to examine the performance of the new fluid factor, and the result is good. Most hydrocarbon reservoirs are identified in the shale–sandstone sequences. The combination of deep learning and the new fluid factor provides a more accurate way for hydrocarbon prediction.