Regional probability perturbations for history matching

Reservoir simulation is ubiquitous in the petroleum industry, and conditioning models to production data (history matching) has become an essential job of reservoir engineers. Traditional history-matching methods directly perturb reservoir properties without regard to the existing geological continuity. When the geological heterogeneity is destroyed, the result is often a history-matched model with little prediction power. This paper presents a method for matching historical production data and, at the same time, honoring large-scale geologic information that is obtained from stratigraphic and sedimentological interpretations. Geostatistical simulation methods are used to inject the interpreted geological heterogeneity into a reservoir model. In order to maintain geological continuity in the history match models, we propose a method that perturbs the probability models used to simulate the geological model. Regional perturbations are incorporated to make the method practical for field cases that have numerous wells and local geologic differences. We demonstrate that the proposed approach does not create geological artifacts at the region borders. Additionally, a new simple yet efficient optimization method that can jointly optimize the magnitude of the perturbations for a large number of regions is proposed. A number of realistic synthetic examples demonstrate the method under various geological scenarios and several production data matching criteria. A realistic 3D synthetic simulation model based on a North Sea fluvial channel-type reservoir demonstrates how the method would work in practice. The current work provides reservoir engineers with an additional tool to use in the history-matching process.     

Upscaling and Discretization Errors in Reservoir Simulation

Abstract

Reservoir simulation models are used as an everyday tool for reservoir management. The number of grid blocks in a simulation model is generally much smaller than the number of the grid blocks in the geological model. The geological model is therefore regularly upscaled for building reasonably sized simulation models. Any upscaling causes a loss in detail and introduces errors. Understanding the nature of the errors that occur due to the upscaling step is important because it can provide a handle on the kind of upscaling to be performed and the optimum level of upscaling. Furthermore, understanding interaction of gridding and upscaling errors is essential for building reliable simulation models. In this article, we analyze errors introduced due to the upscaling step.

First, the generality of the purely local single-phase upscaling is considered and the errors introduced due to its use in complex multiphase flows and investigated. Three kinds of errors, namely total upscaling errors, discretization errors, and errors due to the loss of heterogeneity are defined, and their behavior as a function of the level of upscaling is studied for different types of permeability distributions. The reasons for apparently low total upscaling errors at high levels of upscaling are investigated. The efficacy of the geostatistical tools (variograms, QQ plots) for understanding the geological structure of the upscaled models, as compared to the reference model, is tested for different cases.

Second, the uncertainty introduced in the flow results due to the upscaling errors is studied in conjunction with the uncertainty incorporated through the introduction of geological variability in the ensemble of geological models. The behavior of upscaling errors and geological variability as a function of level of upscaling is studied, and its possible impact on important reservoir management decisions is analyzed. It is shown that the uncertainty models of cumulative oil profiles, obtained from flow results of highly upscaled models can contain significant uncertainties. These uncertainties are a result of upscaling errors and therefore cannot be considered to represent only geological uncertainty, which is captured by the introduction of geological variability in the ensemble of the geological models.     

Sensitivity-based upscaling for history matching of reservoir models

Simulation of reservoir flow processes at the finest scale is computationally expensive and in some cases impractical. Consequently, upscaling of several fine-scale grid blocks into fewer coarse-scale grids has become an integral part of reservoir simulation for most reservoirs. This is because as the number of grid blocks increases, the number of flow equations increases and this increases, in large proportion, the time required for solving flow problems. Although we can adopt parallel computation to share the load, a large number of grid blocks still pose significant computational challenges. Thus, upscaling acts as a bridge between the reservoir scale and the simulation scale. However as the upscaling ratio is increased, the accuracy of the numerical simulation is reduced; hence, there is a need to keep a balance between the two. In this work, we present a sensitivity-based upscaling technique that is applicable during history matching. This method involves partial homogenization of the reservoir model based on the model reduction pattern obtained from analysis of the sensitivity matrix. The technique is based on wavelet transformation and reduction of the data and model spaces as presented in the 2Dwp–wk approach. In the 2Dwp–wk approach, a set of wavelets of measured data is first selected and then a reduced model space composed of important wavelets is gradually built during the first few iterations of nonlinear regression. The building of the reduced model space is done by thresholding the full wavelet sensitivity matrix. The pattern of permeability distribution in the reservoir resulting from the thresholding of the full wavelet sensitivity matrix is used to determine the neighboring grids that are upscaled. In essence, neighboring grid blocks having the same permeability values due to model space reduction are combined into a single grid block in the simulation model, thus integrating upscaling with wavelet multiscale inverse modeling. We apply the method to estimate the parameters of two synthetic reservoirs. The history matching results obtained using this sensitivity-based upscaling are in very close agreement with the match provided by fine-scale inverse analysis. The reliability of the technique is evaluated using various scenarios and almost all the cases considered have shown very good results. The technique speeds up the history matching process without seriously compromising the accuracy of the estimates.     

Calculation OOIP in oil reservoir by pressure matching method using genetic algorithm

This paper describes the optimization pressure matching method with a genetic algorithm for the estimation of original oil in place (OOIP). In this method, the pressure of a reservoir is obtained based on the material balance equation by minimizing the difference between calculated and reservoir pressure in order to optimize reservoir parameters suchas OOIP, aquifer constant and water influx data calculated by the aquifer function.The high non-linearity of the pressure matching method makes deterministic classic optimization methods inefficient and unlikely to be successful. Firstly, the solution is dependent on the initial guesses due to the non-convexities of the system equations, and secondly, the convergence of the solution is not always guaranteed.Therefore, an effective optimization strategy combining a two stage approach, genetic algorithm for the initialization and identification of the search zone followed by a simplex search method used to refine the solution, is proposed.Three examples are provided to illustrate the effectiveness of this work by comparing the results of various methods (graphical methods and volumetric estimation) with the results of the pressure matching method.     

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.     

基于相序的多点地质统计学相建模方法及其应用

目前的多点地质统计建模方法均以一定维度的数据模板为载体,将概率或几何距离作为建模工区未知区域模拟取值的判定标准,该判定标准在地质建模时与地质含义的联系较为薄弱。针对上述不足,文章回归地质建模的本质,考虑相建模过程中数据事件与训练图型对比的地质含义,提出基于相序的多点地质统计相建模方法。该方法以具有沉积学意义的相序为基本的相似度对比单元,新建适合于相序相似度对比的动态数据模板,采用句法模式识别技术,通过动态规划方法计算数据事件中已知相序与训练图型中对应相序的相似度,对建模工区未知区域进行相模拟,实现了从沉积学意义出发对强非均质性储层进行多点地质统计学建模。理想模型和塔河X区三角洲前缘储层沉积相模拟表明,本文方法能够有效重现训练图像所显示的各地质体的几何形态,较好地反映储层不同相的沉积展布规律,大幅提高了相建模的精度。该方法将沉积学与多点地质统计学有效结合,提出了一种全新的多点地质统计学相似度对比方法和多点地质统计学建模框架,为复杂储层的相建模提供了一种新的方法。     

三维地质建模与数值模拟技术在裂缝型有水气藏开发中的应用

以典型碳酸盐岩裂缝型有水气藏为例,阐述了裂缝型气藏三维地质建模与数值模拟一体化气藏评价技术。首先应用三维地质建模技术,利用地质、测井等测试资料,进行地层格架、断层、裂缝及相模型的建立,为气藏数值模拟提供了初始静态地质模型;其次,通过数值模拟技术,利用生产动态测试资料,通过历史拟合调整完善数值模型;最后,对不同采气速度、增压开采工艺、排水采气工艺等开发措施进行指标预测评价,最终确定合理的开发调整方案。生产实践表明,应用气藏一体化评价技术能实现不同开发阶段的协同管理,为复杂气藏调整开发方案提供了依据。     

多点地质统计反演技术

在开发中后期,传统的基于两点统计的地震储层反演方法可提高碎屑岩储层纵向表征分辨率,获得高分辨率的反演结果。然而在储层形态上,两点统计反演难以刻画储层复杂形态及结构特征。多点地质统计反演则能够通过多个空间点联合分布刻画特定地质体形态,同时保证垂向高分辨率特征。给出了一种基于多点地质统计学的储层反演方法和流程,首先通过多点地质统计建模确定岩相分布,然后通过岩相与弹性属性的先验分布抽样获得岩相模型控制下的弹性参数场分布,最后通过合成地震记录与理论地震记录匹配决定最优岩相和弹性参数场,实现多点地质统计地震反演。理论模型对比研究表明：相对于传统的两点统计反演和稀疏脉冲反演,多点地质统计地震反演结果精度高,具有推广应用价值。     

Optimizing the performance of smart wells in complex reservoirs using continuously updated geological models

Optimizing the operation of smart wells (i.e., wells with downhole chokes and sensors) remains a challenge, particularly in light of geological uncertainty. In this paper a combined valve optimization and history-matching procedure is presented and applied. The overall approach allows for the continuous updating of the geological model using data inferred from downhole sensors. The method uses numerically computed gradients, generated using a commercial reservoir simulator for function evaluations, for the valve optimization in conjunction with a probability perturbation approach for the history matching. The methodology is applied to three examples involving both variogram and multiple point geostatistical models. For these cases, using the proposed methodology, oil production is shown to be nearly equal to that achieved using optimized valves with known geology, indicating the potential benefits of the overall approach. In one of the cases, optimization over multiple history-matched models provides significant improvement over optimizing with a single model.     

升尺度在求解等价渗透率中的研究现状及发展趋势

为进一步了解升尺度方法在储层研究中的应用。介绍了单相流中的解析方法、求解压力方法、重正化方法、有效介质方法、流线方法以及两相流中的准静态方法和动态方法等升尺度方法的基本原理及求解思路，结合实际应用分析了不同升尺度方法的适用条件和优缺点。阐述了升尺度研究的发展趋势。综合分析认为，升尺度方法对地质建模和油藏数值模拟具有指导作用。     

孤家子—后五家户气田地震反演与储层建模综合研究

地震反演和储层建模是储层横向预测的两种方法。地质统计学地震反演是一种基于模型的地震反演，采用地质统计学空间插值技术建立模型，将测井曲线或测井解释成果曲线演化为“假阻抗”来合成地震记录，寻找子波，再将地震道直接反演成储层属性。这种方法避免了波阻抗转换为储层属性所造成的误差。储层建模横向预测中，应用协克里金算法联合地震反演与原始测井两种数据，综合建立地质模型。在孤家子—后五家户气田实际应用中，选取具有代表性的R2.5梯度曲线进行反演，反演结果反映了砂体分布。结合各井点测井解释数据，进行了储层建模，提高了横向预测精度。     

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...     

Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks

This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models. It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data. Compared with existing geostatistics-based modeling methods, our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks (GANs). GANs couple a generator with a discriminator, and each uses a deep convolutional neural network. The networks are trained in an adversarial manner until the generator can create “fake” images that the discriminator cannot distinguish from “real” images. We extend the original GAN approach to 3D geological modeling at the reservoir scale. The GANs are trained using a library of 3D facies models. Once the GANs have been trained, they can generate a variety of geologically realistic facies models constrained by well data interpretations. This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends. The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods, which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.     

基于成因机理及主控因素约束的多尺度裂缝“分级-分期-分组”建模方法——以四川盆地元坝地区上二叠统长兴组生物礁相碳酸盐岩储层为例

油气藏中普遍发育多类、多尺度天然裂缝,其成因具有多期次性,规模具有多级次性,发育受多种地质因素综合控制,这些特点为裂缝的精细三维地质建模带来了很大挑战。以四川盆地元坝地区上二叠统长兴组生物礁相碳酸盐岩储层为例,提出了一种基于成因机理及主控因素约束的多尺度裂缝“分级-分期-分组”三维地质建模方法,认为多尺度裂缝建模应遵循等时约束、层次约束及成因控制等原则,建模过程中应充分开展裂缝地质研究,根据研究区实际建立合理的裂缝级次、期次划分方案,分级、分期开展裂缝三维建模。(1)大尺度裂缝主要采用确定性方法进行建模,中-小尺度裂缝则采用其成因机制和发育主控因素进行约束建模;(2)在裂缝分期、分组描述和参数统计获得关键地质规律认识的基础上,通过地质力学方法分期开展中-小尺度裂缝分布预测,进而采用“分期-分组”的思路进行建模;(3)最终将大尺度裂缝模型和中-小尺度裂缝模型进行融合,得到多尺度裂缝网络模型。元坝地区长兴组储层多尺度裂缝建模实践表明,上述建模方法可有效弥补传统方法在建立中-小尺度裂缝模型精度方面的不足。     

低渗透砂岩储集层油藏评价一体化研究方法及应用

随着陆上油田开发难度的不断增大，传统单一的油藏勘探开发方法已经不能满足日益复杂的油藏情况。鄂尔多斯盆地某油田长6^3。油层组属于典型的陆相成因的油气储集层，具有非均质性强、储集层物性差、储量丰度低的特点。介绍了现代油藏描述和油藏数值模拟技术一体化研究思想，针对研究目的层组的地质特征，首先应用随机建模技术建立精细油藏地质模型，并应用网格粗化技术将精细地质模型粗化到油藏数值模拟器能接受的数值模型，最后通过数值模拟中的历史拟合、参数调整步骤，完善地质模型，优化油藏地质认识。生产实际表明，通过一体化技术进行复杂油藏研究能较好解决石油地质研究和油藏工程的衔接问题，能从动态和静态角度研究油藏，为油藏开发的动态预测和方案优化奠定了基础。图5表1参17     

Using downhole temperature measurement to assist reservoir characterization and optimization

Without initial seismic or detailed geological information, reservoir characterization is difficult. Downhole temperature distribution in horizontal wells is an important source that helps to characterize the reservoir and understand the bottom-hole flow conditions. The temperature measurements are obtained from permanent monitoring systems such as downhole temperature gauges and fiber optic sensors. Additionally, production history and bottomhole pressures are usually readily available and are routinely used for history matching to improve the initial geological models. By combining the downhole temperature distribution and the production history, more reliable information can be extracted about the reservoir permeability distribution and bottomhole flow conditions in order to optimize the wellbore performance, particularly in horizontal wells.In this paper, a thermal model and a transient, 3D, multiphase flow reservoir model are used to calculate the wellbore temperature distribution in horizontal wells. By comparing the simulated temperature and the observed data, large-scale permeability trends in the reservoir are derived. These permeability trends are then incorporated as ‘secondary’ information in the geologic model building and history matching. The final outcome is a geologic model that has the constraints of both temperature and production history information.A synthetic case is presented to illustrate the procedure. The results show when using production history matching only without distributed temperature data along the wellbore, the water entry location in horizontal wells cannot be detected satisfactorily. By combining production history matching with downhole temperature distribution data in a wellbore, an improved geological model is developed that can match production history and locate water entries correctly. Based on the downhole flow conditions and the updated geological model, the well performance can be optimized by controlling the inflow rate distribution in a horizontal well.     

油气层反射特征预测的一个新方法

油气层反射特征预测是油气检测的一项基础工作,可为油气检测建立正确的检测标志。这里所介绍的新方法是采用了佐普里兹(Zoeppritz)方程计算反射系数,模拟地震资料采集中的实际观测系统和叠加过程,并对薄层进行模型研究。通过此法制作的油气层反射相对振幅图板,可用来预测油气层的反射特征。给出的算例表明,用该方法预测的油气层反射特征更接近实际情况,特别是气层,与以往方法有明显不同。今后的油气检测步骤应是:首先按新方法预测油气层的反射特征,在地震剖面上寻找油气异常;然后利用叠加前振幅-炮检距分析识别油气层。     

油藏模型网格粗化的理论与方法

近20 年来, 由于油藏描述与油藏模拟的需要, 人们提出了许多油藏模型网格粗化(Reservoir Upscaling) 的理论与方法。这些方法大致可归纳为5 类: 指数律平均法、重正规化技术、解压力方程法、矢量法和拟函数法。传统方法的共同点是它们倾向于抹平一些在空间分布的渗透率极值条带, 例如, 泥岩夹层和裂缝。然而, 非均质油藏的生产实践和模拟实验都表明, 这些渗透率极值条带对原油采收率, 尤其是无水采收率影响极大。因此, 在网格粗化过程中怎样处理渗透率极值条带, 怎样对天然裂缝油藏以及石灰岩油藏进行网格粗化, 是这一研究领域有待解决的挑战性问题。     

地震、测井和地质综合一体化油藏描述与评价——以南堡1号构造东营组一段油藏为例

以南堡1号构造东营组一段油藏为例,针对工区油藏特征和现有资料情况,确立地震、测井和地质综合一体化油藏描述和评价思路.通过井一震结合,搭建层序地层格架,进行层序界面解释和层序约束叠前反演,建立工区储集层地质概念模式;利用储集层地质建模软件,建立等时地层框架构造模型和储集层定量地质知识库;通过对比优选随机建模方法,设计9个实现模型,开展确定性条件约束随机建模的沉积相模拟和相控物性模拟,进行油藏储量估算和评价以及主要油砂体精细描述.结果显示,东一段油藏为一受构造控制的层状边水油藏,其砂层厚度大,物性好,发育多套油水系统;9个实现模型所估算的地质储量是一组服从某种统计概率分布的蒙特卡洛实现,利于合理评价油藏储量规模.