共查询到20条相似文献，搜索用时 24 毫秒
1.
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. 相似文献
2.
A multi-resolution workflow to generate high-resolution models constrained to dynamic data 总被引：2，自引：0，他引：2
Céline Scheidt Jef Caers Yuguang Chen Louis J. Durlofsky 《Computational Geosciences》2011,15(3):545-563
Distance-based stochastic techniques have recently emerged in the context of ensemble modeling, in particular for history
matching, model selection and uncertainty quantification. Starting with an initial ensemble of realizations, a distance between
any two models is defined. This distance is defined such that the objective of the study is incorporated into the geological
modeling process, thereby potentially enhancing the efficacy of the overall workflow. If the intent is to create new models
that are constrained to dynamic data (history matching), the calculation of the distance requires flow simulation for each
model in the initial ensemble. This can be very time consuming, especially for high-resolution models. In this paper, we present
a multi-resolution framework for ensemble modeling. A distance-based procedure is employed, with emphasis on the rapid construction
of multiple models that have improved dynamic data conditioning. Our intent is to construct new high-resolution models constrained
to dynamic data, while performing most of the flow simulations only on upscaled models. An error modeling procedure is introduced
into the distance calculations to account for potential errors in the upscaling. Based on a few fine-scale flow simulations,
the upscaling error is estimated for each model using a clustering technique. We demonstrate the efficiency of the method
on two examples, one where the upscaling error is small, and another where the upscaling error is significant. Results show
that the error modeling procedure can accurately capture the error in upscaling, and can thus reproduce the fine-scale flow
behavior from coarse-scale simulations with sufficient accuracy (in terms of uncertainty predictions). As a consequence, an
ensemble of high-resolution models, which are constrained to dynamic data, can be obtained, but with a minimum of flow simulations
at the fine scale. 相似文献
3.
Multiscale mixed/mimetic methods on corner-point grids 总被引：1，自引：0，他引：1
Multiscale simulation is a promising approach to facilitate direct simulation of large and complex grid models for highly
heterogeneous petroleum reservoirs. Unlike traditional simulation, approaches based on upscaling/downscaling, multiscale methods
seek to solve the full flow problem by incorporating subscale heterogeneities into local discrete approximation spaces. We
consider a multiscale formulation based on a hierarchical grid approach, where basis functions with subgrid resolution are
computed numerically to correctly and accurately account for subscale variations from an underlying (fine-scale) geomodel
when solving the global flow equations on a coarse grid. By using multiscale basis functions to discretise the global flow
equations on a (moderately sized) coarse grid, one can retain the efficiency of an upscaling method and, at the same time,
produce detailed and conservative velocity fields on the underlying fine grid. For pressure equations, the multiscale mixed
finite-element method (MsMFEM) has been shown to be a particularly versatile approach. In this paper, we extend the method
to corner-point grids, which is the industry standard for modelling complex reservoir geology. To implement MsMFEM, one needs
a discretisation method for solving local flow problems on the underlying fine grids. In principle, any stable and conservative
method can be used. Here, we use a mimetic discretisation, which is a generalisation of mixed finite elements that gives a
discrete inner product, allows for polyhedral elements, and can (easily) be extended to curved grid faces. The coarse grid
can, in principle, be any partition of the subgrid, where each coarse block is a connected collection of subgrid cells. However,
we argue that, when generating coarse grids, one should follow certain simple guidelines to achieve improved accuracy. We
discuss partitioning in both index space and physical space and suggest simple processing techniques. The versatility and
accuracy of the new multiscale mixed methodology is demonstrated on two corner-point models: a small Y-shaped sector model
and a complex model of a layered sedimentary bed. A variety of coarse grids, both violating and obeying the above mentioned
guidelines, are employed. The MsMFEM solutions are compared with a reference solution obtained by direct simulation on the
subgrid. 相似文献
4.
统计降尺度法对未来区域气候变化情景预估的研究进展 总被引：65，自引：5，他引：65
由于迄今为止大部分的海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测,降尺度法已广泛用于弥补AOGCM在这方面的不足。简要介绍了3种常用的降尺度法：动力降尺度法、统计降尺度法和统计与动力相结合的降尺度法;系统论述了统计降尺度方法的理论和应用的研究进展,其中包括：统计降尺度法的基本假设,统计降尺度法的优缺点，以及常用的3种统计降尺度法；还论述了用统计降尺度法预估未来气候情景的一般步骤，以及方差放大技术在统计降尺度中的应用；同时还强调了统计降尺度方法和动力降尺度方法比较研究在统计降尺度研究中的重要性；最后指出统计与动力相结合的降尺度方法将成为降尺度技术的重要发展方向。 相似文献
5.
Alexandre Boucher 《Mathematical Geosciences》2009,41(3):265-290
Super-resolution or sub-pixel mapping is the process of providing fine scale land cover maps from coarse-scale satellite sensor
information. Such a procedure calls for a prior model depicting the spatial structures of the land cover types. When available,
an analog of the underlying scene (a training image) may be used for such a model. The single normal equation simulation algorithm
(SNESIM) allows extracting the relevant pattern information from the training image and uses that information to downscale
the coarse fraction data into a simulated fine scale land cover scene. Two non-exclusive approaches are considered to use
training images for super-resolution mapping. The first one downscales the coarse fractions into fine-scale pre-posterior
probabilities which is then merged with a probability lifted from the training image. The second approach pre-classifies the
fine scale patterns of the training image into a few partition classes based on their coarse fractions. All patterns within
a partition class are recorded by a search tree; there is one tree per partition class. At each fine scale pixel along the
simulation path, the coarse fraction data is retrieved first and used to select the appropriate search tree. That search tree
contains the patterns relevant to that coarse fraction data. To ensure exact reproduction of the coarse fractions, a servo-system
keeps track of the number of simulated classes inside each coarse fraction. Being an under-determined stochastic inverse problem,
one can generate several super resolution maps and explore the space of uncertainty for the fine scale land cover. The proposed
SNESIM sub-pixel resolution mapping algorithms allow to: (i) exactly reproduce the coarse fraction, (ii) inject the structural
model carried by the training image, and (iii) condition to any available fine scale ground observations. Two case studies
are provided to illustrate the proposed methodology using Landsat TM data from southeast China. 相似文献
6.
Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization 总被引：2，自引：0，他引：2
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. 相似文献
7.
Application of Bias Correction and Spatial Disaggregation in Removing Model Biases and Downscaling over China 总被引：1，自引：0，他引：1
Global Climate Models (GCM) are the primary tools for studying past climate change and evaluating the projected future response of climate system to changing atmospheric composition. However, the state of art GCMs contain large biases in regional or local scales and are often characterized by low resolution which is too coarse to provide the regional scale information required for regional climate change impact assessment. A popular technique, Bias Correction and Spatial Disaggregation (BCSD), are widespreadly employed to improve the quality of the raw model output and downscaling throughout the world. Unfortunately, this method has not been applied in China. Consequently, the detailed principle and procedure of BCSD are introduced systematically in this study. Furthermore, the applicability of BCSD over China is also examined based on an ensemble of climate models from phase five of the Coupled Model Intercomparison Project (CMIP5), though the excellent performance of it has been validated for other parts of the world in many works. The result shows that BCSD is an effective, model independent approach to removing biases of model and downscaling. Finally, application scope of BCSD is discussed, and a suite of fine resolution multimodel climate projections over China is developed based on 34 climate models and two emissions scenarios (RCP4.5 and RCP8.5) from CMIP5. 相似文献
9.
10.
Anca Maria Hanea Maria Gheorghe Remus Hanea Dan Ababei 《Computational Geosciences》2013,17(6):929-949
Reservoir simulation models are used both in the development of new fields and in developed fields where production forecasts are needed for investment decisions. When simulating a reservoir, one must account for the physical and chemical processes taking place in the subsurface. Rock and fluid properties are crucial when describing the flow in porous media. In this paper, the authors are concerned with estimating the permeability field of a reservoir. The problem of estimating model parameters such as permeability is often referred to as a history-matching problem in reservoir engineering. Currently, one of the most widely used methodologies which address the history-matching problem is the ensemble Kalman filter (EnKF). EnKF is a Monte Carlo implementation of the Bayesian update problem. Nevertheless, the EnKF methodology has certain limitations that encourage the search for an alternative method.For this reason, a new approach based on graphical models is proposed and studied. In particular, the graphical model chosen for this purpose is a dynamic non-parametric Bayesian network (NPBN). This is the first attempt to approach a history-matching problem in reservoir simulation using a NPBN-based method. A two-phase, two-dimensional flow model was implemented for a synthetic reservoir simulation exercise, and initial results are shown. The methods’ performances are evaluated and compared. This paper features a completely novel approach to history matching and constitutes only the first part (part I) of a more detailed investigation. For these reasons (novelty and incompleteness), many questions are left open and a number of recommendations are formulated, to be investigated in part II of the same paper. 相似文献
11.
A multiscale method for the dynamic analysis of underground structures is proposed, which involves the concurrent discretization of the entire domain with both coarse‐scale and fine‐scale finite element meshes. The coarse‐scale mesh is employed to capture seismic response characteristics of the integral system, whereas the fine‐scale mesh describes in detail the dynamic response in positions of potential damage or interest. For both the coarse‐scale and fine‐scale meshes to overlap, a bridging scale term is introduced so that compatibility of dynamic behavior between the coarse‐ and fine‐scale models is enforced. Both material and contact nonlinearities are considered in the multiscale model. As an application, the model is used for large‐scale seismic response of a newly built long‐distance shield tunnel. Results show that this multiscale method does not have spurious wave reflections at the fine/coarse interface and does not need filtering procedures, which is an advantage compared with the displacement coupling method. Stress and deformation response in lining segments and their connecting bolts are investigated and analyzed within the fine‐scale model, and the capacity of critical structural components, such as bolts and joints is evaluated. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
12.
空间尺度转换是近年来区域生态水文研究领域的一个基本研究问题。其需要主要是源于模型的输入数据与所能提供的数据空间尺度不一致以及模型所代表的地表过程空间尺度与所观测的地表过程空间尺度不吻合。综述了目前区域生态水文模拟研究中常用的空间尺度转换研究方法,包括向上尺度转换和向下尺度转换。详细论述了2种向下尺度转换方法: 统计学经验模型和动态模型。前者是通过将GCM大尺度数据与长期的历史观测数据比较从而建立统计学相关模型, 然后利用这个统计学经验模型进行向下的空间尺度转换. 然而动态模型并不直接对GCM数据进行向下尺度的转换,而是对与GCM进行动态耦合的区域气候模型(RCM) 的输出数据进行空间尺度转换. 通常后者所获得的数据精度要比前者高,但是一个主要缺点就是并不是全球所有的研究区域都有对应的RCM。还详细论述了2种向上尺度转换方法: 统计学经验模型和斑块模型。前者是建立一个能代表小尺度信息在大尺度上分布的密度分布概率函数, 然后利用这个函数在所需的大尺度上进行积分而求得大尺度所需的信息。而后者是根据相似性最大化原则将大尺度划分为若干个可操作的小尺度斑块,然后将计算的每个小尺度斑块的信息平均化得到大尺度所需的信息。通常在计算这种斑块化的小尺度信息的时候,对每个小尺度也会采用统计学经验模型来计算代表整个斑块小尺度的信息。建议用斑块模型与统计学经验模型相集合的方法来实现向上的空间尺度转换 相似文献
13.
It is becoming easier to combine geographical data and dynamic models to provide information for problem solving and geographical cognition. However, the scale dependencies of the data, model, and process can confuse the results. This study extends traditional scale research in static geographical patterns to dynamic processes and focuses on the combined scale effect of multiscale geographical data and dynamic models. The capacity for topographical expression under the combined scale effect was investigated by taking multiscale topographical data and meteorological processes in Hong Kong as a case study. A meteorological simulation of the combined scale effect was evaluated against data from Hong Kong Observatory stations. The experiments showed that (1) a digital elevation model (DEM) using 3 arc sec data with a 1 km resolution Weather Research and Forecasting (WRF) model gives better topographical expression and meteorological reproduction in Hong Kong; (2) a fine-scale model is sensitive to the resolution of the DEM data, whereas a coarse-scale model is less sensitive to it; (3) better topographical expression alone does not improve weather process simulation; and (4) uncertainty arising from a scale mismatch between the DEM data and the dynamic model may account for 38 % of the variance in certain meteorological variables (e.g., temperature). This case study gives a clear explanation of the significance and implementation of scale matching for multiscale geographical data and dynamic models. 相似文献
14.
Reservoir characterization needs the integration of various data through history matching, especially dynamic information
such as production or 4D seismic data. Although reservoir heterogeneities are commonly generated using geostatistical models,
random realizations cannot generally match observed dynamic data. To constrain model realizations to reproduce measured dynamic
data, an optimization procedure may be applied in an attempt to minimize an objective function, which quantifies the mismatch
between real and simulated data. Such assisted history matching methods require a parameterization of the geostatistical model
to allow the updating of an initial model realization. However, there are only a few parameterization methods available to
update geostatistical models in a way consistent with the underlying geostatistical properties. This paper presents a local
domain parameterization technique that updates geostatistical realizations using assisted history matching. This technique
allows us to locally change model realizations through the variation of geometrical domains whose geometry and size can be
easily controlled and parameterized. This approach provides a new way to parameterize geostatistical realizations in order
to improve history matching efficiency. 相似文献
15.
Detailed reservoir models routinely contain 106–108 grid blocks. These models often cannot be used directly in a reservoir simulation because of the time and memory required for solving the pressure grid on the fine grid. We propose a nested gridding technique that efficiently obtains an approximate solution for the pressure field. The domain is divided into a series of coarse blocks, each containing several fine cells. Effective mobilities are computed for each coarse grid block and the pressure is then found on the coarse scale. The pressure field within each coarse block is computed using flux boundary conditions obtained from the coarse pressure solution. Streamline-based simulation is used to move saturations forward in time. We test the method for a series of example waterflood problems and demonstrate that the method can give accurate estimates of oil production for large 3D models significantly faster than direct simulation using streamlines on the fine grid, making the method overall approximately up to 1,000 times faster than direct conventional simulation. 相似文献
16.
A new method for upscaling fine scale permeability fields to general quadrilateral-shaped coarse cells is presented. The procedure, referred to as the conforming scale up method, applies a triangle-based finite element technique, capable of accurately resolving both the coarse cell geometry and the subgrid heterogeneity, to the solution of the local fine scale problem. An appropriate averaging of this solution provides the equivalent permeability tensor for the coarse scale quadrilateral cell. The general level of accuracy of the technique is demonstrated through application to a number of flow problems. The real strength of the conforming scale up method is demonstrated when the method is applied in conjunction with a flow-based gridding technique. In this case, the approach is shown to provide results that are significantly more accurate than those obtained using standard techniques. 相似文献
17.
Modern geostatistical techniques allow the generation of high-resolution heterogeneous models of hydraulic conductivity containing millions to billions of cells. Selective upscaling is a numerical approach for the change of scale of fine-scale hydraulic conductivity models into coarser scale models that are suitable for numerical simulations of groundwater flow and mass transport. Selective upscaling uses an elastic gridding technique to selectively determine the geometry of the coarse grid by an iterative procedure. The geometry of the coarse grid is built so that the variances of flow velocities within the coarse blocks are minimum. Selective upscaling is able to handle complex geological formations and flow patterns, and provides full hydraulic conductivity tensor for each block. Selective upscaling is applied to a cross-bedded formation in which the fine-scale hydraulic conductivities are full tensors with principal directions not parallel to the statistical anisotropy of their spatial distribution. Mass transport results from three coarse-scale models constructed by different upscaling techniques are compared to the fine-scale results for different flow conditions. Selective upscaling provides coarse grids in which mass transport simulation is in good agreement with the fine-scale simulations, and consistently superior to simulations on traditional regular (equal-sized) grids or elastic grids built without accounting for flow velocities. 相似文献
18.
In heavy oil recovery by immiscible gas injection, adverse mobility ratio and gravity segregation along with influential mass transfer are the most crucial factors controlling displacement efficiencies. Obtaining relative permeability functions using conventional techniques that are based on a stable displacement front could be highly misleading. In this work, an improved methodology was proposed for estimating relative permeability curves under simultaneous effects of frontal instability and mass transfer using history-matching techniques. The compositional analysis of produced oil from a coreflood experiment was employed, which represents dynamic interactions more realistically. For the history matching, an optimum, high-resolution, two-dimensional core model was used, as opposed to the industry standard use of a one-dimensional model. The results of the simulation were then verified by a semi-empirical approach using the Koval model, which was then used to predict a similar experiment but in a vertical orientation. A good match was obtained between the forward simulation and the experiment. To highlight the effect of mass transfer on the shape of relative permeabilities, the simulation results from two immiscible gas injection corefloods were compared: CO2 injection with mass transfer and N2 injection without mass transfer. The results showed that the two estimated functions were quite similar, indicating that instability levels would determine the displacement pattern rather than local mass transfer. This integrated approach, therefore, highlights the importance of employing the right fluid model and an appropriate 2D-grid model in estimating relative permeabilities in displacement with instability and mass transfer against the current industry practice. 相似文献
19.
Thomas Romary 《Computational Geosciences》2010,14(2):343-355
In history matching of lithofacies reservoir model, we attempt to find multiple realizations of lithofacies configuration
that are conditional to dynamic data and representative of the model uncertainty space. This problem can be formalized in
the Bayesian framework. Given a truncated Gaussian model as a prior and the dynamic data with its associated measurement error,
we want to sample from the conditional distribution of the facies given the data. A relevant way to generate conditioned realizations
is to use Markov chains Monte Carlo (MCMC). However, the dimensions of the model and the computational cost of each iteration
are two important pitfalls for the use of MCMC. Furthermore, classical MCMC algorithms mix slowly, that is, they will not
explore the whole support of the posterior in the time of the simulation. In this paper, we extend the methodology already
described in a previous work to the problem of history matching of a Gaussian-related lithofacies reservoir model. We first
show how to drastically reduce the dimension of the problem by using a truncated Karhunen-Loève expansion of the Gaussian
random field underlying the lithofacies model. Moreover, we propose an innovative criterion of the choice of the number of
components based on the connexity function. Then, we show how we improve the mixing properties of classical single MCMC, without
increasing the global computational cost, by the use of parallel interacting Markov chains. Applying the dimension reduction
and this innovative sampling method drastically lowers the number of iterations needed to sample efficiently from the posterior.
We show the encouraging results obtained when applying the methodology to a synthetic history-matching case. 相似文献
20.
G. M. van Essen S. Kahrobaei H. van Oeveren P. M. J. Van den Hof J. D. Jansen 《Computational Geosciences》2016,20(5):1061-1073
We present a method to determine lower and upper bounds to the predicted production or any other economic objective from history-matched reservoir models. The method consists of two steps: 1) performing a traditional computer-assisted history match of a reservoir model with the objective to minimize the mismatch between predicted and observed production data through adjusting the grid block permeability values of the model. 2) performing two optimization exercises to minimize and maximize an economic objective over the remaining field life, for a fixed production strategy, by manipulating the same grid block permeabilities, however without significantly changing the mismatch obtained under step 1. This is accomplished through a hierarchical optimization procedure that limits the solution space of a secondary optimization problem to the (approximate) null space of the primary optimization problem. We applied this procedure to two different reservoir models. We performed a history match based on synthetic data, starting from a uniform prior and using a gradient-based minimization procedure. After history matching, minimization and maximization of the net present value (NPV), using a fixed control strategy, were executed as secondary optimization problems by changing the model parameters while staying close to the null space of the primary optimization problem. In other words, we optimized the secondary objective functions, while requiring that optimality of the primary objective (a good history match) was preserved. This method therefore provides a way to quantify the economic consequences of the well-known problem that history matching is a strongly ill-posed problem. We also investigated how this method can be used as a means to assess the cost-effectiveness of acquiring different data types to reduce the uncertainty in the expected NPV. 相似文献