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.     

Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin,India

Estimation of petrophysical parameters is an important issue of any reservoirs. Porosity, volume of shale and water saturation has been evaluated for reservoirs of Upper Assam basin, located in northeastern India from well log and seismic data. Absolute acoustic impedance (AAI) and relative acoustic impedance (RAI) are generated from model based inversion of 2-D post-stack seismic data. The top of geological formation, sand reservoirs, shale layers and discontinuities at faults are detected in RAI section under the study area. Tipam Sandstone (TS) and Barail Arenaceous Sandstone (BAS) are the main reservoirs, delineated from the logs of available wells and RAI section. Porosity section is obtained using porosity wavelet and porosity reflectivity from post-stack seismic data. Two multilayered feed forward neural network (MLFN) models are created with inputs: AAI, porosity, density and shear impedance and outputs: volume of shale and water saturation with single hidden layer. The estimated average porosity in TS and BAS reservoir varies from 30% to 36% and 18% to 30% respectively. The volume of shale and water saturation ranges from 10% to 30% and 20% to 60% in TS reservoir and 28% to 30% and 23% to 55% in BAS reservoir respectively.     

Ensemble-based conditioning of reservoir models to seismic data

While 3D seismic has been the basis for geological model building for a long time, time-lapse seismic has primarily been used in a qualitative manner to assist in monitoring reservoir behavior. With the growing acceptance of assisted history matching methods has come an equally rising interest in incorporating 3D or time-lapse seismic data into the history matching process in a more quantitative manner. The common approach in recent studies has been to invert the seismic data to elastic or to dynamic reservoir properties, typically acoustic impedance or saturation changes. Here we consider the use of both 3D and time-lapse seismic amplitude data based on a forward modeling approach that does not require any inversion in the traditional sense. Advantages of such an approach may be better estimation and treatment of model and measurement errors, the combination of two inversion steps into one by removing the explicit inversion to state space variables, and more consistent dependence on the validity of assumptions underlying the inversion process. In this paper, we introduce this approach with the use of an assisted history matching method in mind. Two ensemble-based methods, the ensemble Kalman filter and the ensemble randomized maximum likelihood method, are used to investigate issues arising from the use of seismic amplitude data, and possible solutions are presented. Experiments with a 3D synthetic reservoir model show that additional information on the distribution of reservoir fluids, and on rock properties such as porosity and permeability, can be extracted from the seismic data. The role for localization and iterative methods are discussed in detail.     

基于岩石物理模版的碳酸盐岩含气储层定量解释

笔者针对碳酸盐岩储层勘探开发面临的储层预测与流体识别难度大的问题,基于岩石物理理论,提出了一种对碳酸盐岩含气储层进行定量解释的方法:首先,综合碳酸盐岩地质和测井信息,以岩石物理理论为依据,实现了适用的碳酸盐岩岩石物理模型的构建;然后基于岩石物理模型优选出对孔隙度、含气饱和度较为敏感的参数λρ和μρ,构建能够反映储层孔隙度与含气饱和度的碳酸盐岩岩石物理模版;最后,将岩石物理模版与工区反演提取的参数相结合,确定储层分布范围,并对储层范围内孔隙度和含气饱和度数值进行了定量解释。实例分析表明,基于该模版定量解释得到的孔隙度和含气饱和度与实际地层孔隙度及产气结果基本一致,经验证碳酸盐岩岩石物理模版的可靠性和适用性较好。     

基于AVO的属性分析

针对川中地区砂体分布多,气藏分布在砂体的那个位置很难确定这一情况,对叠前数据用AVO中的Lambda-Mu-Rho技术获得流体的不可压缩性和硬度。同时对道集模型进行AVO分析处理,总结出不同流体饱和度情况下的地震响应特征;将分析结果与实际井旁道地震资料对比,从而建立可靠的储集层性质与地震响应特征的对应关系,为从地震资料中提取储集层参数提供了依据。利用Biot-gassman流体替换模型,分别求得模型在含油砂岩、含气砂岩和含水砂岩时的对应响应,即获得了多个点的截距-梯度交汇图。然后利用用AVO流体反演定量的得到流体的可能性分布,根据这些叠前属性再结合叠后反演的多个属性,综合评定了该区的气藏分布情况,通过已钻遇的井证实了预测的可靠性,该方法充分的应用了地震的叠前信息,解决了叠后反演所不能解决的流体分布预测问题,在砂体范围内找含流体可能性最大的砂,从而降低了勘探风险。     

非参数回归法在孔隙度参数预测中的应用

目前有很多利用地震属性进行储层参数预测的方法,多数方法都是针对一种特定的参数模型而制定的统计方法,即只有在一定的参数模型中才能使用的统计方法.实际应用中,由于地震属性与储层参数之间的关系十分复杂,事先无法给出合适的、具体的参数模型,使用参数模型就有可能产生较大误差.本文简要介绍了非参数回归预测方法的基本原理和方法特点,对如何分析和选取具有明确物理意义的、反映储层参数横向变化较为敏感的、与储层参数关系较为密切的地震属性进行了讨论.用非参数回归法对大港唐家河工区进行了孔隙度参数的平面分布预测,表明该方法在储层参数预测技术以及油藏描述中将会有广阔的应用前景.     

非常规油气藏地质建模与数值模拟一体化技术研究

非常规油气藏地质建模与数值模拟是通过对地质、地震、油藏综合研究,建立储层精细地质模型。首先建立构造模型,同时运用地震反演的成果结合单井岩相数据建立岩相模型,利用钻井泥浆漏失、油气田和单井的实际生产情况和微地震监测,描述油气藏内发育的天然裂缝和人工压裂裂缝,最终利用岩相模型控制建立孔隙度和饱和度等属性模型,为数值模拟提供能够反映与实际地质情况相符的三维油气藏粗化地质模型。在此模型的基础上,进行生产历史拟合、产量预测,并制定合理的开发方案。     

储层预测技术在苏里格气田开发评价中的应用

针对苏里格气田盒8岩性气藏储层横向变化大、非均质性强、储层预测难度大的特点,利用二维地震资料,通过AVO远近道集叠加振幅分析、波阻抗反演和属性分析方法,进行盒8储层岩性及含气性综合预测。根据预测结果优选的开发评价井钻井结果良好,对低渗透砂岩储层岩性及含气性预测,能够进一步提高储层含气性预测的可靠性和开发评价井的预测成功率。      

Sedimentary Microfacies and Porosity Modeling of Deep-Water Sandy Debris Flows by Combining Sedimentary Patterns with Seismic Data: An Example from Unit I of Gas Field A, South China Sea

Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three-dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square (RMS) amplitude performed the best. The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified: debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well-logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies-controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single-well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.     

碳酸盐岩礁滩油气储层地震预测方法探讨

储层结构和孔隙流体预测是目前碳酸盐岩礁滩油气储层地震预测的重点和难点。这里从流体敏感性参数的选择和基于储层结构模拟的孔隙度预测二方面。研究了碳酸盐岩礁滩油气储层的流体识别问题。其中,基于测井资料统计分析和／或岩石物理岩样测试的参数交会图的制作,是优选烃类敏感参数的基础。在单一敏感参数的基础上,构组复合型的流体识别因子,能获得更好的流体识别效果。储层孔隙度预测是这样实现的：首先,对Gassman流体替换方程通过引入近似关系βp-βs≈βp进行简化;然后引入Eshelby—Walsh储层结构参数以获得直接计算孔隙度的表达式;最后,根据弹性反演得到的纵波、横渡阻抗（或纵波、横波速度）等参数,计算得到孔隙度及孔隙流体预测剖面。经实际地震资料的流体预测结果显示,新方法比常规方法预测的精度高。     

多属性反演技术在储层预测研究中的应用——以珠江口盆地陆丰A区中新统珠江组储层为例

珠江口盆地陆丰A区中新统珠江组中-下段2370层是该地区主力油层之一, 但储层厚度差异大、分布不清, 严重制约了区域勘探部署.本文综合地震、测井和地质资料, 分析得出纵波阻抗和V_P/V_S为研究区识别岩性的敏感参数, 并通过叠前同步反演技术刻画出研究区2370层砂岩分布特征, 整体呈北厚南薄的展布.以地震振幅、分频属性及叠前反演属性为基础, 利用多属性反演技术分析统计了各属性与孔隙度之间的关系, 优选出密度、V_P/V_S、地震导数、LR、V_P、25~30Hz地震分频6种属性为储层物性识别参数, 综合预测研究区2370层储层滨岸砂坝砂体孔隙度发育特征.多属性反演结果表明, 在研究区东北部、西部, 厚层的滩坝储集砂体的孔隙度发育, 普遍在18%以上; 而在中部和东部, 受滩间、坝间泥岩与滩坝砂的叠合区影响呈条带状分布, 孔隙度为10%~18%.预测结果与同期钻井的钻后结果基本一致, 预测成功率在75%以上.多属性反演技术在研究区的储层预测中表现出良好的有效性和实用性, 对类似地区的储层预测也具有借鉴意义.     

Bayesian Gaussian Mixture Linear Inversion for Geophysical Inverse Problems

A Bayesian linear inversion methodology based on Gaussian mixture models and its application to geophysical inverse problems are presented in this paper. The proposed inverse method is based on a Bayesian approach under the assumptions of a Gaussian mixture random field for the prior model and a Gaussian linear likelihood function. The model for the latent discrete variable is defined to be a stationary first-order Markov chain. In this approach, a recursive exact solution to an approximation of the posterior distribution of the inverse problem is proposed. A Markov chain Monte Carlo algorithm can be used to efficiently simulate realizations from the correct posterior model. Two inversion studies based on real well log data are presented, and the main results are the posterior distributions of the reservoir properties of interest, the corresponding predictions and prediction intervals, and a set of conditional realizations. The first application is a seismic inversion study for the prediction of lithological facies, P- and S-impedance, where an improvement of 30% in the root-mean-square error of the predictions compared to the traditional Gaussian inversion is obtained. The second application is a rock physics inversion study for the prediction of lithological facies, porosity, and clay volume, where predictions slightly improve compared to the Gaussian inversion approach.     

基于分频融合地震属性的曲流带预测与点坝识别:以渤海湾盆地埕岛油田馆陶组为例*

埕岛油田馆陶组发育典型的高弯度曲流河沉积。通过分频融合地震属性提取与优选、储层构型模式指导、动态响应特征约束等手段,对研究区海上稀井网条件下曲流带和点坝进行精细刻画。研究表明: (1)分频融合地震属性的储层预测方法提高了曲流带的预测精度,振幅类属性与砂体厚度相关性最好,以最大峰值振幅为最佳,相比原始地震属性,分频融合得到的最大峰值振幅属性更好地刻画了曲流带砂体边界与砂体厚度分布;(2)复合正韵律、砂体厚度大、紧邻废弃河道分布为点坝三大重要识别标志,与此相对应,点坝表现为高最大峰值振幅、高反演属性的地震响应特征,而废弃河道呈弯月状低振幅属性、“顶平底凸”的低反演属性特征;(3)在应用地震属性确定点坝位置的基础上,通过经验公式推算点坝跨度,可认知研究区点坝的规模,有效指导地下点坝识别,动态信息的约束也为点坝识别提供了依据。井震结合的曲流带及点坝识别方法可为相似油田构型分析提供借鉴。     

塔河油田石炭系储层横向预测

塔河油田石炭系储层总体上为一套浅海－滨海相碎屑岩沉积层系，储层段具有砂泥岩交互、上部含油，下部含水的纵向分布特征。即以致密层段为封隔，纵向上发育多个油气水小系统。根据这一特征，在储层精细标定基础上，进行多井约束地震地层反演和孔隙度约束反演，刻划储层的空间展布特征，表征储层内部物性变化，预测有利储层分布范围；通过三维地震相分析、三维相干体、非线性（突变）油气预测和吸收系数油气预测等方法，在有利的储层分布区内，识别油气高产富集带。     

Conditioning Fractal (fBm/fGn) Porosity and Permeability Fields to Multiwell Pressure Data

Stochastic fractal (fGn and fBm) porosity and permeability fields are conditioned to given variogram, static (or hard), and multiwell pressure data within a Bayesian estimation framework. Because fGn distributions are normal/second-order stationary, it is shown that the Bayesian estimation methods based on the assumption of normal/second-order stationary distributions can be directly used to generate fGn porosity/permeability fields conditional to pressure data. However, because fBm is not second-order stationary, it is shown that such Bayesian estimation methods can be used with implementation of a pseudocovariance approach to generate fBm porosity/permeability fields conditional to multiwell pressure data. In addition, we provide methods to generate unconditional realizations of fBm/fGn fields honoring all variogram parameters. These unconditional realizations can then be conditioned to hard and pressure data observed at wells by using the randomized maximum likelihood method. Synthetic examples generated from one-, two-, and three-dimensional single-phase flow simulators are used to show the applicability of our methodology for generating realizations of fBm/fGn porosity and permeability fields conditioned to well-test pressure data and evaluating the uncertainty in reservoir performance predictions appropriately using these history-matched realizations.     

普光气田礁滩相复杂孔隙类型储集层渗透率地震预测方法*

礁滩相碳酸盐岩储集层孔隙类型多样,孔—渗关系复杂,由于缺少相应的岩石物理模型,储集层渗透率地震预测一直是一个难题。作者以普光气田为例,通过分析复杂孔隙类型对储集层渗透率的控制作用及不同孔隙类型储集层在常规测井和声学测井上的响应特征,引入反映孔隙形态和连通性的 “孔构参数”表征相似孔隙度样品的岩石物理参数变化;进而建立基于孔构参数的孔隙类型判别标准和不同孔隙类型储集层孔—渗关系模型,对储集层测井渗透率进行精细解释和评价;并通过分析不同孔隙类型储集层的弹性参数与孔构参数的关系,建立起基于孔构参数的岩石物理量版;采用叠前统计学反演方法首先开展孔隙度、孔构参数的地震反演,进一步开展基于孔隙度和孔构参数的渗透率地震反演,实现了复杂孔隙类型储集层渗透率的定量预测。     

Quantitative characterization of shale gas reservoir properties based on BiLSTM with attention mechanism

Evaluating the potential of shale gas reservoirs is inseparable from reservoir properties prediction. Accurate characterization of total organic carbon, porosity and permeability is necessary to understand shale gas reservoirs. Seismic data can help to estimate these parameters in the area crossing-wells. We develop an improved deep learning method to achieve shale gas reservoir properties estimation. The relationship between elastic attributes and reservoir properties is built up by training a deep bidirectional long short-term memory network, which is suitable for time/depth sequence prediction, on the logging and core data. Except some commonly used technologies, such as layer normalization and dropout, we also introduce attention mechanism to further enhance the prediction accuracy. Besides, we propose to carry on the normal scores transform on the input features, which aims to make the relationship between inputs and targets clear and easy to learn. During the training process, we construct quantile loss function, then use Adam algorithm to optimize the network. Not only the characterization results, but also the confidence interval can be output that is meaningful for uncertainty analysis. The well experiment indicates that the method is promising for reducing prediction errors when training samples are insufficient. After analyzing in wells, the established model is acted upon seismic inverted elastic attributes to characterize shale gas reservoirs in the whole studied area. The estimation results coincide well with the actual development results, showing the feasibility of the novel method on the characterization for shale gas reservoirs.     

PP-PS联合反演含气性预测方法及应用

讨论了利用PP波和PS转换横波进行叠前联合反演的理论和实现方法,结合某油田实际多波地震资料,在叠前联合反演的基础上,对深层火成岩气藏进行了含气性预测。经钻井资料验证,根据联合反演资料预测的气藏结果与井资料吻合较好。实践证明,纵横波叠前联合反演可以避免单纯使用纵波信息进行弹性反演而造成的岩石弹性参数的多解性,提高气藏预测的准确性。     

Using time-lapse 4D seismic to monitor saturation changes in carbonate reservoirs

In the past ten years, time-lapse (4D) seismic has evolved as a standard way of monitoring reservoir performance. The method is now being used as good reservoir management practice to provide evidence of saturation changes within the reservoir at field scale. 4D provides a new piece of data describing the dynamic behavior of the reservoir fluids between the wells, often limited to small scale monitoring at the borehole scale. Thus, it provides sophisticated techniques for reservoir monitoring and management relying on the integration of geological models, static and dynamic properties of the reservoir rock, and detailed production and pressure field data.While 4D seismic data has been very successful in monitoring hydrocarbon production from clastic reservoirs, this work has focused on implementing 4D time lapse to monitor saturation changes in carbonate reservoirs and it’s capability to be used as enhanced oil recovery (EOR) tool that can help in enhancing the recovery factor for the filed and help to locate new drilling to sweep more oil out of the reservoir and locate the by-pass oil.The principal goal of this research was to detect the maximum change in seismic attributes (amplitude, acoustic impedance, travel time) that could occur as a result of oil production, water and gas injection in carbonate reservoirs by using time-lapse 4D seismic.     

基于地震信息的歧口18-1稀井网区储层预测

针对歧口18-1区块沙河街组扇三角洲储层砂体横向变化快的地质特征和稀井网的资料条件,应用工区的三维地震资料,采用地震属性分析、测井约束反演等方法,分层次开展歧口18-1沙河街组储层砂体空间分布的预测,确定了区块优质储层的分布规律。通过提取各种地震属性,在单井地震属性岩性标定的基础上,优选均方根振幅属性作为参数,确定了歧口18-1区块沙河街组扇三角洲有利相带的空间分布;据此,在扇三角洲内部应用测井约束反演技术预测优质储层的分布规律及其内部结构,为断块有利勘探目标的确定和开发方案的制定提供了地质依据。这种精度逐级提高的层次性储层预测技术,尤其适合稀井网、大井距条件下的海上油田优质储层的确定,可有效地降低开发风险。