Deterministic ensemble smoother with multiple data assimilation as an alternative for history-matching seismic data

This paper reports the results of an investigation on the use of a deterministic analysis scheme combined with the method ensemble smoother with multiple data assimilation (ES-MDA) for the problem of assimilating a large number of correlated data points. This is the typical case when history-matching time-lapse seismic data in petroleum reservoir models. The motivation for the use of the deterministic analysis is twofold. First, it tends to result in a smaller underestimation of the ensemble variance after data assimilation. This is particularly important for problems with a large number of measurements. Second, the deterministic analysis avoids the factorization of a large covariance matrix required in the standard implementation of ES-MDA with the perturbed observations scheme. The deterministic analysis is tested in a synthetic history-matching problem to assimilate production and seismic data.     

Large-scale stochastic linear inversion using hierarchical matrices

Stochastic inverse modeling deals with the estimation of functions from sparse data, which is a problem with a nonunique solution, with the objective to evaluate best estimates, measures of uncertainty, and sets of solutions that are consistent with the data. As finer resolutions become desirable, the computational requirements increase dramatically when using conventional solvers. A method is developed in this paper to solve large-scale stochastic linear inverse problems, based on the hierarchical matrix (or ? ² matrix) approach. The proposed approach can also exploit the sparsity of the underlying measurement operator, which relates observations to unknowns. Conventional direct algorithms for solving large-scale linear inverse problems, using stochastic linear inversion techniques, typically scale as ??(n ² m+nm ²), where n is the number of measurements and m is the number of unknowns. We typically have n ? m. In contrast, the algorithm presented here scales as ??(n ² m), i.e., it scales linearly with the larger problem dimension m. The algorithm also allows quantification of uncertainty in the solution at a computational cost that also grows only linearly in the number of unknowns. The speedup gained is significant since the number of unknowns m is often large. The effectiveness of the algorithm is demonstrated by solving a realistic crosswell tomography problem by formulating it as a stochastic linear inverse problem. In the case of the crosswell tomography problem, the sparsity of the measurement operator allows us to further reduce the cost of our proposed algorithm from ??(n ² m) to $\mathcal {O}(n^{2} \sqrt {m} + nm)$ . The computational speedup gained by using the new algorithm makes it easier, among other things, to optimize the location of sources and receivers, by minimizing the mean square error of the estimation. Without this fast algorithm, this optimization would be computationally impractical using conventional methods.     

Maximum likelihood classification for facies inference from reservoir attributes

A cluster analysis methodology is developed to recover facies realizations from observed reservoir attributes. A maximum likelihood estimator allows us for identifying the most probable underlying facies using a spatial clustering algorithm. In seismic characterization, this algorithm can yield relevant geological models for subsequent history-matching studies. In history-matching procedures, it provides informative facies maps as well as starting points for further studies.     

改进的模拟退火-变尺度优化方法在测井解释中的应用

实际中的测井参数反演是一个多参数、非线性优化问题,所采用的目标函数,即度量由参数化的理论模型得出的预测值与观测值的吻合程度,往往存在多解的现象。针对这种状况我们提出了模拟退火与变尺度综合反演方法用于参数计算,经过实际试算证明该方法效果很好。     

Hybridization of the probability perturbation method with gradient information

Geostatistically based history-matching methods make it possible to devise history-matching strategies that will honor geologic knowledge about the reservoir. However, the performance of these methods is known to be impeded by slow convergence rates resulting from the stochastic nature of the algorithm. It is the purpose of this paper to introduce a method that integrates qualitative gradient information into the probability perturbation method to improve convergence. The potential of the proposed method is demonstrated on a synthetic history-matching example. The results indicate that inclusion of qualitative gradient information improves the performance of the probability perturbation method.     

单纯形—模拟退火混合算法反求水文地质参数及其并行求解

本文利用单纯形法局部搜索速度快和模拟退火算法全局寻优的特点,同时为了克服各自算法的弊病,提出采用单纯形—模拟退火混合算法(SMSA)进行水文地质逆问题的求解。论文详细描述了SMSA算法的具体操作算子的实现,并将该算法应用于一个大型水源地的地下水模拟反演。计算结果表明,SMSA算法在水文地质参数反演计算具有求解速度快,精度高的特点,而且易于实现并行运算。     

Non-parametric Bayesian networks for parameter estimation in reservoir simulation: a graphical take on the ensemble Kalman filter (part I)

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.     

Evaluation of petroleum hydrocarbon biodegradation in shallow groundwater by hydrogeochemical indicators and C, S-isotopes

Biodegradation is one of the main natural attenuation processes in groundwater contaminated with petroleum hydrocarbons. In this work, preliminary studies have been carried out by analyzing the concentrations of total petroleum hydrocarbons (TPH), dissolved inorganic carbon (DIC), dominant terminal electron accepters or donors, as well as δ ¹³C_DIC and δ ³⁴S_SO4, to reveal the biodegradation mechanism of petroleum hydrocarbons in a contaminated site. The results show that along groundwater flow in the central line of the plume, the concentrations of electron acceptors, pH, and E _h increased but TPH and DIC decreased. The δ ¹³C_DIC values of the contaminated groundwater were in the range of ?14.02 to ?22.28 ‰_PDB and ?7.71 to 8.36 ‰_PDB, which reflected a significant depletion and enrichment of ¹³C, respectively. The increase of DIC is believed to result from the non-methanogenic and methanogenic biodegradation of petroleum hydrocarbon in groundwater. Meanwhile, from the contaminated source to the downgradient of the plume, the ³⁴S in the contaminated groundwater became more depleted. The Rayleigh model calculation confirmed the occurrence of bacterial sulfate reduction as a biodegradation pathway of the petroleum hydrocarbon in the contaminated aquifers. It was concluded that stable isotope measurements, combined with other biogeochemical measurements, can be a useful tool to prove the occurrence of the biodegradation process and to identify the dominant terminal electron-accepting process in contaminated aquifers.     

水环境逆边界逆动态混合控制精确算法

根据逆边界逆动态控制理论,将河流水污染动态控制问题提为逆边界逆动态混合控制问题。针对多个或单个污染源排放浓度和排放总量计算,提出了一维对流-扩散方程逆控制的精确算法。该方法与现行最优控制方法相比,其优点是充分考虑了河流沿程的稀释混合容量,并能充分考虑水质动态标准和社会经济变化等因素,可获得动态控制精确解的近似解。     

Gaussian Processes for history-matching: application to an unconventional gas reservoir

The process of reservoir history-matching is a costly task. Many available history-matching algorithms either fail to perform such a task or they require a large number of simulation runs. To overcome such struggles, we apply the Gaussian Process (GP) modeling technique to approximate the costly objective functions and to expedite finding the global optima. A GP model is a proxy, which is employed to model the input-output relationships by assuming a multi-Gaussian distribution on the output values. An infill criterion is used in conjunction with a GP model to help sequentially add the samples with potentially lower outputs. The IC fault model is used to compare the efficiency of GP-based optimization method with other typical optimization methods for minimizing the objective function. In this paper, we present the applicability of using a GP modeling approach for reservoir history-matching problems, which is exemplified by numerical analysis of production data from a horizontal multi-stage fractured tight gas condensate well. The results for the case that is studied here show a quick convergence to the lowest objective values in less than 100 simulations for this 20-dimensional problem. This amounts to an almost 10 times faster performance compared to the Differential Evolution (DE) algorithm that is also known to be a powerful optimization technique. The sensitivities are conducted to explain the performance of the GP-based optimization technique with various correlation functions.     

Randomized maximum likelihood based posterior sampling

Minimization of a stochastic cost function is commonly used for approximate sampling in high-dimensional Bayesian inverse problems with Gaussian prior distributions and multimodal posterior distributions. The density of the samples generated by minimization is not the desired target density, unless the observation operator is linear, but the distribution of samples is useful as a proposal density for importance sampling or for Markov chain Monte Carlo methods. In this paper, we focus on applications to sampling from multimodal posterior distributions in high dimensions. We first show that sampling from multimodal distributions is improved by computing all critical points instead of only minimizers of the objective function. For applications to high-dimensional geoscience inverse problems, we demonstrate an efficient approximate weighting that uses a low-rank Gauss-Newton approximation of the determinant of the Jacobian. The method is applied to two toy problems with known posterior distributions and a Darcy flow problem with multiple modes in the posterior.

     

A new solid state diffusion model applied to inverse zoning and diffusion rims in minerals

Any oxide and silicate mineral which is nominally anhydrous but crystallized in the presence of H₂O incorporates traces of H₂O in solid solution. In the case of MgO it can be shown that OH^? pairs convert into H₂+O ₂ ^2? . If the H₂ molecules are lost, the O ₂ ^2? remain in the lattice as excess oxygen stabilized by excess cation vacancies. When the O ₂ ^2? anions decay either thermally or by decompression unbound O^? states (positive holes) are generated which lead to surface charges and subsurface space charge layers. Calculated space charge profiles are presented. O^? concentrations as small as 10–20 ppm suffice to create electric surface fields of the order of 4·10⁷ V·m^?1. The diffusion mechanism which derives from these premises incorporates novel features: the cation diffusion is coupled to the counterdiffusion of unbound and vacancy-bound O^? states. The cation diffusion is predicted to be very fast because first, it is field-enhanced (electrochemically driven) and second, it is not rate-limited by the intrinsic cation vacancy concentration nor by the counter-diffusion of other cations. The model may apply to cases of inverse zoning and diffusion rim formation in minerals under certain P-T conditions.     

A multiscale method for distributed parameter estimation with application to reservoir history matching

A method for multiscale parameter estimation with application to reservoir history matching is presented. Starting from a given fine-scale model, coarser models are generated using a global upscaling technique where the coarse models are tuned to match the solution of the fine model. Conditioning to dynamic data is done by history-matching the coarse model. Using consistently the same resolution both for the forward and inverse problems, this model is successively refined using a combination of downscaling and history matching until model-matching dynamic data are obtained at the finest scale. Large-scale corrections are obtained using fast models, which, combined with a downscaling procedure, provide a better initial model for the final adjustment on the fine scale. The result is thus a series of models with different resolution, all matching history as good as possible with this grid. Numerical examples show that this method may significantly reduce the computational effort and/or improve the quality of the solution when achieving a fine-scale match as compared to history-matching directly on the fine scale.     

Multiscale Parameterization of Petrophysical Properties for Efficient History-Matching

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.     

Stability of earthquake moment tensor inversions: effect of the double-couple constraint

We show that spurious large non-double-couple components can be obtained in inversions for the full deviatoric moment tensor for shallow crustal earthquakes due to inaccurate Earth models. The traditional “best double-couple” solution does not in general provide an optimal estimate of a double-couple mechanism, and is only reliable when the non-double-couple component of the full deviatoric solution is small. The inverse problem for the moment tensors of the 1998 Antarctic Plate and 2000 Wharton Basin strike-slip earthquakes is shown in each case to have two well-fitting minima in the misfit function of pure double-couple solutions. Such pairs of solutions are most likely to exist for earthquakes which are close either to vertical strike-slip or to dip-slip on a fault plane dipping at 45°. It is shown theoretically that these pairs of solutions arise from the combination of the pure double-couple constraint and the instability of two elements of the moment tensor. No significant non-double-couple component is found for the shallow thrusting 1996 Biak, Indonesia earthquake.     

Assessment of using borehole-closure data to determine the constitutive behavior of salt

Borehole closure data have often been used to determine the constitutive behaviour of rock salt. This is an inverse problem and analysis of the data is confused by the fact that borehole closure is a structural response that does not uniquely determine constitutive behaviour since both the stress and deformation depend on the constitutive behaviour. This application brief assesses the conclusion of a recent study that the transient power law, ? = K τ^mtⁿ, which includes no term to account for steady-state deformation, is a better constitutive model for salt than are models that account for steady-state straining, and that an empirical model developed from the borehole closure data could be extrapolated to give conservative predictions of long-term closure of single or widely spaced openings.     

Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data

This study concerns the identification of parameters of soil constitutive models from geotechnical measurements by inverse analysis. To deal with the non‐uniqueness of the solution, the inverse analysis is based on a genetic algorithm (GA) optimization process. For a given uncertainty on the measurements, the GA identifies a set of solutions. A statistical method based on a principal component analysis (PCA) is, then, proposed to evaluate the representativeness of this set. It is shown that this representativeness is controlled by the GA population size for which an optimal value can be defined. The PCA also gives a first‐order approximation of the solution set of the inverse problem as an ellipsoid. These developments are first made on a synthetic excavation problem and on a pressuremeter test. Some experimental applications are, then, studied in a companion paper, to show the reliability of the method. Copyright © 2009 John Wiley & Sons, Ltd.     

应用改进并行遗传算法反求水文地质参数研究

利用单纯形法局部搜索速度快和遗传算法全局寻优的特点,同时为了克服两种方法各自的弊病,提出采用混合单纯形技术的并行遗传算法(Hybrid Simplex Parallel Genetic Algorithm,SPGA)进行水文地质逆问题的求解。详细阐述了SPGA算法的具体操作和实现,并将该方法应用于水源地的地下水模拟反演中。计算结果表明,SPGA算法在水文地质参数寻优计算中具有比较好的可靠性和计算效率。