[en] For several decades, the French Atomic and Alternative Energies Commission – CEA - has been undertaking experimental programs aimed at validating the calculation tools used to design standard and advanced LWRs, as well as FBRs

[en] History matching a channelized reservoir with multiple facies has always posed a great challenge to researchers. In this paper, we present a workflow combining the ensemble smoother with multiple data assimilation (ES-MDA) method with a parameterization algorithm referred to as the common basis discrete cosine transform (DCT) and a post-processing technique in order to integrate static and dynamic data into multi-facies channelized reservoir models. The parameterization algorithm is developed to capture the critical features and describe the geological similarity between different realizations in the prior ensemble by transforming the discrete facies indicators into continuous variables. And the ES-MDA method is employed to update the continuous variables by assimilating the static and dynamic data. Finally, a post-processing technique based on a regularization framework is used to improve the spatial continuity of facies and estimate the non-Gaussian distributed reservoir properties. We apply this automatic history matching workflow to two synthetic problems that represent complex three-facies (shale, levee, and sand) channelized reservoirs. One is a 2D three-facies reservoir with a relatively high number of channels and the other is a 3D three-facies five-layer reservoir containing two geological zones with different channel patterns. The computational results show that the proposed workflow can greatly reduce the uncertainty in the reservoir description through the integration of production data. And the posterior realizations can well preserve the key geological features of the prior models, with a good history data match and predictive capacity. In addition, we also illustrate the superiority of the common basis DCT over the traditional DCT algorithm.

[en] Meteorological observations in Tibet are poor in quality with a severe amount of missing data; this is mostly caused by extreme climatological conditions and higher maintenance costs. This paper focuses on the imputation of missing data and the reconstruction of the regional temperature field. Due to insufficient observation stations and complicated topography, we employ the weather research and forecasting (WRF) model to produce the proper orthogonal decomposition (POD) basis for the study region. We then develop the gappy POD method for the imputation of missing data. Both methods are compared and tested for various missing data cases, and the results show that the gappy POD method dramatically outperforms the regularized EM algorithm when the amount of missing spatial data is not severe. Furthermore, between the two methods, only the gappy POD method is capable of reconstructing the temperature field at locations where the data are absent. The gappy POD method can also be generalized for data assimilation with the assumption that the data across all model grids have missing values.

[en] The Aerosol Radiance Assimilation Study (ARAS) has created a new approach for the assimilation of visible/near-IR radiances into the ECMWF’s Integrated Forecast System (IFS) for the constraining aerosol properties within the model. The capability is based on a new observation operator, based on the forward model used in the Optimal Retrieval of Aerosol and Cloud (ORAC) retrieval scheme, which predicts top-of-atmosphere radiances based on the model's aerosol field with sufficient accuracy while being computationally efficient enough to run in a operational analysis system such as that run at ECMWF. The system has been tested in the full IFS assimilation system, replacing the currently operational assimilation of MODIS AOD products, using MODIS radiances. This presentation will give an overview of the new operator, show example results of its impact on the model output and discuss its merits and disadvantages compared to the AOD assimilation.

[en] Drought is a major limiting factor affecting wheat production in the world. We aimed to study the effect of soil water deficit on dry matter remobilization (DMR), grain yield (GY) and yield components of durum and bread wheat genotypes. Drought stress accelerated DMR. Lowest remobilization of dry matter into grains was detected in the tallest, late heading genotypes, which were also characterized by low harvest index (HI). Drought stress showed less affect on plant height (PH), peduncle length (PL), spike length (SL), spike width (SW), spikelet number per spike (SNS) but strongly affected the biological yield (BY), spike mass (SM), grain number per spike (GNS) and grain mass per spike (GMS), thousand kernels mass (TKM). GY positively and significantly correlated with spikes m/sup -2/ (SN), BY and HI under drought stress condition. We consider that wheat characteristics DMR, SN, BY, HI are good selection criteria under drought stress. (author)

[en] It is well known that the true values of measured and computed data are impossible to know exactly because of various uncontrollable errors and uncertainties arising in the data measurement and interpretation reduction processes. Hence, all inferences, predictions, engineering computations, and other applications of measured and/or computed data are necessarily based on weighted averages over the possibly true values, with weights indicating the degree of plausibility of each value. Furthermore, combination of data from different sources involves a weighted propagation (e.g., via sensitivities) of all uncertainties, requiring reasoning from incomplete information and using probability theory for extracting optimal values together with 'best-estimate' uncertainties from often sparse, incomplete, error-afflicted, and occasionally discrepant data. The current state-of-the-art data assimilation/model calibration methodologies1 for large-scale nonlinear systems cannot take into account uncertainties higher-order than secondorder (i.e., covariances) thereby failing to quantify fully the deviations of the problem under consideration from a normal (Gaussian) multivariate distribution. Such deviations would be quantified by the third- and fourth-order moments (skewness and kurtosis) of the model's predicted results (responses). These higher-order moments would be constructed by combining modeling and experimental uncertainties (which also incorporate the corresponding skewness and kurtosis information), using derivatives of the model responses with respect to the model's parameters. This paper presents explicit expressions for skewness and kurtosis of computed responses, thereby permitting quantification of the deviations of the computed response uncertainties from multivariate normality. In addition, this paper presents a new and most efficient procedure for computing the second-order response derivatives with respect to model parameters using the 'adjoint sensitivity analysis procedure' (ASAP)

[en] In the strong-constraint formulation of the history-matching problem, we assume that all the model errors relate to a selection of uncertain model input parameters. One does not account for additional model errors that could result from, e.g., excluded uncertain parameters, neglected physics in the model formulation, the use of an approximate model forcing, or discretization errors resulting from numerical approximations. If parameters with significant uncertainties are unaccounted for, there is a risk for an unphysical update, of some uncertain parameters, that compensates for errors in the omitted parameters. This paper gives the theoretical foundation for introducing model errors in ensemble methods for history matching. In particular, we explain procedures for practically including model errors in iterative ensemble smoothers like ESMDA and IES, and we demonstrate the impact of adding (or neglecting) model errors in the parameter-estimation problem. Also, we present a new result regarding the consistency of using the sample covariance of the predicted nonlinear measurements in the update schemes.

[en] Highlights: • Closed-loop field development optimization extended to subsurface reservoirs described by multipoint geostatistics. • TruMAP procedure presented for performance assessment of closed-loop optimization in terms of true NPV improvement. • Results involve a massive computational experiment that took 320,000 CPU-hours (about 9.5 million reservoir simulation runs). • Results show that consecutive steps of closed-loop optimization improve the probability of increasing true-model NPV. • The new CLFD implementation improved the true NPV in 96% of cases. -- Abstract: Closed-loop field development (CLFD) optimization is a comprehensive framework for optimal development of subsurface resources. CLFD involves three major steps: 1) optimization of full development plan based on current set of models, 2) drilling new wells and collecting new spatial and temporal (production) data, 3) model calibration based on all data. This process is repeated until the optimal number of wells is drilled. This work introduces a new CLFD implementation for complex systems described by multipoint geostatistics (MPS). Model calibration is accomplished in two steps: conditioning to spatial data by a geostatistical simulation method, and conditioning to production data by optimization-based PCA. A statistical procedure (TruMAP) is presented to assess the performance of CLFD. For performance assessment by TruMAP, the methodology is applied to an oil reservoir example for 25 different true-model cases. Application of a single-step of CLFD, improved the true NPV in 64%–80% of cases. The full CLFD procedure (with three steps) improved the true NPV in 96% of cases, with an average improvement of 37%. These results indicate that probability of improving true NPV increases with closed-loop step. This massive computational experiment involved about 9.5 million reservoir simulation runs that took about 320,000 CPU hours.

No abstract available

[en] Highlights: • Integral experiments. • Nuclear data. • Data assimilation. • Uncertainty quantification. • Marginal likelihood optimization. When adjusting nuclear data with integral experiments, care must be taken that spurious adjustments are not made by assimilating poorly characterized integral parameters. If there are unaccounted for biases or poorly estimated uncertainties in the calculated and experimental values for an integral parameter, the Bayesian data assimilation may adjust the nuclear data in a manner that does not reflect the physics of the integral parameter. To identify and lessen the impact of these inconsistent integral parameters, we present a Marginal Likelihood Optimization algorithm. In a data-driven way, the marginalized likelihood is used to modulate hyperparameter terms that decrease the influence of inconsistent integral parameters on the adjustment. The advantage of this approach over other methods in the literature is that it incorporates correlation information and does not remove an integral parameter from the adjustment. Herein, we present and motivate the algorithm, and apply it to an integral data assimilation case study.