History-matching of Petroleum Reservoir Models by the Ensemble Kalman Filter and Parameterization Methods PDF Download
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Author: Leila Heidari
Publisher:
ISBN:
Category :
Languages : en
Pages : 224
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Book Description
History-matching enables integration of data acquired after the production in the reservoir model building workflow. Ensemble Kalman Filter (EnKF) is a sequential assimilation or history-matching method capable of integrating the measured data as soon as they are obtained. This work is based on the EnKF application for History-matching purposes and is divided into two main sections. First section deals with the application of the EnKF to several case studies in order to better understand the merits and shortcomings of the method. These case studies include two synthetic case studies (a simple one and a rather complex one), a Facies model and a real reservoir model. In most cases the method is successful in reproducing the measured data. The encountered problems are explained and possible solutions are proposed. Second section deals with two newly proposed algorithms combining the EnKF with two parameterization methods: pilot point method and gradual deformation method, which are capable of preserving second order statistical properties (mean and covariance). Both developed algorithms are applied to the simple synthetic case study. For the pilot point method, the application was successful through an adequate number and proper positioning of pilot points. In case of the gradual deformation, the application can be successful provided the background ensemble is large enough. For both cases, some improvement scenarios are proposed and further applications to more complex scenarios are recommended.
Author: Leila Heidari
Publisher:
ISBN:
Category :
Languages : en
Pages : 224
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Book Description
History-matching enables integration of data acquired after the production in the reservoir model building workflow. Ensemble Kalman Filter (EnKF) is a sequential assimilation or history-matching method capable of integrating the measured data as soon as they are obtained. This work is based on the EnKF application for History-matching purposes and is divided into two main sections. First section deals with the application of the EnKF to several case studies in order to better understand the merits and shortcomings of the method. These case studies include two synthetic case studies (a simple one and a rather complex one), a Facies model and a real reservoir model. In most cases the method is successful in reproducing the measured data. The encountered problems are explained and possible solutions are proposed. Second section deals with two newly proposed algorithms combining the EnKF with two parameterization methods: pilot point method and gradual deformation method, which are capable of preserving second order statistical properties (mean and covariance). Both developed algorithms are applied to the simple synthetic case study. For the pilot point method, the application was successful through an adequate number and proper positioning of pilot points. In case of the gradual deformation, the application can be successful provided the background ensemble is large enough. For both cases, some improvement scenarios are proposed and further applications to more complex scenarios are recommended.
Author: Abhiniandhan Raghu
Publisher:
ISBN:
Category : Ecological heterogeneity
Languages : en
Pages : 126
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Book Description
The high heterogeneity of petroleum reservoirs, represented by their spatially varying rock properties (porosity and permeability), greatly dictates the quantity of recoverable oil. In this work, the estimation of these rock properties, which is crucial for the future performance prediction of a reservoir, is carried out through a history matching technique using constrained ensemble Kalman filtering (EnKF) and particle filtering (PF) methods. The first part of the thesis addresses some of the main limitations of the conventional EnKF. The EnKF, formulated on the grounds of Monte Carlo sampling and the Kalman filter (KF), arrives at estimates of parameters based on statistical analysis and hence could potentially yield reservoir parameter estimates that are not geologically realistic and consistent. In order to overcome this limitation, hard and soft data constraints in the recursive EnKF estimation methodology are incorporated. Hard data refers to the actual values of the reservoir parameters at discrete locations obtained by core sampling and well logging. On the other hand, the soft data considered here is obtained from the variogram, which characterize the spatial correlation of the rock properties in a reservoir. In this algorithm, the correlation matrix obtained after the unconstrained EnKF update is transformed to honour the true correlation structure from the variogram by applying a scaling and projection method. This thesis also deals with the problem of spurious correlation induced by the Kalman gain computations in the EnKF update step, potentially leading to erroneous update of parameters. In order to solve this issue, a covariance localization-based EnKF coupled with geostatistics is implemented in reservoir history matching. These algorithms are implemented on two synthetic reservoir models and their efficacy in yielding estimates consistent with the geostatistics is observed. It is found that the computational time involved in the localization-based EnKF framework for reservoir history matching is considerably reduced owing to the reduction in the size of the parameter space in the EnKF update step. Also, the geostatistics-based covariance localization performs better in capturing the spatial heterogeneity and variability of the reservoir permeability than the traditional methods. In the second part of the thesis, we extend the history matching implementation using the particle filtering. Reservoir models, being nonlinear, the distributions of the noise and parameters are generally non-Gaussian in nature. Since the EnKF may fail to obtain accurate estimates when the distributions involved in the model are non-Gaussian, we attempt to use a completely Bayesian filter, the particle filter, to estimate reservoir parameters. In addition, the geostatistics-based covariance localization is also coupled with the particle filter and is found to perform better than the filter without any localization.
Author: Alexandre Emerick
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659107283
Category :
Languages : en
Pages : 264
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Book Description
In the last decade, ensemble-based methods have been widely investigated and applied for data assimilation of flow problems associated with atmospheric physics and petroleum reservoir history matching. Among these methods, the ensemble Kalman filter (EnKF) is the most popular one for history-matching applications. The main advantages of EnKF are computational efficiency and easy implementation. Moreover, because EnKF generates multiple history-matched models, EnKF can provide a measure of the uncertainty in reservoir performance predictions. However, because of the inherent assumptions of linearity and Gaussianity and the use of limited ensemble sizes, EnKF does not always provide an acceptable history-match and does not provide an accurate characterization of uncertainty. In this work, we investigate the use of ensemble-based methods, with emphasis on the EnKF, and propose modifications that allow us to obtain a better history match and a more accurate characterization of the uncertainty in reservoir description and reservoir performance predictions.
Author: Juliana Y. Leung
Publisher: McGraw Hill Professional
ISBN: 1259834301
Category : Technology & Engineering
Languages : en
Pages : 465
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Book Description
Reservoir engineering fundamentals and applications along with well testing procedures This practical resource lays out the tools and techniques necessary to successfully construct petroleum reservoir models of all types and sizes. You will learn how to improve reserve estimations and make development decisions that will optimize well performance. Written by a pair of experts, Petroleum Reservoir Modeling and Simulation: Geology, Geostatistics, and Performance Prediction offers comprehensive coverage of quantitative modeling, geostatistics, well testing principles, upscaled models, and history matching. Throughout, special attention is paid to shale, carbonate, and subsea formations. Coverage includes: An overview of reservoir engineering Spatial correlation Spatial estimation Spatial simulation Geostatistical simulation constrained to higher-order statistics Numerical schemes for flow simulation Gridding schemes for flow simulation Upscaling of reservoir models History matching Dynamic data integration
Author: Elkin Rafael Arroyo Negrete
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This work presents a new approach that combines the comprehensive capabilitiesof the ensemble Kalman filter (EnKF) and the flow path information from streamlines to eliminate and/or reduce some of the problems and limitations of the use of the EnKF for history matching reservoir models. The recent use of the EnKF for data assimilation and assessment of uncertainties in future forecasts in reservoir engineering seems to be promising. EnKF provides ways of incorporating any type of production data or timelapse seismic information in an efficient way. However, the use of the EnKF in history matching comes with its shares of challenges and concerns. The overshooting of parameters leading to loss of geologic realism, possible increase in the material balance errors of the updated phase(s), and limitations associated with non-Gaussian permeability distribution are some of the most critical problems of the EnKF. The use of larger ensemble size may mitigate some of these problems but are prohibitively expensive inpractice. We present a streamline-based conditioning technique that can be implemented with the EnKF to eliminate or reduce the magnitude of these problems, allowing for the use of a reduced ensemble size, thereby leading to significant savings in time during field scale implementation. Our approach involves no extra computational cost and is easy to implement. Additionally, the final history matched model tends to preserve most of the geological features of the initial geologic model. A quick look at the procedure is provided that enables the implementation of this approach into the current EnKF implementations. Our procedure uses the streamline path information to condition the covariance matrix in the Kalman Update. We demonstrate the power and utility of our approach with synthetic examples and a field case. Our result shows that using the conditioned technique presented in this thesis, the overshooting/under shooting problems disappears and the limitation to work with non-Gaussian distribution is reduced. Finally, an analysis of the scalability in a parallel implementation of our computer code is given.
Author: Geir Evensen
Publisher: Springer Science & Business Media
ISBN: 3540383018
Category : Science
Languages : en
Pages : 285
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Book Description
This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.
Author: Yeung Yip
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Ensemble Kalman Filter (EnKF) is a data assimilation technique that has gained increasing interest in the application of petroleum history matching in recent years. The basic methodology of the EnKF consists of the forecast step and the update step. This data assimilation method utilizes a collection of state vectors, known as an ensemble, which are simulated forward in time. In other words, each ensemble member represents a reservoir model (realization). Subsequently, during the update step, the sample covariance is computed from the ensemble, while the collection of state vectors is updated using the formulations which involve this updated sample covariance. When a small ensemble size is used for a large, field-scale model, poor estimate of the covariance matrix could occur (Anderson and Anderson 1999; Devegowda and Arroyo 2006). To mitigate such problem, various covariance conditioning schemes have been proposed to improve the performance of EnKF, without the use of large ensemble sizes that require enormous computational resources. In this study, we implemented EnKF coupled with these various covariance localization schemes: Distance-based, Streamline trajectory-based, and Streamline sensitivity-based localization and Hierarchical EnKF on a synthetic reservoir field case study. We will describe the methodology of each of the covariance localization schemes with their characteristics and limitations.
Author: Y. Zee Ma
Publisher: AAPG
ISBN: 0891813780
Category : Science
Languages : en
Pages : 329
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Book Description
Author: Baosheng Liang
Publisher:
ISBN:
Category : Hydrocarbon reservoirs
Languages : en
Pages : 0
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Book Description
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 few decades. However, limited success has been achieved due to the high complexity of the problem and the large computational effort required by the practical applications. An automatic history matching module based on the ensemble Kalman filter is developed and validated in this dissertation. The ensemble Kalman filter has three steps: initial sampling, forecasting through a reservoir simulator, and assimilation. The initial random sampling is improved by the singular value decomposition, which properly selects the ensemble members with less dependence. In this way, the same level of accuracy is achieved through a smaller ensemble size. Four different schemes for the assimilation step are investigated and direct inverse and square root approaches are recommended. A modified ensemble Kalman filter algorithm, which addresses the preference to the ensemble members through a nonequally weighting factor, is proposed. This weighted ensemble Kalman filter generates better production matches and recovery forecasting than those from the conventional ensemble Kalman filter. The proposed method also has faster convergence at the early time period of history matching. Another variant, the singular evolutive interpolated Kalman filter, is also applied. The resampling step in this method appears to improve the filter stability and help the filter to deliver rapid convergence both in model and data domains. This method and the ensemble Kalman filter are effective for history matching and forecasting uncertainty quantification. The independence of the ensemble members during the forecasting step allows the benefit of high-performance computing for the ensemble Kalman filter implementation during automatic history matching. Two-level computation is adopted; distributing ensemble members simultaneously while simulating each member in a parallel style. Such computation yields a significant speedup. The developed module is integrated with reservoir simulators UTCHEM, GEM and ECLIPSE, and has been implemented in the framework Integrated Reservoir Simulation Platform (IRSP). The successful applications to two and three-dimensional cases using blackoil and compositional reservoir cases demonstrate the efficiency of the developed automatic history matching module.
Author: Deepak Devegowda
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models and the assessment of the underlying uncertainties. A considerable body of research work and enhanced computational capabilities have led to an increased application of robust and efficient history matching algorithms to condition reservoir models to dynamic data. Moreover, there has been a shift towards generating multiple plausible reservoir models in recognition of the significance of the associated uncertainties. This provides for uncertainty analysis in reservoir performance forecasts, enabling better management decisions for reservoir development. Additionally, the increased deployment of permanent well sensors and downhole monitors has led to an increasing interest in maintaining 'live' models that are current and consistent with historical observations. One such data assimilation approach that has gained popularity in the recent past is the Ensemble Kalman Filter (EnKF) (Evensen 2003). It is a Monte Carlo approach to generate a suite of plausible subsurface models conditioned to previously obtained measurements. One advantage of the EnKF is its ability to integrate different types of data at different scales thereby allowing for a framework where all available dynamic data is simultaneously or sequentially utilized to improve estimates of the reservoir model parameters. Of particular interest is the use of partitioning tracer data to infer the location and distribution of target un-swept oil. Due to the difficulty in differentiating the relative effects of spatial variations in fractional flow and fluid saturations and partitioning coefficients on the tracer response, interpretation of partitioning tracer responses is particularly challenging in the presence of mobile oil saturations. The purpose of this research is to improve the performance of the EnKF in parameter estimation for reservoir characterization studies without the use of a large ensemble size so as to keep the algorithm efficient and computationally inexpensive for large, field-scale models. To achieve this, we propose the use of streamline-derived information to mitigate problems associated with the use of the EnKF with small sample sizes and non-linear dynamics in non-Gaussian settings. Following this, we present the application of the EnKF for interpretation of partitioning tracer tests specifically to obtain improved estimates of the spatial distribution of target oil.
