无井条件下建立碎屑岩储层地震地质模型研究

常规的储层建模以井数据为基础,建立孔隙度、渗透率和含油饱和度等储层属性参数模型,并通过油田开发生产数据进行拟合,得到最佳地质模型.在南海西北部深水区无井控制的情况下,针对中央峡谷浊积水道储层,采用确定性建模与连续型随机建模相结合的方法,建立碎屑岩储层地震地质模型,应用地震正演模拟和相似性分析方法,确定浊积水道砂体最佳模拟参数.文章首先分析了碎屑岩储层特征,应用连续型随机建模的方法模拟储层空间分布.然后分析实际地震、地质资料,应用层序地层学思想,对研究区划分沉积体系域,建立层序地层格架;基于波阻抗和多种地震属性,采用人机交互的方法对浊积水道储层框架结构进行精细解释,建立储层结构模型;将储层结构模型与砂泥互层随机介质进行"交"、"并"运算,利用连续型随机模拟方法对储层内部介质进行精细刻画,建立中央峡谷浊积水道随机介质储层模型;通过对模型正演模拟、常规处理和相似性分析等建立最佳储层模型.文章研究了在无井条件下建立储层地震地质模型的方法,揭示南海西北部深水区中央峡谷浊积水道储层的空间展布和内部结构特征.     

等时地层模型控制下的砂砾岩体岩相预测

砂砾岩体是陆相断陷湖盆陡坡带广泛发育的岩性类型,亦是东营凹陷最重要的一种增储上产储层类型.地质上,该类沉积储层非均质性强,不同相带储层物性差异大,油气分布相控特征明显;地震上,砂砾岩体内幕反射杂乱、空白,成层性差,空间上识别可靠的等时地层界面困难,导致后期有利相带、储层描述精度低,失去了沉积学理论基础和方法支持.本次研究创新在井震频谱分析、Fischer曲线和耦合系数三因子联合井震标定基础上,探索形成了EMD地震层序格架约束下的基于价值函数的等时地层模型建立方法,客观快速的建立了研究区三维相对地质年代模型,极大提高了砂砾岩体层序划分精度,并在该模型约束下通过门槛值约束敏感属性融合法对影响砂砾岩体非均质性的岩相进行了预测,实现了油气优势相带精细刻画,有效指导了砂砾岩体井位部署及后期开发方案编制.     

基于层序地层学的地质统计学反演

地质统计学反演具有较高的空间分辨率,在薄储层预测中应用广泛,其反演精度受地质框架模型、纵/横向变程和岩性空间展布趋势的影响较大.传统地质框架模型、变程及岩性空间展布趋势的选取主要通过参数测试来实现,效率低,反演精度不高,并不能完全反应储层空间变化特征,尤其是对于储层横向变化较快的曲流河沉积,其储层叠置特征和空间展布特征刻画难度更大.为此,本文以渤海W油田为例,将层序地层学理论引入地质统计学反演中,以层序地层学理论为指导,建立精细的层序地层格架,并构建相应的地质框架模型,借助储层沉积学分析,进而开展层序地层格架约束下变程及地质框架模型约束下岩性空间展布趋势选取方式及其对反演精度影响的研究.研究认为,基于层序地层学选取反演参数效率高、精度高,所选参数地质含义明确;反演结果空间分辨率高,与井上信息吻合度更高,储层内部砂体的叠置特征及空间展布特征更加清晰,从而为油田开发提供了高品质数据支撑.     

Wheeler域储层地震预测技术研究

利用常规地震数据进行层序划分、沉积相识别、沉积演化分析及储层预测,因受现今构造趋势的影响,具有较强的多解性.Wheeler域地震资料因具有等时性,地层旋回性清楚、展布范围明了,井震标定后便于层序及体系域划分,易于识别砂体,在进行地层的沉积演化过程分析,可以在相控约束下分析砂体的分布特征、成因,评价有利储层,减少多解性.本文通过正演探讨了同一沉积模式,不同地震频率下的地震成像,研究了不同沉积模式下的Wheeler转换方法,实现了地震资料从常规时间域转换到Wheeler域.通过Wheeler域井震标定,建立起地震地质的联系;拓展性的提出了在地震最小研究单元内,在双域(常规时间域和Wheeler域)进行解释和综合研究的技术流程;建立了基于地震沉积学的储层地震预测技术.并在准噶尔盆地西缘白垩系储层研究进行了应用.经钻井证实,这种基于地震沉积学的Wheeler域储层地震预测技术比较准确,效果良好,值得完善和推广.     

小波变换在识别储层流体性质中的应用

根据小波变换的特点提出一种识别储层流体的新方法. 本文假设测井所得的信号为地层微观孔隙结构、地层流体性质及岩性和岩相等各部分的综合贡献之和，不同的储层中小波分析的能量谱特征不同，因而通过对已知储层模型进行小波能量谱分析，可得到各种储层的特征能量谱，根据这些储层特征能量谱就可以对待划分的储层段进行储层流体性质判别.     

定量地震地貌学研究进展

定量地震地貌学以三维地震数据为基础,通过对地貌特征参数的定量分析来了解盆地历史、沉积过程和填充构架,在沉积体系定量分析和储层表征方面为一新方向.其主要研究内容包括系统地质研究、地震属性分析、地貌特征定量分析、沉积单元交汇分析和沉积体系影响因素研究等五方面,核心为多数据融合及沉积单元形态定量数据的利用.定量地震地貌学在冲积河道、三角洲、浊积水道、水下扇储层特征及结构研究方面已取得较大突破,在地层沉积定量研究、储集体规模预测方面具有较大优势和前景.     

塔河油田卡4段地层相控储层预测

塔河油田石炭系卡4段储层灰岩含量较高,造成地震预测多解性严重,文章提出相控储层预测的思路和研究方法：提出以区域等时地层格架为约束的局部区域和同一沉积体系的准层序级别的砂体等时对比新思路,以Wheeler域年代地层切片技术为手段建立精细等时层序地层格架,进而开展准层序级别的变时窗属性提取分析,采用振幅和伪熵属性表征卡4段沉积微相展布,以Wheeler反变换技术实现了实践意义上的相控储层预测理念,雕刻了有利储层空间形态,为该区岩性圈闭的发现提供了参考.     

曲折度电阻率模型在大庆G地区低渗储层评价中的应用

大庆G地区P油层孔渗较低、泥质含量较高,属于中低孔特低渗、高含泥储层,利用压汞实验资料对该区储层特征进行分析,研究结果表明该区储层分选性较差、孔喉不均、微观孔隙结构复杂.复杂的孔隙结构会引起导电路径的复杂化,影响储层的电性特征.经典阿尔奇公式由于没有考虑孔隙结构变化对岩石电阻率的影响,不能准确描述孔隙结构复杂的低孔、渗储层的导电规律,因而建立一种适合于低孔、渗复杂储层的电阻率模型成为当务之急.针对P油层具有更复杂的孔隙结构,本文利用曲折度岩石体积物理模型建立了岩石孔隙结构和岩石电阻率之间的联系及关系式.该式表明,孔隙度一定时,孔隙通道越弯曲,孔隙结构越复杂,孔隙曲折度越大,岩石的地层因素值也越大.为了使该关系式实用化,本文提出在曲折度电阻率模型中引入结构系数,解决孔隙曲折度的表征问题,实现对岩石孔隙结构复杂程度的定量计算,从而建立了新的地层因素公式.利用压汞和岩电等实验数据对新的地层因素公式进行验证,证实地层因素和结构系数存在着幂次关系,岩石的孔隙结构越复杂,结构系数越大,地层因素越高.针对研究区泥质含量较高、地层矿化度较低的特点,选用能够描述饱含水地层电阻率与地层水电阻率之间弯曲关系的DOLL方程作为饱和度基本方程,并将新的地层因素公式引入DOLL方程中,建立了该区低孔、渗泥质砂岩饱和度方程.实际处理结果表明,该导电模型在一定程度上考虑了孔隙结构变化对电阻率的影响,计算的含水饱和度平均相对误差小于8％,精度满足低孔、渗储层开发生产的需要.     

川中磨溪地区嘉二段碳酸盐岩储层物性下限值确定及控制因素分析

川中磨溪地区嘉二段碳酸盐岩储集空间类型以孔隙性储层为主,储层孔喉配置关系较为复杂,储层非均质性强,给物性下限值的确定带来一定困难.基于嘉二段孔隙结构差异,分为两部分(嘉二1与2A小层、嘉二2B和2C小层)对研究区嘉二段碳酸盐岩储层物性下限值进行确定,通过前人对沉积、储层等的研究及现有岩心分析与动态生产资料确定储层物性下限值.结合物性下限值的研究结果探讨沉积与成岩等作用对储层的控制,研究区储层有效性主要受沉积微相和成岩作用的控制,沉积严格控制着储层的分布,成岩影响着原生孔隙的保留程度和次生孔隙的发育程度.     

微弱方波信号检测模型混沌解的判定

薄油气储层的识别和解释中存在极其微弱有效信号，对这些有效信号进行混沌检测的先决条件是检测模型存在混沌区域。本文以微弱方波有效信号为例，运用Melnikov方法就混沌检测模型（Dufing方程）的混沌判据问题进行数学,证明并推导该数学模型出现混沌状态的临界值，为薄油气储层的识别和解释中的微弱信号的混沌检测提供理论依据。     

联合多点地质统计学与序贯高斯模拟的随机反演方法

岩相和储层物性参数是油藏表征的重要参数,地震反演是储层表征和油气藏勘探开发的重要手段.随机地震反演通常基于地质统计学理论,能够对不同类型的信息源进行综合,建立具有较高分辨率的储层模型,因而得到广泛关注.其中,概率扰动方法是一种高效的迭代随机反演策略,它能综合考虑多种约束信息,且只需要较少的迭代次数即可获得反演结果.在概率扰动的优化反演策略中,本文有效的联合多点地质统计学与序贯高斯模拟,并结合统计岩石物理理论实现随机反演.首先,通过多点地质统计学随机模拟,获得一系列等可能的岩相模型,扰动更新初始岩相模型后利用相控序贯高斯模拟建立多个储层物性参数模型;然后通过统计岩石物理理论,计算相应的弹性参数;最后,正演得到合成地震记录并与实际地震数据对比,通过概率扰动方法进行迭代,直到获得满足给定误差要求的反演结果.利用多点地质统计学,能够更好地表征储层空间特征.相控序贯高斯模拟的应用,能够有效反映不同岩相中储层物性参数的分布.提出的方法可在较少的迭代次数内同时获得具有较高分辨率的岩相和物性参数反演结果,模型测试和实际数据应用验证了方法的可行性和有效性.     

基于三维层状介质模型地震层析成像正演网格研究

地震层析成像技术是研究地层速度结构的一个重要手段,该技术是利用震源到接收点的地震波走时或波形的观测数据,结合建立的数学物理模型来重建地层速度结构。而层析成像正演模拟的精度将直接影响反演出的速度与地层真实速度的拟合度,因此找出一种精度较高的层析成像正演模拟算法是非常重要的。本文针对三维层状介质,通过网格化模型,采用最小走时射线追踪方法展开层析成像正演研究,分别采用三角形网格、矩形网格和六边形网格进行模型参数化,并对比分析各自正演结果的精度,总结出针对三维层状介质模型的最佳网格划分方式。      

The nonlinear oil–water two-phase flow behavior for a horizontal well in triple media carbonate reservoir

Carbonate reservoir is one of the important reservoirs in the world. Because of the characteristics of carbonate reservoir, horizontal well has become a key technology for efficiently developing carbonate reservoir. Establishing corresponding mathematical models and analyzing transient pressure behaviors of this type of well-reservoir configuration can provide a better understanding of fluid flow patterns in formation as well as estimations of important parameters. A mathematical model for a oil–water two-phase flow horizontal well in triple media carbonate reservoir by conceptualizing vugs as spherical shapes are presented in this article. A semi-analytical solution is obtained in the Laplace domain using source function theory, Laplace transformation, and superposition principle. Analysis of transient pressure responses indicates that seven characteristic flow periods of horizontal well in triple media carbonate reservoir can be identified. Parametric analysis shows that water saturation of matrix, vug and fracture system, horizontal section length, and horizontal well position can significantly influence the transient pressure responses of horizontal well in triple media carbonate reservoir. The model presented in this article can be applied to obtain important parameters pertinent to reservoir by type curve matching.     

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Developing a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), Nash–Sutcliffe efficiency coefficient (E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.     

Geostatistical facies simulation with geometric patterns of a petroleum reservoir

During exploration and pre-feasibility studies of a typical petroleum project many analyses are required to support decision making. Among them is reservoir lithofacies modeling, preferably using uncertainty assessment, which can be carried out with geostatistical simulation. The resulting multiple equally probable facies models can be used, for instance, in flow simulations. This allows assessing uncertainties in reservoir flow behavior during its production lifetime, which is useful for injector and producer well planning. Flow, among other factors, is controlled by elements that act as flow corridors and barriers. Clean sand channels and shale layers are examples of such reservoir elements that have specific geometries. Besides simulating the necessary facies, it is also important to simulate their shapes. Object-based and process-based simulations excel in geometry reproduction, while variogram-based simulations perform very well at data conditioning. Multiple-point geostatistics (MPS) combines both characteristics, consequently it was employed in this study to produce models of a real-world reservoir that are both data adherent and geologically realistic. This work aims at illustrating how subsurface information typically available in petroleum projects can be used with MPS to generate realistic reservoir models. A workflow using the SNESIM algorithm is demonstrated incorporating various sources of information. Results show that complex structures (e.g. channel networks) emerged from a simple model (e.g. single branch) and the reservoir facies models produced with MPS were judged suitable for geometry-sensitive applications such as flow simulations.     

Connectivity metrics for subsurface flow and transport

Understanding the role of connectivity for the characterization of heterogeneous porous aquifers or reservoirs is a very active and new field of research. In that framework, connectivity metrics are becoming important tools to describe a reservoir. In this paper, we provide a review of the various metrics that were proposed so far, and we classify them in four main groups. We define first the static connectivity metrics which depend only on the connectivity structure of the parameter fields (hydraulic conductivity or geological facies). By contrast, dynamic connectivity metrics are related to physical processes such as flow or transport. The dynamic metrics depend on the problem configuration and on the specific physics that is considered. Most dynamic connectivity metrics are directly expressed as a function of an upscaled physical parameter describing the overall behavior of the media. Another important distinction is that connectivity metrics can either be global or localized. The global metrics are not related to a specific location while the localized metrics relate to one or several specific points in the field. Using these metrics to characterize a given aquifer requires the possibility to measure dynamic connectivity metrics in the field, to relate them with static connectivity metrics, and to constrain models with those information. Some tools are already available for these different steps and reviewed here, but they are not yet routinely integrated in practical applications. This is why new steps should be added in hydrogeological studies to infer the connectivity structure and to better constrain the models. These steps must include specific field methodologies, interpretation techniques, and modeling tools to provide more realistic and more reliable forecasts in a broad range of applications.     

Reservoir-system simulation and optimization techniques

Reservoir operation is one of the challenging problems for water resources planners and managers. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. This paper discusses an overview of simulation and optimization modeling methods utilized in resolving critical issues with regard to reservoir systems. In designing a highly efficient as well as effective dam and reservoir operational system, reservoir simulation constitutes one of the most important steps to be considered. Reservoirs with well-functional and reliable optimization models require very accurate simulations. However, the nonlinearity of natural physical processes causes a major problem in determining the simulation of the reservoir’s parameters (elevation, surface-area, storage). Optimization techniques have shown high efficiency when used with simulation modeling and the combination of the two methods had given the best results in the reservoir management. The principal concern of this review study is to critically evaluate and analyze simulation, optimization and combined simulation–optimization modeling approach and present an overview of their utility in previous studies. Inferences and suggestions which may assist in improving quality of this overview in the future are provided. These will also enable future researchers, system analysts and managers to achieve more precise optimal operational system.     

Recession curve estimation for storm event separations

An accurate recession curve model is important for separating individual flow events, which is especially difficult over catchments in regions with a maritime climate where frequent rainfall events cause the flows to rise before they reach the baseflow level. The traditional recession curve equations are based on static linear and nonlinear reservoir models. These models work quite well for ground water dominated recession curves, but not so well when the direct runoff is significant in the recession part. In this study, a new modelling methodology is explored based on self-adaptive parameters in the linear and nonlinear reservoir models. It has been found that the adaptive forms performed better than the static ones, especially when a window for the adaptive parameter estimation is properly selected. While the nonlinear adaptive model had better accuracy over the linear one, it could become unstable if its window is too narrow, indicating that more research work is needed to find an useful pattern for the window size. A comparison between the recession curve models and PDM model (a rainfall-runoff model) has shown that they agreed quite well in most winter events, but less so in the summer.     

A THREE-DIMENSIONAL MODEL TO COMPUTE LAND SUBSIDENCE

ABSTRACT

An attempt is made to improve the existing analytical three-dimensional models to predict land subsidence due to the extraction of fluids from natural reservoirs. The elastic model proposed by McCann and Wilts (1951) and the poro-elastic one introduced by Geertsma (1957, 1966) are presented first. The general equations of recent poro-elastic theory are mentioned. Then follows a new, more generalized, three-dimensional approach from which the previous models can be derived as particular cases. The contribution to the land subsidence of the depressurized oil or water bearing strata is separated from that of the surrounding strata. It is possible in this way to take into account the different physical properties of the reservoir and of the remaining medium. It is finally shown that the elastic model can be considered as a limit poro-elastic one and therefore its application appears more strictly correlated with the real condition of the sub-soil. In particular, the model by McCann and Wilts (1951) proves to be advantageous only when the depressurized reservoir is much more liable to deformation than the surrounding medium.     

The role of sensitivity analysis in hydrologic modeling

Sensitivity is a measure of the effect of change in one factor on another factor. Sensitivity analysis is potentially useful in all phases of the modeling process: model formulation, model calibration and model verification. The sensitivity of model parameters should be recognized as a special case of the above general definition. Parametric sensitivity is a vital part of most optimization techniques. However, other facets of sensitivity need to be recognized. The time-dependent nature of sensitivity should be considered in the formulation of hydrologic models. A variety of simplified hydrologic models are used to demonstrate the potential of sensitivity in all phases of the modeling process. The failure to recognize and exploit the potential of sensitivity analysis results primarily from the inadequacy of the mathematical foundations of sensitivity. A comprehensive mathematical framework of sensitivity is provided and additional research needs are identified.