应用随机反演改善储层表征的过程(英文)

为了适当地完成储层表征的过程,一个有效的方法就是把现场所有可以利用的信息融合成一个一致性的模型。在实际生产中实现这种融合并非简单的任务,所以有必要运用如地震反演等特殊方法。应用地震反演可以使测井数据和地震数据的有效结合成为可能,并且可以得到一个模型,该模型在预测过程中可通过流体数字模拟来验证。地震反演可以通过多种方法进行,主要分为两大类:一类是确定性方法(其代表是回归反演和约束稀疏脉冲反演),另一类是随机方法(其代表是地质统计学反演)。在本次研究中,通过随机反演结果和确定性反演结果的对比展示了随机反演是如何改进储层表征过程的。事实上,随机反演,可以运用较高的采样率(和储层模型的网格大小相接近),来产生一个更可靠的模型。随机反演的另一个好处就是随机方法可产生一些基本的统计测量值来改进解释精度,并且在储层表征过程中能生成大量的实现,从而使储层模型的不确定性研究成为可能。     

油气储层相控随机建模技术的约束方法

提出根据相控随机建模技术描述油气储层特征,并用已知地质数据和沉积微相的研究成果,如平面展布方向、宽厚比等来指导相控建模,文章提出“多层分级控制,同级套合管理”的相控随机建模策略,并将相序、概率、定量知识库或变差函数三个方面相结合来表征油气储层的非均质特性。具体则是从沉积形成与演化的成因角度指导沉积储层随机建模的过程,应用多参数协同、分层次约束的方法,运用沉积相带的平面展布和垂向演化来控制建模的结果,并用宝力格油田的实际地质和钻井数据验证优选得到的多个模型实现。结果表明:优选模型真实地反映了地下油气储层的非均质特性和连通性展布特征,并经新钻井数据验证确实有效。     

随机反演在文昌A油田储层非均质性研究中的应用

地震反演是油气储层预测的关键技术,该技术可以将地震信息转换成具有直接地质意义的油气藏参数信息,因而在地质研究中发挥着重要作用.叠后地震随机反演方法兼顾了地震数据的横向分辨率与测井数据的纵向分辨率,更是在油气藏描述方面具有显著优势.这里以文昌A油田ZJ2-1油组储层为研究对象,在分析储层岩石物理特征的基础上,选取多井平均子波,建立低频模型,利用约束稀疏脉冲反演来确定水平变差函数,通过测井数据的分析确定垂直变差函数,从而进行地震随机反演,在储层的非均质性研究中取得了较好的效果.     

综合应用测井及地震资料建立复杂断块地质模型

针对滩海油田具有完钻井少,以地震资料为主的特点,采用井震结合的方法进行地质建模。复杂断块油藏具有许多内部断层,充分利用地震解释层位和断层成果建立合理的构造模型。采用“多步建模”的思路,选用序贯高斯同位协同模拟算法建立储层参数模型。以测井资料为硬数据,地震反演数据为软数据,先后建立自然伽玛模型、泥质含量模型和砂泥岩相模型,然后以砂泥岩相模型为约束,建立储层参数模型。经研究表明,综合使用地震解释成果和三维地震数据体,有助于建立准确、合理的构造模型;而采用“多步建模”的思路,利用序贯高斯同位协同模拟算法,并综合测井资料、地震资料建立储层参数模型,有助于从地质的角度对储层参数模拟进行约束,提高储层随机建模精度,保证储层参数模型的准确和可靠性。     

储层随机建模方法研究进展

油气储层随机建模方法是近年来新发展起来的一门油气预测技术,它在综合了地质、地球物理信息的基础上,充分利用已知点的储层信息,根据所要预测的储层属性来选择不同的随机模型,从而实现了对油气储层的定量表征及对各种尺度非均质性的定量刻画,其最终模拟结果可以有任意多个以供油藏描述选择。这里简要介绍了目前比较常用的储层随机建模方法、原理,各方法的优缺点,以及不同随机模型的地质适用性,并针对我国储层的特点,指出储层随机建模技术的发展方向。     

密井网区辫状河道砂体精细地质建模方法研究

通过密井网资料的精细地质解剖,采用多信息集成约束储层建模的研究思路,按照如下2个步骤可以建立出适合开发中后期的精细地质模型.(1)建立高精度储层骨架模型,通过高分辨率层序地层学方法、储层定量模拟技术以及相关露头资料研究成果的借鉴以及密井网区精细储层地质知识库的建立是提高所建立砂体骨架模型横向分辨率的重要保证.纵向上的精度则通过具有一定精度的井数据以及适合的插值方法来保证.通过使用变差函数这一工具可以将上述各类信息有机地综合在一起,最终实现高精度砂体骨架模型的建立.(2)建立高精度储层参数模型,该模型的精度是建立在高精度砂体骨架模型的基础上,在严格相控约束下,利用密井网数据,选取适合的建模方法,就可以实现高精度储层参数模型建立.该研究思路和方法,充分利用了现有的各种信息,提高了井间砂体预测的精度,同时对于相似条件下稀井网地区的储层分布预测也具有很重要的理论指导意义.     

缝洞型碳酸盐岩储层多类多尺度建模方法研究:以塔河油田四区奥陶系油藏为例

碳酸盐岩缝洞型储层历经多期构造运动以及强烈的风化、剥蚀和淋滤作用,储集空间类型多样,形态极不规则且随机分布,导致储层三维空间描述困难,现有的碎屑岩储层建模方法难以直接借鉴。文中提出碳酸盐岩缝洞型储层应该按照大型洞穴、溶蚀孔洞、大尺度裂缝、小尺度裂缝的"多类多尺度建模"的基本思路。以钻井和地震识别成果作为大型洞穴确定性数据,以地震波阻抗的大型洞穴发育概率体作为井间约束数据,在垂向岩溶分带和平面古地貌分区的岩溶相控下,采用具有趋势的序贯指示模拟方法,建立大型洞穴离散分布模型;以大型洞穴分布作为"相控"约束条件,以井孔解释的溶蚀孔洞作为硬数据,采用序贯指示模拟算法,建立溶蚀孔洞随机分布模型;根据蚂蚁体地震属性自动拾取的断裂信息,人机交互补充和修正地震解释断层数据,建立确定性的大尺度裂缝离散分布模型;基于大尺度裂缝离散分布模型建立井间裂缝发育概率体,根据井孔裂缝密度、裂缝产状,结合退火模拟和基于目标的示性点过程模拟方法,建立小尺度裂缝离散分布模型。以塔河油田四区奥陶系缝洞型油藏为例,建立研究区缝洞型储集体空间展布模型,再现缝洞型储层的结构形态。     

蒙特卡罗法在煤层气资源量计算中的应用

将蒙特卡罗(Monte-Carlo)法引入煤层气资源量计算,可弥补容积法视各参数为常数的不足。重点论述了蒙特卡罗法用于煤层气资源量计算的主要算法和函数实现,包括参数选择、分布函数确定、伪随机数的产生以及对应伪随机数参数值的确定。利用上述算法自主开发了蒙特卡罗法软件。研究实例表明,在数据样本较多的情况下,该算法与容积法能保持较小误差范围且有更高的可信度。      

地震储层表征:在模拟储层的三维地震响应时考虑盖层、地震勘探布局和储层物性的敏感度(英文)

根据三维地震地质模型对地震数据进行模拟是从勘探到生产的周期内决策过程中的一个不可或缺的组成部分。虽然对于在储层内的动力过程和地震地质的模型表述已经取得很大进展,但如何从这些模型得到地震数据的精确模拟仍面临很多挑战。通常是在地球模型范围内根据物性用一维褶积方法来模拟地震数据。然而这个过程一般不考虑地震勘探布局和盖层对地震信号的影响。我们审视了为什么这些因素会制约三维地球模型的有效性,并考虑了为什么需要把盖层和地震勘探布局对三维覆盖和分辨率的影响加进模拟过程之中。我们提出了一种新方法,把建立物性模型和一种新的地震模拟技术结合起来,给出一个工作流程;利用这个流程,勘探工作者可以很快模拟出三维的PSDM数据,这些数据加进了盖层和地震勘探布局对覆盖及分辨率的影响。我们利用从远离挪威海岸的一个油田得到的数据,在考虑覆盖和分辨率效应的地震数据模拟之前,对岩石物性做了一些扰动,然后进行地震数据模拟,以此来说明如何可以用这种方法提高三维地球模型的精确性和增进我们对储层的了解。     

山西辛安岩溶泉随机模拟

本文应用三个随机模型对一个完整岩溶水系统进行模拟,其模拟结果与实测值十分接近。      

An Improved Gradual Deformation Method for Reconciling Random and Gradient Searches in Stochastic Optimizations

Constraining stochastic models of reservoir properties such as porosity and permeability can be formulated as an optimization problem. While an optimization based on random search methods preserves the spatial variability of the stochastic model, it is prohibitively computer intensive. In contrast, gradient search methods may be very efficient but it does not preserve the spatial variability of the stochastic model. The gradual deformation method allows for modifying a reservoir model (i.e., realization of the stochastic model) from a small number of parameters while preserving its spatial variability. It can be considered as a first step towards the merger of random and gradient search methods. The gradual deformation method yields chains of reservoir models that can be investigated successively to identify an optimal reservoir model. The investigation of each chain is based on gradient computations, but the building of chains of reservoir models is random. In this paper, we propose an algorithm that further improves the efficiency of the gradual deformation method. Contrary to the previous gradual deformation method, we also use gradient information to build chains of reservoir models. The idea is to combine the initial reservoir model or the previously optimized reservoir model with a compound reservoir model. This compound model is a linear combination of a set of independent reservoir models. The combination coefficients are calculated so that the search direction from the initial model is as close as possible to the gradient search direction. This new gradual deformation scheme allows us for reducing the number of optimization parameters while selecting an optimal search direction. The numerical example compares the performance of the new gradual deformation scheme with that of the traditional one.     

Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

This paper proposes a novel history-matching method where reservoir structure is inverted from dynamic fluid flow response. The proposed workflow consists of searching for models that match production history from a large set of prior structural model realizations. This prior set represents the reservoir structural uncertainty because of interpretation uncertainty on seismic sections. To make such a search effective, we introduce a parameter space defined with a “similarity distance” for accommodating this large set of realizations. The inverse solutions are found using a stochastic search method. Realistic reservoir examples are presented to prove the applicability of the proposed method.     

Multiscale Parameterization of Petrophysical Properties for Efficient History-Matching

The prediction of fluid flows within hydrocarbon reservoirs requires the characterization of petrophysical properties. Such characterization is performed on the basis of geostatistics and history-matching; in short, a reservoir model is first randomly drawn, and then sequentially adjusted until it reproduces the available dynamic data. Two main concerns typical of the problem under consideration are the heterogeneity of rocks occurring at all scales and the use of data of distinct resolution levels. Therefore, referring to sequential Gaussian simulation, this paper proposes a new stochastic simulation method able to handle several scales for both continuous or discrete random fields. This method adds flexibility to history-matching as it boils down to the multiscale parameterization of reservoir models. In other words, reservoir models can be updated at either coarse or fine scales, or both. Parameterization adapts to the available data; the coarser the scale targeted, the smaller the number of unknown parameters, and the more efficient the history-matching process. This paper focuses on the use of variational optimization techniques driven by the gradual deformation method to vary reservoir models. Other data assimilation methods and perturbation processes could have been envisioned as well. Last, a numerical application case is presented in order to highlight the advantages of the proposed method for conditioning permeability models to dynamic data. For simplicity, we focus on two-scale processes. The coarse scale describes the variations in the trend while the fine scale characterizes local variations around the trend. The relationships between data resolution and parameterization are investigated.     

Theoretical connections between optimization algorithms based on an approximate gradient

Performing a line search method in the direction given by the simplex gradient is a well-known method in the mathematical optimization community. For reservoir engineering optimization problems, both a modification of the simultaneous perturbation stochastic approximation (SPSA) and ensemble-based optimization (EnOpt) have recently been applied for estimating optimal well controls in the production optimization step of closed-loop reservoir management. The modified SPSA algorithm has also been applied to assisted history-matching problems. A recent comparison of the performance of EnOpt and a SPSA-type algorithm (G-SPSA) for a set of production optimization test problems showed that the two algorithms resulted in similar estimates of the optimal net-present-value and required roughly the same amount of computational time to achieve these estimates. Here, we show that, theoretically, this result is not surprising. In fact, we show that both the simplex, preconditioned simplex, and EnOpt algorithms can be derived directly from a modified SPSA-type algorithm where the preconditioned simplex algorithm is presented for the first time in this paper. We also show that the expectation of all these preconditioned stochastic gradients is a first-order approximation of the preconditioning covariance matrix times the true gradient or a covariance matrix squared times the true gradient.     

History matching on the Imperial College fault model using parallel tempering

The history-matching inverse problem from petroleum engineering is analysed using the Imperial College fault model. This fault model produces a challenging inverse problem and is designed to show some of the problems which can occur whilst performing history-matching calculations on complicated geologies. It is shown that there can be multiple distinct geologies which match the history data. Furthermore, it is shown that the maximum-a-posteriori estimate does not correspond to the true geology in some cases. Both of these statements are corroborated via numerical examples where the parameter spaces are ?, ?³, ?⁷ and ?¹³. In addition, it is shown that the number of matches which agree with the data increases with dimension for these examples. It is also shown that the different matches can result in different reservoir management decision which, if incorrectly taken, would incur substantial financial penalties. All of these analyses are performed in a systematic manner, where it is shown that the standard algorithms can give a misleading answer. The history-matching problem is written as a minimisation problem, and it is shown that knowledge of all of the local minima is required. This presents significant computational issues as the resulting objective function is highly nonlinear, expensive to evaluate and multimodal. Previously used algorithms have been proved to be inadequate. Parallel tempering is a method which, if run for long enough, can find all the local minima. However, as the objective is expensive, a number of algorithm modifications had to be used to ensure convergence within a reasonable time. This new information is outlined in the paper. The algorithm as implemented produced results and new insights into this problem which were not suspected before. The results produced by this algorithm for the multimodal history-matching problem are superior to all other results of which we are aware. However, a considered amount of computation time was used within this paper, so this result does not infer that the algorithm cannot be improved upon. This algorithm not only produces good results but can be applied to all other history-matching problems. We have shown that this method provides a robust route of finding multiple local optima/solutions to the inverse problem, which is of considerable benefit to the petroleum industry. Furthermore, it is an entirely parallel algorithm which is becoming computationally feasible for other history-matching problems.     

Gaussian Processes for history-matching: application to an unconventional gas reservoir

The process of reservoir history-matching is a costly task. Many available history-matching algorithms either fail to perform such a task or they require a large number of simulation runs. To overcome such struggles, we apply the Gaussian Process (GP) modeling technique to approximate the costly objective functions and to expedite finding the global optima. A GP model is a proxy, which is employed to model the input-output relationships by assuming a multi-Gaussian distribution on the output values. An infill criterion is used in conjunction with a GP model to help sequentially add the samples with potentially lower outputs. The IC fault model is used to compare the efficiency of GP-based optimization method with other typical optimization methods for minimizing the objective function. In this paper, we present the applicability of using a GP modeling approach for reservoir history-matching problems, which is exemplified by numerical analysis of production data from a horizontal multi-stage fractured tight gas condensate well. The results for the case that is studied here show a quick convergence to the lowest objective values in less than 100 simulations for this 20-dimensional problem. This amounts to an almost 10 times faster performance compared to the Differential Evolution (DE) algorithm that is also known to be a powerful optimization technique. The sensitivities are conducted to explain the performance of the GP-based optimization technique with various correlation functions.     

Hybridization of the probability perturbation method with gradient information

Geostatistically based history-matching methods make it possible to devise history-matching strategies that will honor geologic knowledge about the reservoir. However, the performance of these methods is known to be impeded by slow convergence rates resulting from the stochastic nature of the algorithm. It is the purpose of this paper to introduce a method that integrates qualitative gradient information into the probability perturbation method to improve convergence. The potential of the proposed method is demonstrated on a synthetic history-matching example. The results indicate that inclusion of qualitative gradient information improves the performance of the probability perturbation method.     

The FFT Moving Average (FFT-MA) Generator: An Efficient Numerical Method for Generating and Conditioning Gaussian Simulations

A fast Fourier transform (FFT) moving average (FFT-MA) method for generating Gaussian stochastic processes is derived. Using discrete Fourier transforms makes the calculations easy and fast so that large random fields can be produced. On the other hand, the basic moving average frame allows us to uncouple the random numbers from the structural parameters (mean, variance, correlation length, ... ), but also to draw the randomness components in spatial domain. Such features impart great flexibility to the FFT-MA generator. For instance, changing only the random numbers gives distinct realizations all having the same covariance function. Similarly, several realizations can be built from the same random number set, but from different structural parameters. Integrating the FFT-MA generator into an optimization procedure provides a tool theoretically capable to determine the random numbers identifying the Gaussian field as well as the structural parameters from dynamic data. Moreover, all or only some of the random numbers can be perturbed so that realizations produced using the FFT-MA generator can be locally updated through an optimization process.     

水库调洪演算的随机数学模型

以介于微分方程和概率论之间的边缘数学分支随机微分方程的数学模型,对水库调洪过程中的随机现象和规律进行数学描述和分析,试图全面正确地综合各种不确定性因素对库水位随机过程的影响.根据水库蓄洪量具有Wiener过程特性的分析,推导了带有随机输入项和随机初始条件的调洪演算Ito方程.在此基础上,运用Fokker-Planck向前方程,求介了调洪过程库水位的概率密度分布.计算成果表明,运用随机微分方程进行水库的调洪演算,有利于正确分析水库调洪的随机过程和进一步开展水库泄洪风险分析.