基于单井敏感性局域化EnKF的油藏辅助历史拟合方法

常用的油藏自动历史拟合算法多数存在梯度计算不准确、产生伪相关性等问题,导致参数修正错误、模型反演失真。通过建立一种基于单井动态敏感性的局域化集合卡尔曼滤波（FMM-CL-EnKF）历史拟合方法,解决了传统距离截断方法处理伪相关性时与实际地层状况不匹配的问题。基于程函方程,根据地质模型静态参数场信息,快速追踪压力波从井点到地层网格的传播时间确定各单井动态最大敏感性区域,从而构建局域化矩阵。同时,结合集合卡尔曼滤波（EnKF）方法,实现数据同化方法梯度的矫正,减弱伪相关,通过逐步拟合生产动态达到更新油藏模型和获取最优估计的目的。概念算例和矿场算例的计算结果表明,FMM-CL-EnKF方法在模型集合生产动态拟合效果及反演模型参数场准确性等方面均优于标准EnKF方法。     

单一模型EnKF法更新油藏模型的探讨

近年来，人们开始利用集合卡尔曼滤波（EnKF）进行自动历史拟合，更新油藏模型。实验证明EnKF方法是一种有效的方法。EnKF方法要求一组初始实现，但是在实际应用中往往只有一个油藏或地质模型，这使得直接利用EnKF进行历史拟合，更新油藏模型比较困难。为此，提出了一种由一个模型派生出多个模型来满足EnKF基本要求的随机导航点方法，并试验在尽量少的模型条件下，利用EnKF更新油藏模型。模型实验表明，利用随机点抽样派生出多个模型，再利用EnKF可以实现单一模型更新，最终取得的模型与参考模型具有较好的一致性。     

An Automatic History Matching Module with Distributed and Parallel Computing

Abstract

The data assimilation process of adjusting variables in a reservoir simulation model to honor observations of field data is known as history matching and has been extensively studied for a few decades. However, limited success has been achieved due to the high complexity of the problem and the large computational effort required in the real fields. Successful applications of the ensemble Kalman filter (EnKF) to reservoir history matching have been reported in various publications. The EnKF is a sequential method: once new data are available, only these data are used to update all the unknown reservoir properties while previous geological information is unused directly. In this method, multiple reservoir models rather than one single model are implemented, and each model is called a member. Conventionally, the impact of each member on the updating is equally treated. Another approach is the weighted EnKF. During the updating, the method weighs the contribution of each member through the comparison between the simulation response and the measurements. Better matching performance has been found in the weighted EnKF than in the conventional EnKF. To improve computational efficiency, two-level high-performance computing for reservoir history matching process is implemented in this research, distributing ensemble members simultaneously while simulating each member in a parallel style.

An automatic history-matching module based on the weighted EnKF and high-performance computing is developed and validated through a synthetic case operating from primary, waterflooding to flooding of water alternating with gas. The study shows that the weighted EnKF improves the matching results, and the high-performance computing process significantly reduces the history matching execution time.     

Ensemble Kalman filter for automatic history matching of geologic facies

In this paper we address two fairly difficult problems. The first is the problem of matching production data (in this case, production and injection rates) by adjustment of the locations of the geologic facies boundaries. The second is the use of a Kalman filter for updating the facies locations in the reservoir model.Traditional automatic history matching tools are not widely available for reservoirs with unknown facies boundaries, largely because of the complexity of developing software for computing the sensitivity of the data to model parameters, the lack of differentiability of facies type, and the high computational cost in generating multiple reservoir models that are conditional to given data. With careful definition of variables, the use of the ensemble Kalman filter (EnKF) minimizes those difficulties. First, the gradient does not need to be computed explicitly, the coding for the EnKF algorithm is easy and adaptable to any reservoir simulator on a plug-in basis. Second, an approximation to differentiability results from the correlation of variables. Third, the ensemble Kalman filter (EnKF) method takes one simulation run per reservoir model realization, and the simulations of the reservoir models in the ensemble are ideal for multiple-processor parallel computation.We use the truncated pluri-Gaussian model to generate random facies realizations. The geostatistical model is fully specified by the threshold truncation map and the covariance models for the two Gaussian random fields. The pluri-Gaussian model is well known but not widely used, partly because of the difficulty of generating conditional realizations. In the first example, we demonstrate the application of the EnKF to the problem of generating facies realizations conditional to observations at 18 wells on a 128 × 128 grid. In the second example, realizations of facies on a 50 × 50 grid, conditional to facies observations at the wells and to production and injection rates, are generated using the EnKF. In general, we found that application of the EnKF to the problem of adjusting facies boundaries to match production data was relatively straightforward and efficient.     

基于微地震离散裂缝与自动历史拟合的数值模拟

针对复杂缝网精细刻画、表征复杂,数值模拟历史拟合繁琐、不确定性强,导致非常规储层实现精准数值模拟难度大的问题,采用微地震复杂缝网反演、离散裂缝方法缝网精细刻画、集合卡曼滤波方法(EnKF)自动拟合技术3种手段形成的综合数值模拟技术,对复杂缝网条件下的非常规储层实现精准数值模拟.结果表明:文中提出的复杂缝网反演方法与直接...     

A Weighted Ensemble Kalman Filter for Automatic History Matching

Abstract

The ensemble Kalman filter (EnKF) performs the initial sampling, forecasting, and assimilation steps for automatic history matching in the petroleum industry. It tunes multiple members sequentially and updates the statistical mean and variance of the model. Many applications have been reported in various publications. The forecasting step is implemented by running the reservoir model simulator. In the assimilation equation, the ensemble mean is calculated through equally weighting all the members. Therefore, the contribution factor to the mean from each member is the same. This paper proposes a modified assimilation equation by introducing a weighting factor for each ensemble member. Both the proposed weighted EnKF and the traditional EnKF are applied to a modified field case of a complex seventeen-layer reservoir. The performances of the weighted EnKF on production history match, forecasting, and field permeability match are better than those from the traditional EnKF. In addition, we investigate the impact of geological uncertainty in the initial ensemble generation on the final matching results. Two scenarios which have the same semivariogram as the reference field are implemented, and their results show that the initial geological information is important to the history matching performance.     

基于多次数据吸收集合平滑算法的自动油藏历史拟合研究

针对常见历史拟合方法存在计算量大、油藏参数更新异常、油藏模型修正失真等问题.采用集合平滑算法,通过引入集合卡尔曼滤波算法(EnKF)中多次迭代思路,对相同数据重复吸收,推导出多次数据吸收集合平滑算法(ES-MDA)的核心公式,并编写了自动油藏历史拟合软件.以北海布伦特油田海相砂岩油藏为例,将基于ES-MDA算法的油藏自...     

EnKF整合三维地震数据和动态数据的应用

主要介绍集合卡尔曼滤波EnKF在更新油藏储层静态参数方面的应用。根据集合卡尔曼滤波产生的必要条件,介绍了集合卡尔曼滤波整合动态数据和三维地震数据的基本原理。通过实例分析,验证了集合卡尔曼滤波在整合动态数据和三维地震数据更新油藏参数方面的有效性,更新后的油藏模型能够较好地反映储层非均质性,并且与三维地震数据有较好的一致性。还比较了利用三维地震数据和四维地震数据进行更新油藏模型的差别。通过bKF方法将地震数据和动态数据结合起来描述油藏特征,能够很好地拟合观测数据,并获得较好的模型。     

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models. However, they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models. For stable convergence in ensemble Kalman filter(EnKF), increasing ensemble size can be one of the solutions, but it causes high computational cost in large-scale reservoir systems. In this paper, we propose a preprocessing of good initial model selection to reduce the ensemble size, and then, EnKF is utilized to predict production performances stochastically. In the model selection scheme,representative models are chosen by using principal component analysis(PCA) and clustering analysis. The dimension of initial models is reduced using PCA, and the reduced models are grouped by clustering. Then, we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data.One representative model with the minimum error is considered as the best model, and we use the ensemble members near the best model in the cluster plane for applying EnKF. We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.     

Iterative static modeling of channelized reservoirs using historymatched facies probability data and rejection of training image

Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image (TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter (EnKF) and ensemble smoother (ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.     

基于布谷鸟算法的储层物性参数同步反演

物性反演是储层预测与评价的重要手段,可直观描述储集层所蕴含的信息。由于地球物理反演的非线性特征,以局部寻优方法开展的储层物性参数反演方法难以降低不适定性,结果存在多解性。为此,提出一种基于布谷鸟算法的储层物性参数同步反演方法。以弹性阻抗与储层物性参数关系为基础,构建储层物性反演目标函数,引入布谷鸟算法寻找目标函数最优解。布谷鸟算法作为一种新型元启发式算法,其包含的Levy飞行机制能有效解决常规方法陷入局部极值的问题,可实现更高精度的储层物性参数预测。理论模型和实际数据测试均表明,该方法能够有效反演物性参数,可为储层描述提供数据支持。     

自动识别油藏边界水侵量微分方程反演算法

针对水驱油数值模拟,提出一种基于微分方程反演的自动历史拟合算法,解决了单相微可压缩流体渗流中油藏边界水侵量自动识别问题。利用生产井的动态观测资料识别油藏边界上的水侵量,在数学上称为边界控制反问题。基于微分方程反演理论,将其转化为一个非线性优化问题,利用共轭梯度法求解。共轭梯度法是通过引入相应的伴随问题和敏感性问题,分别确定每次迭代的搜索方向和搜索步长,解决了时空域中一般反演方法的不稳定性、收敛速度慢和依赖于初值等一系列问题。理论模型和实际资料检验表明,该方法不仅丰富了微分方程反演理论,而且具有生产实用价值。     

融合地质建模与地震反演技术提高储层预测精度的新方法及其应用——以涠洲11-1N油田为例

常规地震反演预测储层效果往往不理想,为此提出了一套融合地质建模与地震反演技术提高储层预测精度的新方法,即采用地质建模技术精确建立初始波阻抗模型,再用该初始波阻抗模型约束进行地震反演,进一步提高地震反演储层预测精度。本文提出的新方法在涠洲11-1N油田进行了应用,储层预测精度得到了明显提高,解决了该油田生产中遇到的地质问题。     

基于小层约束地震反演技术在T油田的应用

为了实现地震储层预测技术向油田开发领域的延伸,了解储层展布体系,提出了基于开发小层的储层精细地震反演技术。首先利用高分辨率层序地层学理论指导小层划分,确保地层格架的等时性和合理性。在此基础上以开发小层为约束,以测井资料为出发点,根据地震响应的趋势进行波阻抗反演,预测储层展布规律。在T油田的应用证明,基于小层约束的地震反演能够提高地震资料的纵向分辨能力,有效提高储层预测的精度,可更加清楚地揭示小层内储层的分布特征,为油藏评价、油田开发方案编制和实施提供翔实的基础资料。     

一种三维高精度储层参数反演方法

本语文运用正、反演结合的最优化算法，首先建立井点处的波阻抗模型；再进行地质层位的三维精细对比及追踪；然后进行井点波阻抗模型引导的、地质层位约束的，面元控制的三维自适应外推反演，得到三维高精度波阻抗数据体；最后结合测井、地质、钻井、岩心分析等资料反演出速度、密度孔隙率等三储层参数数据体，并输出储层参数时间切片、沿层切片以及储层的速度、孔隙率、累计厚度、有效厚度等储层参数平面图。所得结果标定准确、垂向     

Application of prestack simultaneous AVA multiparameter inversion in gasbearing reservoir prediction

随着油气勘探的不断深入，叠前地震数据的反演和应用逐渐成为热点。叠前AVA同步反演可从小、中、大三个角度部分叠加数据体上同步反演出纵、横波阻抗和密度数据体。在此基础上，可提取出地层的组合弹性参数。通常砂岩储层含气后纵波速度明显降低，横波速度变化不大，同时密度也降低。与单纯应用纵波阻抗相比，纵、横波阻抗等属性联合可提高流体的识别精度。因而，应用同步反演技术进行含气储层识别是可行的。首先介绍了叠前AVA同步反演的基本原理，继而从地震和测井资料处理、角度子波提取和标定以及最终反演等方面，讨论了叠前AVA同步反演技术在胜利油田中层气藏勘探中的应用。结果表明该方法进行含气储层预测是有效的。     

水驱油藏时移地震叠前匹配反演——西非深水扇A油田时移地震研究实例

水驱时移地震研究即是将基础数据与监测数据的地震响应差异翻译为油藏特征的变化。以深水扇油藏水驱开发时移地震研究为例, 本文探讨了应用基于岩石物理研究的地震叠前匹配反演获得水驱开发敏感弹性属性体及其差异体的方法。通过流体替代开展岩石物理分析, 对水驱过程中岩石物理参数的流体敏感性进行排序, 确定匹配反演目标属性; 深入讨论了影响匹配反演效果的子波、约束模型及关键参数等三个主要因素。匹配反演获得的多种水驱流体敏感属性体及其差异体为后续剩余油分布预测及油藏开采部署提供了可靠依据。     

油藏特征描述

综述了第 6 5届EAGE年会有关油藏特征描述的方法和技术 ,包括 :多分量地震数据及地震相分析在油藏特征描述中的应用 ,AVO反演及分析 ,岩石参数估算 ,地震属性分析 ,岩性和油气检测 ,用于储层特征描述的生产数据的优化反演和生产史匹配 ,地质体填图等。     

分频融合反演技术在春光探区的应用

春光探区位于准噶尔盆地西缘车排子凸起,部分储层薄、规模小且横向变化快,常用的高分辨率反演方法不能适用于本区储层预测。为此,本文提出分频融合反演方法,即将常规模型反演结果作为初始模型,利用高频地震资料进行高分辨率反演。该技术通过高分辨率测井约束模型反演提高垂向分辨率,并保证了模型反演的准确性;同时利用地震调谐频率约束进行高分辨率反演,降低了测井资料高频信息横向插值的不确定性。该方法适用于储层较薄且横向变化快的油气区,可为该类油气区的储层预测提供帮助。     

测井约束反演技术在不同类型沉积体系中的应用

为了把测井约束反演技术真正有效地应用到储层预测中，针对不同类型沉积体系，提出了应注意的问题：①针对浅层河流相砂泥岩互层，必须做好测井资料的环境校正，以避免因泥浆浸泡而引起的速度差异；②对于含有碳酸盐岩的冲积扇相砂砾岩体，在建立地质模型的基础上，利用多井多参数约束反演，建立相应的解释模型，以克服反演结果的多解性；③在储层与围岩速度难以区分的湖相沉积体系中，应在波阻抗反演的基础上，找出能区分储层和围岩的其它测井曲线(如自然伽马、视电阻率等)进行反演，以提高分辨率；④在复杂的海相碳酸盐岩裂缝储层中，要以钻井资料为依据，选择以钻井为约束主体的反演方法，以达到反演结果与地质条件吻合的目的。