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Author: Mingliang Liu Publisher: ISBN: Category : Carbon sequestration Languages : en Pages : 153
Book Description
Reservoir characterization and history matching are essential steps in various subsurface applications, such as petroleum exploration and production and geological carbon sequestration, aiming to estimate the rock and fluid properties of the subsurface from geophysical measurements and borehole data. Mathematically, both tasks can be formulated as inverse problems, which attempt to find optimal earth models that are consistent with the true measurements. The objective of this dissertation is to develop a stochastic inversion method to improve the accuracy of predicted reservoir properties as well as quantification of the associated uncertainty by assimilating both the surface geophysical observations and the production data from borehole using Ensemble Smoother with Multiple Data Assimilation. To avoid the common phenomenon of ensemble collapse in which the model uncertainty would be underestimated, we propose to re-parameterize the high-dimensional geophysics data with data order reduction methods, for example, singular value decomposition and deep convolutional autoencoder, and then perform the models updating efficiently in the low-dimensional data space. We first apply the method to seismic and rock physics inversion for the joint estimation of elastic and petrophysical properties from the pre-stack seismic data. In the production or monitoring stage, we extend the proposed method to seismic history matching for the prediction of porosity and permeability models by integrating both the time-lapse seismic and production data. The proposed method is tested on synthetic examples and successfully applied in petroleum exploration and production and carbon dioxide sequestration.
Author: Mingliang Liu Publisher: ISBN: Category : Carbon sequestration Languages : en Pages : 153
Book Description
Reservoir characterization and history matching are essential steps in various subsurface applications, such as petroleum exploration and production and geological carbon sequestration, aiming to estimate the rock and fluid properties of the subsurface from geophysical measurements and borehole data. Mathematically, both tasks can be formulated as inverse problems, which attempt to find optimal earth models that are consistent with the true measurements. The objective of this dissertation is to develop a stochastic inversion method to improve the accuracy of predicted reservoir properties as well as quantification of the associated uncertainty by assimilating both the surface geophysical observations and the production data from borehole using Ensemble Smoother with Multiple Data Assimilation. To avoid the common phenomenon of ensemble collapse in which the model uncertainty would be underestimated, we propose to re-parameterize the high-dimensional geophysics data with data order reduction methods, for example, singular value decomposition and deep convolutional autoencoder, and then perform the models updating efficiently in the low-dimensional data space. We first apply the method to seismic and rock physics inversion for the joint estimation of elastic and petrophysical properties from the pre-stack seismic data. In the production or monitoring stage, we extend the proposed method to seismic history matching for the prediction of porosity and permeability models by integrating both the time-lapse seismic and production data. The proposed method is tested on synthetic examples and successfully applied in petroleum exploration and production and carbon dioxide sequestration.
Author: Alexandre Emerick Publisher: LAP Lambert Academic Publishing ISBN: 9783659107283 Category : Languages : en Pages : 264
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: Behnam Jafarpour Publisher: ISBN: Category : Languages : en Pages : 212
Book Description
This improves under-constrained inverse problems such as reservoir history matching in which the number of unknowns significantly exceeds available data. The proposed parameterization approach is general and can be applied with any inversion algorithm. The suitability of the proposed estimation framework for hydrocarbon reservoir characterization is demonstrated through several water flooding examples using synthetic reservoir models.
Author: Dario Grana Publisher: John Wiley & Sons ISBN: 1119086205 Category : Science Languages : en Pages : 256
Book Description
Seismic reservoir characterization aims to build 3-dimensional models of rock and fluid properties, including elastic and petrophysical variables, to describe and monitor the state of the subsurface for hydrocarbon exploration and production and for CO2 sequestration. Rock physics modeling and seismic wave propagation theory provide a set of physical equations to predict the seismic response of subsurface rocks based on their elastic and petrophysical properties. However, the rock and fluid properties are generally unknown and surface geophysical measurements are often the only available data to constrain reservoir models far away from well control. Therefore, reservoir properties are generally estimated from geophysical data as a solution of an inverse problem, by combining rock physics and seismic models with inverse theory and geostatistical methods, in the context of the geological modeling of the subsurface. A probabilistic approach to the inverse problem provides the probability distribution of rock and fluid properties given the measured geophysical data and allows quantifying the uncertainty of the predicted results. The reservoir characterization problem includes both discrete properties, such as facies or rock types, and continuous properties, such as porosity, mineral volumes, fluid saturations, seismic velocities and density. Seismic Reservoir Modeling: Theory, Examples and Algorithms presents the main concepts and methods of seismic reservoir characterization. The book presents an overview of rock physics models that link the petrophysical properties to the elastic properties in porous rocks and a review of the most common geostatistical methods to interpolate and simulate multiple realizations of subsurface properties conditioned on a limited number of direct and indirect measurements based on spatial correlation models. The core of the book focuses on Bayesian inverse methods for the prediction of elastic petrophysical properties from seismic data using analytical and numerical statistical methods. The authors present basic and advanced methodologies of the current state of the art in seismic reservoir characterization and illustrate them through expository examples as well as real data applications to hydrocarbon reservoirs and CO2 sequestration studies.
Author: Seonkyoo Yoon Publisher: ISBN: Category : Languages : en Pages : 106
Book Description
Subsurface flow modeling is an indispensable task for reservoir management, but the associated computational cost is burdensome owing to model complexity and the fact that many simulation runs are required for its applications such as production optimization, uncertainty quantification, and history matching. To relieve the computational burden in reservoir flow modeling, a reduced-order modeling procedure based on hyper-reduction is presented. The procedure consists of three components: state reduction, constraint reduction, and nonlinearity treatment. State reduction based on proper orthogonal decomposition (POD) is considered, and the impact of state reduction, with different strategies for collecting snapshots, on accuracy and predictability is investigated. Petrov- Galerkin projection is used for constraint reduction, and a hyper-reduction that couples the Petrov-Galerkin projection and a 'gappy' reconstruction is applied for the nonlinearity treatment. The hyper-reduction method is a Gauss-Newton framework with approximated tensors (GNAT), and the main contribution of this study is the presentation of a procedure for applying the method to subsurface flow simulation. A fully implicit oil-water two-phase subsurface flow model in three-dimensional space is considered, and the application of the proposed hyper-reduced-order modeling procedure achieves a runtime speedup of more than 300 relative to the full-order method, which cannot be achieved when only constraint reduction is adopted. In addition, two types of sequential Bayesian filtering for history matching are considered to investigate the performance of the developed hyper-reduced-order model to relive the associated computational cost. First, an ensemble Kalman filter (EnKF) is considered for Gaussian system and a procedure embedding the hyper-reduced model (HRM) into the EnKF is presented. The use of the HRM for the EnKF significantly reduces the computational cost without much loss of accuracy, but the combination requires a few remedies such as clustering to find an optimum reduced-order model according to spatial similarity of geological condition, which causes an additional computation. For non-Gaussian system, an advanced particle filter, known as regularized particle filter (RPF), is considered because it does not take any distributional assumptions. Particle filtering has rarely been applied for reservoir history matching due to the fact that it is hard to locate the initial particles on highly probable regions of state spaces especially when large scale system is considered, which makes the required number of particles scale exponentially with the model dimension. To resolve the issues, reparameterization is adopted to reduce the order of the geological parameters. For the reparameterization, principal component analysis (PCA) is used to compute the reduced space of the model parameters, and by constraining the filtering analysis with the computed subspace the required number of initial particles can be reduced down to a manageable level. Consequently, a huge computational saving is achieved by embedding the HRM into the RPF. Furthermore, the additional cost of clustering required to identify the geospatially optimum reduced-order model is saved because the advanced particle filter allows to easily identify the groups of geospatially similar particles.
Author: Esmail Mohamed Khalil Eltahan Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
A hallmark of unconventional reservoirs is characterization uncertainty. Assisted History Matching (AHM) methods provide attractive means for uncertainty quantification (UQ), because they yield an ensemble of qualifying models instead of a single candidate. Here we integrate embedded discrete fracture model (EDFM), one of fractured-reservoirs modeling techniques, with a commercial AHM and optimization tool. We develop a new parameterization scheme that allows for altering individual properties of multiple wells or fracture groups. The reservoir is divided into three types of regions: formation matrix; EDFM fracture groups; and stimulated rock volume (SRV) around fracture groups. The method is developed in a sleek, stand-alone form and is composed of four main steps: (1) reading parameters exported by tool; (2) generating an EDFM instance; (3) running the instance on a simulator; and (4) calculating a pre-defined objective function. We present two applications. First, we test the method on a hypothetical case with synthetic production data from two wells. Using 20 history-matching parameters, we compare the performance of five AHM algorithms. Two of which are based on Bayesian approach, two are stochastic particle-swarm optimization (PSO), and one is commercial DECE algorithm. Performance is measured with metrics, such as solutions sample size, total simulation runs, marginal parameter posterior distributions, and distributions of estimated ultimate recovery (EUR). In the second application, we assess the effect of natural fractures on UQ of a single horizontal well in the middle Bakken. This is achieved by comparing four AHM scenarios with increasingly varying natural-fracture intensity. Results of the first study show that, based on pre-set acceptance criteria, DECE fails to generate any satisfying solutions. Bayesian methods are noticeably superior to PSO, although PSO is capable to generate large number of solutions. PSO tends to be focused on narrow regions of the posteriors and seems to significantly underestimate uncertainty. Bayesian Algorithm I, a method with a proxy-based acceptance/rejection sampler, ranks first in efficiency but evidently underperforms in accuracy. Results from the second study reveal that, even though varying intensity of natural fractures cam significantly alter other model parameters, that appears not to have influence on UQ (or long-term production)
Author: Reza Yousefzadeh Publisher: Springer Nature ISBN: 3031280792 Category : Technology & Engineering Languages : en Pages : 142
Book Description
This book explores methods for managing uncertainty in reservoir characterization and optimization. It covers the fundamentals, challenges, and solutions to tackle the challenges made by geological uncertainty. The first chapter discusses types and sources of uncertainty and the challenges in different phases of reservoir management, along with general methods to manage it. The second chapter focuses on geological uncertainty, explaining its impact on field development and methods to handle it using prior information, seismic and petrophysical data, and geological parametrization. The third chapter deals with reducing geological uncertainty through history matching and the various methods used, including closed-loop management, ensemble assimilation, and stochastic optimization. The fourth chapter presents dimensionality reduction methods to tackle high-dimensional geological realizations. The fifth chapter covers field development optimization using robust optimization, including solutions for its challenges such as high computational cost and risk attitudes. The final chapter introduces different types of proxy models in history matching and robust optimization, discussing their pros and cons, and applications. The book will be of interest to researchers and professors, geologists and professionals in oil and gas production and exploration.
Author: Siavash Nejadi Publisher: ISBN: Category : Reservoirs Languages : en Pages : 203
Book Description
Reservoir simulation models play an important role in the production forecasting and field development planning. To enhance their predictive capabilities and capture the uncertainties in model parameters, stochastic reservoir models should be calibrated to both geologic and flow observations. The relationship between production performance and model parameters is vastly non-linear, rendering history matching process a challenging task. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo based technique for assisted history matching and real-time updating of reservoir models. EnKF works efficiently with Gaussian variables, but it often fails to honor the reference probability distribution of the model parameters where the distribution of model parameters are non-Gaussian and the system dynamics are strongly nonlinear. In this thesis, novel sampling procedures are proposed to honor geologic information in reservoirs with non-Gaussian model parameters. The methodologies include generating multiple geological models and updating the uncertain parameters using dynamic flow responses using iterative EnKF technique. Two new re-sampling steps are presented for characterization of multiple facies reservoirs. After certain number of assimilation steps, the updated ensemble is used to generate a new ensemble that is conditional to both the geological information and the early production data. Probability field simulation and a novel probability weighted re-sampling scheme are introduce to re-sample a new ensemble. After the re-sampling step, iterative EnKF is again applied on the ensemble members to assimilate the remaining production history. A new automated dynamic data integration workflow is implemented for characterization and uncertainty assessment of fracture reservoir models. This new methodology includes generating multiple discrete fracture network (DFN) models, upscaling the models for flow simulation, and updating the DFN model parameters using dynamic flow responses. The assisted history matching algorithm entails combining a probability weighted sampling with iterative EnKF. The performances of the introduced methodologies are evaluated by performing various simulation studies for different synthetic and field case studies. The qualities of the final matching results are assessed by examining the geological realism of the updated ensemble using the reference probability distribution of the model parameters and computing the predicted dynamic data mismatch.
Author: Devesh Kumar Publisher: ISBN: Category : Languages : en Pages :
Book Description
Inverse modeling techniques for estimating reservoir parameters (e.g., Transmissivity, Permeability, etc.) utilize some secondary information (e.g., hydraulic head or production data at well locations) to estimate reservoir parameters. Ensemble-based data assimilation methods are one such class of inverse modeling techniques. Ensemble Kalman filters (EnKF) in specific are built around the basic framework where modeling parameters such as transmissivity, permeability, storativity, porosity, hydraulic head, phase-saturation are included within a state vector psi^f that are updated to psi^a, based on the available dynamic data. Although EnKF presents the ability to update a large number of parameters successively as data becomes available, it suffers from some major drawbacks. It is optimal only in the case when the multivariate joint distribution describing the state vector is multi-Gaussian. Also, a linear update equation comprised of covariance values between the observed variables and update parameter and covariance between the different observed variables are used in EnKF. These assumptions and simplifications result in models that yield inaccurate predictions of reservoir performance.The aim of this research work is to propose a novel method for data assimilation which is free from the Gaussian and linear transfer function assumptions. This new method can be used to sequentially assimilate dynamic data into reservoir models using an ensemble based approach. Updating is performed in the indicator space where modeling is performed non-parametrically and the indicator transform is insensitive to non-linear operations. It is demonstrated that this indicator transform helps us achieve the desired generality which is a shortcoming of EnKF. Because the expected value of indicators directly yield the probability corresponding to an outcome, the method can be used to quantify the residual uncertainty in spatial description of reservoir properties. Because at all steps of the process an ensemble of models is available, so quantification of residual uncertainty in prediction forecasts is possible. Another advantage is that the data assimilation is sequential in nature implying that the updates can be performed in a quasi-real time sense as data becomes available.
Author: Mingliang Liu
Author: Mingliang Liu
Author: Alexandre Emerick
Author: Behnam Jafarpour
Author: Y. Zee Ma
Author: Dario Grana
Author: Seonkyoo Yoon
Author: Esmail Mohamed Khalil Eltahan
Author: Reza Yousefzadeh
Author: Siavash Nejadi
Author: Devesh Kumar