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Soares, Ricardo Vasconcellos (NORCE Norwegian Research Centre and University of Bergen) | Luo, Xiaodong (NORCE Norwegian Research Centre) | Evensen, Geir (NORCE Norwegian Research Centre and Nansen Environmental and Remote Sensing Center (NERSC)) | Bhakta, Tuhin (NORCE Norwegian Research Centre)
Summary In applications of ensemble-based history matching, it is common to conduct Kalman gain or covariance localization to mitigate spurious correlations and excessive variability reduction resulting from the use of relatively small ensembles. Another alternative strategy not very well explored in reservoir applications is to apply a local analysis scheme, which consists of defining a smaller group of local model variables and observed data (observations), and perform history matching within each group individually. This work aims to demonstrate the practical advantages of a new local analysis scheme over the Kalman gain localization in a 4D seismic history-matching problem that involves big seismic data sets. In the proposed local analysis scheme, we use a correlation-based adaptive data-selection strategy to choose observations for the update of each group of local model variables. Compared to the Kalman gain localization scheme, the proposed local analysis scheme has an improved capacity in handling big models and big data sets, especially in terms of computer memory required to store relevant matrices involved in ensemble-based history-matching algorithms. In addition, we show that despite the need for a higher computational cost to perform model update per iteration step, the proposed local analysis scheme makes the ensemble-based history-matching algorithm converge faster, rendering the same level of data mismatch values at a faster pace. Meanwhile, with the same numbers of iteration steps, the ensemble-based history-matching algorithm equipped with the proposed local analysis scheme tends to yield better qualities for the estimated reservoir models than that with a Kalman gain localization scheme. As such, the proposed adaptive local analysis scheme has the potential of facilitating wider applications of ensemble-based algorithms to practical large-scale history-matching problems.
Silva Neto, Gilson Moura (Petrobras, University of Campinas, and NORCE Norwegian Research Centre) | Soares, Ricardo Vasconcellos (NORCE Norwegian Research Centre, University of Bergen) | Evensen, Geir (NORCE Norwegian Research Centre and Nansen Environmental and Remote Sensing Center) | Davolio, Alessandra (University of Campinas) | Schiozer, Denis José (University of Campinas)
Summary Time-lapse-seismic-data assimilation has been drawing the reservoir-engineering community's attention over the past few years. One of the advantages of including this kind of data to improve the reservoir-flow models is that it provides complementary information compared with the wells' production data. Ensemble-based methods are some of the standard tools used to calibrate reservoir models using time-lapse seismic data. One of the drawbacks of assimilating time-lapse seismic data involves the large data sets, mainly for large reservoir models. This situation leads to high-dimensional problems that demand significant computational resources to process and store the matrices when using conventional and straightforward methods. Another known issue associated with the ensemble-based methods is the limited ensemble sizes, which cause spurious correlations between the data and the parameters and limit the degrees of freedom. In this work, we propose a data-assimilation scheme using an efficient implementation of the subspace ensemble randomized maximum likelihood (SEnRML) method with local analysis. This method reduces the computational requirements for assimilating large data sets because the number of operations scales linearly with the number of observed data points. Furthermore, by implementing it with local analysis, we reduce the memory requirements at each update step and mitigate the effects of the limited ensemble sizes. We test two local analysis approaches: one distance-based approach and one correlation-based approach. We apply these implementations to two synthetic time-lapse-seismic-data-assimilation cases, one 2D example, and one field-scale application that mimics some of the real-field challenges. We compare the results with reference solutions and with the known ensemble smoother with multiple data assimilation (ES-MDA) using Kalman gain distance-based localization. The results show that our method can efficiently assimilate time-lapse seismic data, leading to updated models that are comparable with other straightforward methods. The correlation-based local analysis approach provided results similar to the distance-based approach, with the advantage that the former can be applied to data and parameters that do not have specific spatial positions.
A site-specific rock mass classification scheme was developed as part of the geotechnical engineering design for a new build nuclear power station in the UK. A robust and explicit classification scheme was used to interpret and classify all the available data, not just that from the most recent investigations, in a clear, unambiguous and efficient manner. To achieve this objective, the scheme had to be sufficiently simple that it could be applied to historical boreholes. This paper describes the process of selecting the input parameters for the classification scheme; the pilot study that was then undertaken to test its robustness and utility for geotechnical design; and the results of its application for the whole site. Two input parameters were selected: fracture spacing and weathering grade. Using these parameters, the bedrock was divided into five groups. The rock mass classification was then used to provide Geological Strength Index values. It was also viewed in a three-dimensional model, enabling easy identification and correlation of faults.
This paper presents the development and application of a site-specific rock mass classification scheme as part of the geotechnical engineering design for Horizon Nuclear Power's Wylfa Newydd new build nuclear power station in the UK. It describes the rationale for developing a site-specific classification scheme and the process of selecting the input parameters during the scheme's development. A pilot study was undertaken to test the classification scheme's robustness and utility for geotechnical design. This paper describes the implementation of the scheme, advantages and limitations of the classification, and how it could be used for other projects.
The proposed Wylfa Newydd power station site is located on the northern coast of Anglesey, northwest Wales, as shown in Figures 1 and 2.
The site is located in a geologically complex area; superficial deposits, predominantly of Quaternary glacial origin, overlie metamorphic bedrock belonging to the Monian Supergroup of Late Pre-Cambrian and Early Cambrian age (British Geological Survey, 2014). The site is crossed by igneous intrusions and faults of varying persistence and orientation.
Chen, Yung-Wei (National Taiwan Ocean University) | Shih, Chao-Feng (National Taiwan Ocean University) | Liu, Yu-Chen (National Taiwan Ocean University) | Soon, Shih-Ping (National Taiwan Ocean University)
This paper presents an equal-norm multiple-scale Trefftz method (MSTM) associated with the group-preserving schemes (GPS) to tackle some difficulties in nonlinear sloshing behaviors. The MSTM combined with the vector regularization method is first adopted to eliminate the higher-order numerical oscillation phenomena and noisy dissipation in the boundary value problem. Then, the weighting factors of initial and boundary value problems are introduced into the linear system to prevent the elevation from vanishing without iterative computational controlled volume. More important, the explicit scheme, based on the GL (n, R), and the implicit scheme can be combined to reduce iteration number and increase computational efficiency. A comparison of the results shows that the proposed approach is better than previously reported methods.
Sloshing of liquid in tanks has received considerable attention from many researchers in related engineering fields. The problem arises because excessive sloshing of the confined liquid can strongly damage the structure or the loads induced by sloshing, which may seriously modify the dynamics of the vehicle that supports the tanks—for example, fuel sloshing in liquid propellant launch vehicles (Lu et al., 2015), oil oscillations in large storage tanks as a result of long-period strong ground motions (Hashimoto et al., 2017), and sloshing in nuclear fuel pools owing to earthquakes (Eswaran and Reddy, 2016). Besides, sloshing effects in the ballast tanks of a ship may cause it to experience large rolling moments and eventually capsize because of loss of dynamic stability (Krata, 2013; Sanapala et al., 2018). Also, if the forcing frequency coincides with the natural sloshing frequency, the high dynamic pressures, by reason of resonance, may damage the tank walls. Thus, accurate prediction of sloshing behaviors in tanks driven by external forces is very critical for successful structural design and reducing impacts on vehicle maneuvering.