Day 1 Tue, March 28, 2023最新文献

{"title":"Parameter Inversion in Geothermal Reservoir Using Markov Chain Monte Carlo and Deep Learning","authors":"Zhen Zhang, Xupeng He, Yiteng Li, M. AlSinan, H. Kwak, H. Hoteit","doi":"10.2118/212185-ms","DOIUrl":"https://doi.org/10.2118/212185-ms","url":null,"abstract":"\u0000 Traditional history-matching process suffers from non-uniqueness solutions, subsurface uncertainties, and high computational cost. This work proposes a robust history-matching workflow utilizing the Bayesian Markov Chain Monte Carlo (MCMC) and Bidirectional Long-Short Term Memory (BiLSTM) network to perform history matching under uncertainties for geothermal resource development efficiently. There are mainly four steps. Step 1: Identifying uncertainty parameters. Step 2: The BiLSTM is built to map the nonlinear relationship between the key uncertainty parameters (e.g., injection rates, reservoir temperature, etc.) and time series outputs (temperature of producer). Bayesian optimization is used to automate the tuning process of the hyper-parameters. Step 3: The Bayesian MCMC is performed to inverse the uncertainty parameters. The BiLSTM is served as the forward model to reduce the computational expense. Step 4: If the errors of the predicted response between the high-fidelity model and Bayesian MCMC are high, we need to revisit the accuracy of the BiLSTM and the prior information on the uncertainty parameters. We demonstrate the proposed method using a 3D fractured geothermal reservoir, where the cold water is injected into a geothermal reservoir, and the energy is extracted by producing hot water in a producer. Results show that the proposed Bayesian MCMC and BiLSTM method can successfully inverse the uncertainty parameters with narrow uncertainties by comparing the inversed parameters and the ground truth. We then compare its superiority with models like PCE, Kriging, and SVR, and our method achieves the highest accuracy. We propose a Bayesian MCMC and BiLSTM-based history matching method for uncertainty parameters inversion and demonstrate its accuracy and robustness compared with other models. This approach provides an efficient and practical history-matching method for geothermal extraction with significant uncertainties.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"2000 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134475124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing a Hardware Agnostic Commercial Black-Oil Reservoir Simulator","authors":"M. D. E. Szyndel, Christopher Lemon, Daniel de Brito Dias, Eamon Dodds, Eduard Khramchenkov, Simone Rinco, Soham Sheth, M. Tene, Choongyong Han, Xundan Shi, Christian Wolfsteiner, H. Cao, Terrence Liao, Michael Sekachev, Rustem Zaydullin","doi":"10.2118/212205-ms","DOIUrl":"https://doi.org/10.2118/212205-ms","url":null,"abstract":"\u0000 Commercial reservoir simulators have traditionally been optimized for parallel computations on central processing units (CPUs). The recent advances in general-purpose graphics processing units (GPUs) have provided a powerful alternative to CPU, presenting an opportunity to significantly reduce run times for simulations. Realizing peak performance on GPU requires that GPU-specific code be written, and also requires that data are laid out sympathetically to the hardware. The cost of copying data between the CPU memory and GPU memory at the time of this writing is egregious. Peak performance will only be realized if this is minimized.\u0000 In paper Cao et al., 2021, the authors establish approaches to enable a simulator to give excellent performance on a CPU or GPU, with the same simulation result using either hardware. We discuss how their prototype was generalized into high-quality, maintainable code with applicability across a wide range of models.\u0000 Different parts of a reservoir simulator benefit from different approaches. A modern, object-oriented simulator requires components to handle initialization, property calculation, linearization, linear solver, well and aquifer calculations, field management, and reporting. Each of these areas will present architectural challenges when broadening the scope of the simulator from CPU only to supporting CPU or GPU. We outline these challenges and present the approaches taken to address them. In particular, we discuss the importance of abstracting compute scheduling, testing methods, data storage classes, and associated memory management to a generic framework layer.\u0000 We have created a high-quality reservoir simulator with the capacity to run on a CPU or GPU with results that match to within a very small tolerance. We present software engineering approaches that enable the team to achieve and maintain this in the future. In addition, we present test outcomes and discuss how to achieve excellent performance.\u0000 To our knowledge, no simulator capable of both CPU simulation and full GPU simulation (meaning simulation with no copies of full grid-size data for purposes other than reporting) has been presented. We will present novel software approaches used to implement the first such commercial simulator.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131631937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Clustering Techniques for Automated Lumping of Components in Compositional Models","authors":"M. Cancelliere, J. A. Saint Antonin","doi":"10.2118/212211-ms","DOIUrl":"https://doi.org/10.2118/212211-ms","url":null,"abstract":"\u0000 Compositional simulation run times grow significantly as we increase the number of components used to characterize our fluid (SPE 69575). Therefore, having usable and practical models requires that we minimize the number of components without sacrificing prediction accuracy. In this paper we validate a novel approach that automates the compositional lumping as a part of the simulator pre-processing and allows quick evaluation of the impact on results and run-times of reducing the number of components in actual simulation runs.\u0000 Different clustering techniques such as K-means or Agglomerative are applied on five different compositions from the literature which typically would require compositional modelling (gas condensate to volatile oil). The performance of these lumped compositions obtained from clustering are compared with an exhaustive brute-search lumping and the original full composition. These comparisons are made by simulating classical CCE & DLE or CVD lab experiments. The results are quantitatively assessed for proximity to the full composition simulation. With the techniques already validated, a preprocessor is developed that allows the user to input a full composition and set the number of components to be used for the run. These heuristic clustering techniques provide excellent results with minimal time. Although brute-force search may occasionally deliver marginally better outcomes, it does so at immense computational costs and any advantage vanishes after regression.\u0000 To the best of our knowledge, advanced clustering techniques have not previously been applied to the problem of lumping as the industry has relied mostly on theoretical or empirical arguments to prescribe the lumping approach, to be carried out manually, with the occasional study on brute force search (SPE-170912). An additional novelty is to automate compositional lumping in the simulator preprocessor, allowing for accelerated validation of the lumping approach under the expected reservoir conditions. The speed and flexibility of the approach makes it an excellent practical option to test and scale the number of components used in compositional models.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116600809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reservoir Connectivity Identification and Robust Production Forecasting Using Physics Informed Machine Learning","authors":"M. Nagao, A. Datta-Gupta, Tsubasa Onishi, S. Sankaran","doi":"10.2118/212201-ms","DOIUrl":"https://doi.org/10.2118/212201-ms","url":null,"abstract":"\u0000 Routine well-wise injection/production data contain significant information which can be used for closed-loop reservoir management and rapid field decisions. Traditional physics-based numerical reservoir simulation can be computationally prohibitive for short-term decision cycles, and also requires detailed geologic model. Reduced physics models provide an efficient simulator free workflow, but often have a limited range of applicability. Pure machine learning models lack physical interpretability and can have limited predictive power. We propose a hybrid machine learning and physics-based approach for rapid production forecasting and reservoir connectivity characterization using routine injection/production and pressure data.\u0000 Our framework takes routine measurements such as injection rate and pressure data as input and multiphase production rates as output. We combine reduced physics models into a neural network architecture by utilizing two different approaches. In the first approach, the reduced physics model is used for pre-processing to obtain approximate solutions that feed it into a neural network as input. This physics-based input feature can reduce the model complexity and provide significant improvement in prediction performance. The second approach augments the residual terms in the neural network loss function with physics-based regularization that relies on the governing partial differential equations (PDE). Reduced physics models are used for the governing PDE to enable efficient neural network training. The regularization allows the model to avoid overfitting and provides better predictive performance.\u0000 Our proposed hybrid models are first validated using a 2D benchmark reservoir simulation case and then applied to a field-scale reservoir case to show the robustness and efficiency of the method. The hybrid models are shown to provide superior prediction performance than pure machine learning models and reduced physics models in terms of multiphase production rates. Specifically, in the second method, the trained hybrid neural network model satisfies the reduced physics model, making it physically interpretable, and provides inter-well connectivity in terms of well flux allocation. The flux allocation estimated from the hybrid model was compared with streamline-based flux allocation, and excellent agreement was obtained. By combining the reduced physics model with the efficacy of deep learning, model calibration can be done very efficiently without constructing a geologic model.\u0000 The proposed hybrid models with physics-based regularization and preprocessing provide novel approaches to augment data-driven models with underlying physics to build interpretable models for understanding reservoir connectivity between wells and robust future production forecasting.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127649908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A GPU-Accelerated Simulator for Challenging Extreme-Scale Geomechanical Models","authors":"Matteo Frigo, G. Isotton, C. Janna, N. Spiezia, M. Ferronato, A. Franceschini, Annachiara Filippini, G. Scrofani","doi":"10.2118/212198-ms","DOIUrl":"https://doi.org/10.2118/212198-ms","url":null,"abstract":"Simulation software is a very common tool to model geomechanical problems since direct measurements are extremely expensive and usually unfeasible. In addition, there is an increasing interest in simulating past events and forecasting future ones. Very fine meshes are needed to provide a realistic representation of complex stratigraphy. Hence the full exploitation of modern HPC infrastructure is mandatory. In this work, a fully parallel GPU-accelerated simulator for extreme-scale models is presented and its performance is assessed through a real basin scale model that is used as benchmark.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125554665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graphics Processing Unit Performance Scalability Study on a Commercial Black-Oil Reservoir Simulator","authors":"M. Tene, M. Sekachev, Daniel de Brito Dias, M. D. E. Szyndel","doi":"10.2118/212183-ms","DOIUrl":"https://doi.org/10.2118/212183-ms","url":null,"abstract":"\u0000 Commercial reservoir simulators have traditionally been optimized for distributed parallel execution on Central Processing Units (CPUs). Recent advances in Graphics Processing Units (GPUs) have led to the development of GPU-native simulators and triggered a shift towards a hardware-agnostic design in existing CPU solutions. For the latter, the suite of algorithms and data structures employed for a given computation are implemented for each target device. This results in a hybrid approach, where some simulator components inherently expose enough instruction parallelism or memory bandwidth requirements to warrant running on the GPU, while others are more suitable for the CPU. This paper examines the performance characteristics of a commercial black-oil reservoir simulator, which was recently extended with GPU support.\u0000 Each simulation case will distribute load on the various modules in a reservoir simulator differently, depending on the target physical properties and the forecasted data desired. To assess this, the scalability of the simulator is measured in detail using the CPU and GPU, for components where both implementations are available, focusing on time spent during model initialization, property calculation, linearization, solver, field management and reporting. This is done using test cases which stress the simulator across several axes: grid resolution, different petrophysical property distributions, well count and the volume of reported data. The synthetic models which form the basis for these studies were designed to represent realistic reservoir engineering scenarios.\u0000 The results show that a static partition between CPU- and GPU-assigned tasks, as employed by default in the simulator, is performant for scenarios where the work dedicated to grid cell properties and linear solution vastly outnumbers the effort spent resolving well or aquifer connections, field management and reporting. This is expected for typical simulation cases. However, when one of the latter aspects becomes dominant, the balance can shift, leading to suboptimal hardware utilization. In conclusion, if performance across all possible inputs is to be maintained, then a fully-CPU-and-GPU-capable simulator is needed, employing a dynamic scheduling strategy, where the runtime data locality, volume and parallelism of the corresponding computations are all considered when determining the target device for each operation.\u0000 To the authors’ knowledge, a study on the scalability of a commercial reservoir simulator, across two different hardware architectures, has not previously been conducted to this level of detail. The results on realistic models are presented in the hope that they will contribute to the discussion surrounding the benefits of modern computing hardware for reservoir simulation and help drive deployment and design decisions for existing and future developments in both the commercial and academic spheres.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"464 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123652821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Physics-Informed Neural Network for Temporospatial Prediction of Hydraulic-Geomechanical Processes","authors":"Chi Zhang, Shihao Wang, Yushu Wu","doi":"10.2118/212202-ms","DOIUrl":"https://doi.org/10.2118/212202-ms","url":null,"abstract":"\u0000 This work aims to quantify the temporal and spatial evolution of pressure and stress fields in poroelastic reservoirs by replacing the conventional reservoir-geomechanical simulators with a novel convolutional-recurrent network (CNN-RNN) proxy. The proposed convolutional-recurrent neural network uses the governing equations of the coupled hydraulic-geomechanical process as the loss function. Initial conditions and spatial rock property fields are taken as inputs to predict the variation of pressure and stress fields. A customized convolutional filter mimicking the higher-order finite difference approach is adopted to improve the solution accuracy of the network.\u0000 We apply the neural network to solve one synthetic 2D hydraulic-geomechanical problem. The pressure and stress fields predicted from our neural network are compared with the reference numerical solutions derived from the finite difference method. The performance exhibits the potential of the proposed deep learning model for hydraulic-geomechanical processes simulation. The predicted pressure field displays a high degree of accuracy up to 95%, while the error in stress prediction is slightly higher due to the limitation of the current adopted neural network. In particular, our model outperforms the traditional second-order finite difference method in both speed and accuracy. Overall, the work shows the capability of the neural network to capture temporospatial prediction in hydraulic-geomechanical processes.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115811233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mineral-Composition Dependent Fracture Numerical Model of Thermally Treated Shale Gas Reservoirs","authors":"Dongqi Ji, Zhengdong Lei, Jiandong Liu, Xu Han, Chenqi Ge, Zhiping Li, Zhangxin Chen","doi":"10.2118/212166-ms","DOIUrl":"https://doi.org/10.2118/212166-ms","url":null,"abstract":"\u0000 Thermal treatment of shale gas reservoirs can vaporize water, accelerate gas desorption, and induce micro-fractures in shale matrix, which is a potential method to enhance shale gas productivity. However, few studies are focused on the thermal micro-cracking behavior of shale, especially at the mineral-scale. Furthermore, the effect of mineral composition on micro-fracture generation and shale permeability alternations are not fully understood in the current research results. In this work, a mineral-dependent fracture numerical model of thermally treated shale gas reservoirs is proposed. This model couples thermally induced stress in minerals, permeability enhancement, fluids flow and energy conservations in shale. A novel constitutive model based on volumetric constraint to relate stress and strain of minerals in shale is applied in the numerical simulation process. Comparison to experimental results demonstrates the reliability and robustness of the presented computation model. The proposed simulation method in this work is a powerful tool to link the macro-scale characteristics and thermally induced micro-fracture of shale.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116420993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Constrained Neural Network (PcNN): Phase Behavior Modeling for Complex Reservoir Fluids","authors":"Yiteng Li, Xupeng He, Zhen Zhang, M. AlSinan, H. Kwak, H. Hoteit","doi":"10.2118/212209-ms","DOIUrl":"https://doi.org/10.2118/212209-ms","url":null,"abstract":"\u0000 The highly nonlinear nature of equation-of-state-based (EOS-based) flash calculations encages high-fidelity compositional simulation, as most of the CPU time is spent on detecting phase stability and calculating equilibrium phase amounts and compositions. With the rapid development of machine learning (ML) techniques, they are growing to substitute classical iterative solvers for speeding up flash calculations.\u0000 However, conventional data-driven neural networks fail to account for physical constraints, like chemical potential equilibrium (equivalent to fugacity equality in the PT flash formulation) and interphase/intraphase mass conservation. In this work, we propose a physics-constrained neural network (PcNN) that first conserves both fugacity equality and mass balance constraints. To ease the inclusion of fugacity equality, it is reformulated in terms of equilibrium ratios and then introduced with a relaxation parameter such that phase split calculations are extended to the single-phase regime. This makes it technologically feasible to incorporate the fugacity equality constraint into the proposed PcNN model without any computational difficulty.\u0000 The workflow for the development of the proposed PcNN model includes four steps. Step 1: Perform the constrained Latin hypercube sampling (LHS) to generate representative mixtures covering a variety of fluid types, including wet gas, gas condensate, volatile oil, and black oil. Step 2: Conduct PT flash calculations using the Peng-Robinson (PR) EOS for each fluid mixture. A wide range of reservoir pressures and temperatures are considered, from which we sample the training data for each fluid mixture through grid search. Step 3: Build an optimized PcNN model by including the fugacity equality and mass conservation constraints in the loss function. Bayesian optimization is used to determine the optimal hyperparameters. Step 4: Validate the PcNN model. In this step, we conduct blind validation by comparing it with the iterative PT flash algorithm.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123382298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fast History Matching and Optimization Tool and its Application to a Full Field with More than 1,000 Wells","authors":"G. Ren, Zhenzhen Wang, Yuanbo Lin, Tsubasa Onishi, Xiaoyue Guan, X. Wen","doi":"10.2118/212188-ms","DOIUrl":"https://doi.org/10.2118/212188-ms","url":null,"abstract":"\u0000 In this work, we study a waterflood field containing over 1,000 wells and the modern field management techniques with full-fidelity 3D geo-cellular reservoir models become computationally prohibitive. To overcome the difficulty, we developed a novel flow-network data-driven model, GPSNet, and used it for rapid history matching and optimization. GPSNet includes physics, such as mass conservation, multiphase flow, phase changes, etc., while maintaining a good level of efficiency. To build such a model, a cluster of 1-D connections among well completion points are constructed and form a flow network. Multi-phase fluid flow is assumed to occur in each 1-D connection and the flow in the whole network is simulated by our in-house general-purpose simulator. Next, to effectively reduce the uncertainty, a hierarchical history-matching workflow is adopted to match the production data. Ensemble Smoother with Multiple Data Assimilation (ESMDA) is utilized to reduce the error at each step of the history matching. Next, a best-matched candidate is selected for numerical optimization to maximize oil production rates with constraints satisfying field conditions. Excellent history-matching results have been achieved on the field level and good matches have also been observed for key producers. In addition, the history matching consumes mere 4 hours to finish 1,100 simulation jobs. The successful application of the GPSNet to this waterflood field demonstrates a promising workflow that can be used as a fast and reliable decision-making tool for reservoir management.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134505317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}