Although polymer flooding technology has been widely applied. Yet the "entry profile inversion" phenomenon occurs inevitably in its later stage, which seriously affects the development effect. In recent years, the micro-nano oil-displacement system is a novel developed flooding system. The oil-displacement system consists of micro-nano particles and its carrier fluid. After coming into porous media, it shows the properties of "plugging large pore and leave the small one open" and the motion feature of "trapping, deformation, migration". In this paper, physicochemical properties, reservoir adaptability, oil displacement mechanism of micro-nano oil-displacement system in pore throat is explored by using macroscopic physical simulation and CT scanning technology. Furthermore, the typical field application case is analyzed. Results show that, micro-nano particles have good physicochemical performance and transport ability in porous media. According to the reservoir adaptability evaluation, the matching relationships between particle size and core permeability is obtained, to provide guidance for field application scheme. By using NMR andCT techniques, its micro percolation law in porous media and remaining oil distribution during displacement process is analyzed. During the experiment, micro-nano particles presents the motion feature of "migration, trapping, and deformation" in the core pore, which can realize deep fluid diversion and expand swept volume. From 3D macro experiment, the sweep volume can be further expanded by injecting MNS and adjusting well pattern structure after polymer flooding. The dual goals of expanding sweep volume and improving oil washing efficiency can be achieved by using binary composite system (MNS and petroleum sulfonate) and ternary composite system (MNS, alkali and petroleum sulfonate). Finally, the micro-nano oil-displacement system conformance control technology has been applied in different oilfields, which all obtained significant oil increment effect. By using the research methods of interdisciplinary innovative, the oil displacement mechanism and field application of micro-nano oil-displacement system is researched. The research results provide guidance for oil companies to enhance oil recovery significantly.
{"title":"Research Progress and Field Trail of a New Micro-Nano Oil-Displacement System Flooding Technology","authors":"Zhe Sun, Xiujun Wang","doi":"10.2118/209656-ms","DOIUrl":"https://doi.org/10.2118/209656-ms","url":null,"abstract":"\u0000 Although polymer flooding technology has been widely applied. Yet the \"entry profile inversion\" phenomenon occurs inevitably in its later stage, which seriously affects the development effect. In recent years, the micro-nano oil-displacement system is a novel developed flooding system.\u0000 The oil-displacement system consists of micro-nano particles and its carrier fluid. After coming into porous media, it shows the properties of \"plugging large pore and leave the small one open\" and the motion feature of \"trapping, deformation, migration\". In this paper, physicochemical properties, reservoir adaptability, oil displacement mechanism of micro-nano oil-displacement system in pore throat is explored by using macroscopic physical simulation and CT scanning technology. Furthermore, the typical field application case is analyzed.\u0000 Results show that, micro-nano particles have good physicochemical performance and transport ability in porous media. According to the reservoir adaptability evaluation, the matching relationships between particle size and core permeability is obtained, to provide guidance for field application scheme. By using NMR andCT techniques, its micro percolation law in porous media and remaining oil distribution during displacement process is analyzed. During the experiment, micro-nano particles presents the motion feature of \"migration, trapping, and deformation\" in the core pore, which can realize deep fluid diversion and expand swept volume. From 3D macro experiment, the sweep volume can be further expanded by injecting MNS and adjusting well pattern structure after polymer flooding. The dual goals of expanding sweep volume and improving oil washing efficiency can be achieved by using binary composite system (MNS and petroleum sulfonate) and ternary composite system (MNS, alkali and petroleum sulfonate). Finally, the micro-nano oil-displacement system conformance control technology has been applied in different oilfields, which all obtained significant oil increment effect.\u0000 By using the research methods of interdisciplinary innovative, the oil displacement mechanism and field application of micro-nano oil-displacement system is researched. The research results provide guidance for oil companies to enhance oil recovery significantly.","PeriodicalId":148855,"journal":{"name":"Day 4 Thu, June 09, 2022","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126570908","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}
Depleted oil and gas fields may provide important locations for Carbon Capture and Storage (CCS). However, injection of carbon dioxide into pressure depleted oil and gas fields can be problematic due to the low reservoir pressure and the phase change behavior of carbon dioxide. The change of carbon dioxide from a liquid into a gas can trigger physical phenomena, such as significant cooling of the fluid as a result of the Joule-Thomson effect and the latent heat of vaporization, which can cause material embrittlement and loss of equipment functionality, and unstable or surging injection rates. Current mitigations restrict the quantity of carbon dioxide able to be injected by use of multiple injection tubing strings that can be costly or technically prohibitive. A more attractive alternative may be the use of downhole variable flow restricting devices which will autonomously respond to the changing well conditions, without the need for intervention or a workover in later well life. There is limited software currently available to model flow control to ensure carbon dioxide remains in liquid form in the completion. Through nodal analysis, the CCS simulator developed in this study can simulate the choking effect of downhole flow control devices placed at intervals in the completion that are sized and numbered to achieve the desired pressure distribution and CO2 injection rate. The modelling can then illustrate the required operating parameters of the downhole flow control solution with the results indicating the equivalent orifice sizes required for the flow control devices. The adjustable flow control devices can be removed or fully opened when the reservoir pressure increase and injection rate climbs and thus deemed to be no longer necessary. The use of downhole flow control devices can replace the need for a multiple string completion as the reservoir pressures and injection rates vary over the life of the well.
{"title":"Using a CCS Simulator to Maintain Liquid CO2 in the Completion","authors":"Anna Helene Petitt, M. Konopczynski","doi":"10.2118/209705-ms","DOIUrl":"https://doi.org/10.2118/209705-ms","url":null,"abstract":"\u0000 Depleted oil and gas fields may provide important locations for Carbon Capture and Storage (CCS). However, injection of carbon dioxide into pressure depleted oil and gas fields can be problematic due to the low reservoir pressure and the phase change behavior of carbon dioxide. The change of carbon dioxide from a liquid into a gas can trigger physical phenomena, such as significant cooling of the fluid as a result of the Joule-Thomson effect and the latent heat of vaporization, which can cause material embrittlement and loss of equipment functionality, and unstable or surging injection rates. Current mitigations restrict the quantity of carbon dioxide able to be injected by use of multiple injection tubing strings that can be costly or technically prohibitive. A more attractive alternative may be the use of downhole variable flow restricting devices which will autonomously respond to the changing well conditions, without the need for intervention or a workover in later well life.\u0000 There is limited software currently available to model flow control to ensure carbon dioxide remains in liquid form in the completion. Through nodal analysis, the CCS simulator developed in this study can simulate the choking effect of downhole flow control devices placed at intervals in the completion that are sized and numbered to achieve the desired pressure distribution and CO2 injection rate. The modelling can then illustrate the required operating parameters of the downhole flow control solution with the results indicating the equivalent orifice sizes required for the flow control devices. The adjustable flow control devices can be removed or fully opened when the reservoir pressure increase and injection rate climbs and thus deemed to be no longer necessary. The use of downhole flow control devices can replace the need for a multiple string completion as the reservoir pressures and injection rates vary over the life of the well.","PeriodicalId":148855,"journal":{"name":"Day 4 Thu, June 09, 2022","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127188368","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}
I. D. Piñerez Torrijos, S. Strand, T. Puntervold, Agnes Kahlbom Wathne, Amalie Harestad, Katarina Radenkovic, P. Andersen
Rock wettability is of utmost importance when assessing reservoir recovery processes, because it controls key transport properties of fluid flow in porous media. The effects of wettability on capillary forces, fluid distribution, and oil mobilization are of great interest for understanding waterflooding and water-based EOR processes such as Smart Water injection. Two strongly water-wet and three reduced water-wet chalk cores containing Swi = 20% and 80 % non-wetting mineral oil were used in this study. Spontaneous imbibition (SI) experiments were used to assess the wettability of restored core material and forced imbibition (FI) tests were carried out to capture fluid flow behavior under a viscous force dominated environment. Oil recovery and pressure drop profiles, start and endpoint core saturations and pressure drops were collected in front of and during FI tests with formation water (FW) as injection fluid to avoid any chemical induced wettability alteration. The SI oil recovery results showed that the cores exposed to crude oil possessed reduced water wetness compared to the strongly water-wet reference cores. The FI oil recovery results showed only small differences in oil production profiles and ultimate recoveries. The oil recovery profiles displayed a piston-like displacement indicating that oil recovery was controlled by capillary forces at the injection rate used. SENDRA was used to simulate the effect of wettability on relative permeability and capillary pressure curves for the strongly to reduced water-wet cores from FI processes. On average, higher oil relative permeability end points and lower water relative permeability end points were measured for the strongly water-wet cores compared to the cores reduced in water-wetness. The core scale simulation with SENDRA indicates continuous production of water and oil taking infinite time to reach residual oil saturation, however, the end of production was reached at a finite time in the experiments. A history matching approach based only on single rate injection did not yield reliable results, partly, because the capillary and viscous forces cannot easily be separated in the history matching process. This affects estimates of residual oil saturation and water end points of relative permeability.
{"title":"The Effect of Core Wettability on Oil Mobilization, Capillary Forces and Relative Permeability in Chalk","authors":"I. D. Piñerez Torrijos, S. Strand, T. Puntervold, Agnes Kahlbom Wathne, Amalie Harestad, Katarina Radenkovic, P. Andersen","doi":"10.2118/209686-ms","DOIUrl":"https://doi.org/10.2118/209686-ms","url":null,"abstract":"\u0000 Rock wettability is of utmost importance when assessing reservoir recovery processes, because it controls key transport properties of fluid flow in porous media. The effects of wettability on capillary forces, fluid distribution, and oil mobilization are of great interest for understanding waterflooding and water-based EOR processes such as Smart Water injection.\u0000 Two strongly water-wet and three reduced water-wet chalk cores containing Swi = 20% and 80 % non-wetting mineral oil were used in this study. Spontaneous imbibition (SI) experiments were used to assess the wettability of restored core material and forced imbibition (FI) tests were carried out to capture fluid flow behavior under a viscous force dominated environment. Oil recovery and pressure drop profiles, start and endpoint core saturations and pressure drops were collected in front of and during FI tests with formation water (FW) as injection fluid to avoid any chemical induced wettability alteration. The SI oil recovery results showed that the cores exposed to crude oil possessed reduced water wetness compared to the strongly water-wet reference cores. The FI oil recovery results showed only small differences in oil production profiles and ultimate recoveries. The oil recovery profiles displayed a piston-like displacement indicating that oil recovery was controlled by capillary forces at the injection rate used.\u0000 SENDRA was used to simulate the effect of wettability on relative permeability and capillary pressure curves for the strongly to reduced water-wet cores from FI processes. On average, higher oil relative permeability end points and lower water relative permeability end points were measured for the strongly water-wet cores compared to the cores reduced in water-wetness. The core scale simulation with SENDRA indicates continuous production of water and oil taking infinite time to reach residual oil saturation, however, the end of production was reached at a finite time in the experiments. A history matching approach based only on single rate injection did not yield reliable results, partly, because the capillary and viscous forces cannot easily be separated in the history matching process. This affects estimates of residual oil saturation and water end points of relative permeability.","PeriodicalId":148855,"journal":{"name":"Day 4 Thu, June 09, 2022","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122388681","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}
Efficient development of tight reservoirs often relies on complex-hydraulic-fracture-network. Due to the time repeated iteration for simulation, the (semi)-analytical model or fully-numerical model often requires a trade-off especially for the accuracy of production analysis. Hence, a comprehensive model for accelerating production matching needs to be established. In this paper, a neighboring-long-short-term-memory (n-LSTM) model, integrated with a complex fracture semi-analytical flow model, can make production performance analysis with high efficiency. The interconnections between hydraulic and natural fractures with arbitrary angles and complex geometry were considered in flow model. Then, the reservoir flow derived from Laplace domain was coupled with fracture network flow numerically solved by finite difference method to obtain the semi-analytical flow solution. The specific distribution of flow solutions was obtained based on the range of reservoir properties, well information, and geological parameters. Thus datasets including production rate and date can be constructed, enlarged and split into training and testing dataset. The integrated model proposed in this paper adopted a non-orthogonal network with 4100 feet length and 53 segments for testing, and was applied for the characterization of complex fractures in the Changqing tight reservoir in the Ordos Basin, China. It is worth mentioning that 65 semi-analytical solutions are expanded to 1280 pairs of production-time data point using the n-LSTM model. With the strong power of capture and excavate the non-linear relationship between multitype data, it only takes a few minutes to forecast and match the daily production data with samples from actual oilfield. As a result, the mean square error of 0.31% in the training dataset and 2.63% in the testing dataset shows that the semi-analytical solution that accurately characterizes the complex fracture networks can be combined with improved LSTM for the prediction and analysis of oil production. In addition, it can be found that the prediction results of the integrated model can also identify the 1/4 slope and 1/2 slope straight lines in the log/log transient response curve. The interpreted results expand the application of semi-analytical solution assisted data-driven model and reduce the consumption of a large amount of repetition time. This paper provides an integrated data-driven model combined with semi-analytical model to make well performance analysis with more efficiency and high accuracy. This workflow, incorporated with fracture precise characterization, data generation and expansion, prediction and calibration, can be readily applied in oilfield to obtain fracture parameters with less time. In addition, time-series and small samples can be enlarged and excavated, especially for the in-proper records in production history.
{"title":"An Integrated Model Combining Complex Fracture Networks and Time-Varying Data Modeling Techniques for Production Performance Analysis in Tight Reservoirs","authors":"Chong Cao, Linsong Cheng, Zhihao Jia, P. Jia, Xuze Zhang, Yongchao Xue","doi":"10.2118/209632-ms","DOIUrl":"https://doi.org/10.2118/209632-ms","url":null,"abstract":"\u0000 Efficient development of tight reservoirs often relies on complex-hydraulic-fracture-network. Due to the time repeated iteration for simulation, the (semi)-analytical model or fully-numerical model often requires a trade-off especially for the accuracy of production analysis. Hence, a comprehensive model for accelerating production matching needs to be established. In this paper, a neighboring-long-short-term-memory (n-LSTM) model, integrated with a complex fracture semi-analytical flow model, can make production performance analysis with high efficiency. The interconnections between hydraulic and natural fractures with arbitrary angles and complex geometry were considered in flow model. Then, the reservoir flow derived from Laplace domain was coupled with fracture network flow numerically solved by finite difference method to obtain the semi-analytical flow solution. The specific distribution of flow solutions was obtained based on the range of reservoir properties, well information, and geological parameters. Thus datasets including production rate and date can be constructed, enlarged and split into training and testing dataset.\u0000 The integrated model proposed in this paper adopted a non-orthogonal network with 4100 feet length and 53 segments for testing, and was applied for the characterization of complex fractures in the Changqing tight reservoir in the Ordos Basin, China. It is worth mentioning that 65 semi-analytical solutions are expanded to 1280 pairs of production-time data point using the n-LSTM model. With the strong power of capture and excavate the non-linear relationship between multitype data, it only takes a few minutes to forecast and match the daily production data with samples from actual oilfield. As a result, the mean square error of 0.31% in the training dataset and 2.63% in the testing dataset shows that the semi-analytical solution that accurately characterizes the complex fracture networks can be combined with improved LSTM for the prediction and analysis of oil production. In addition, it can be found that the prediction results of the integrated model can also identify the 1/4 slope and 1/2 slope straight lines in the log/log transient response curve. The interpreted results expand the application of semi-analytical solution assisted data-driven model and reduce the consumption of a large amount of repetition time.\u0000 This paper provides an integrated data-driven model combined with semi-analytical model to make well performance analysis with more efficiency and high accuracy. This workflow, incorporated with fracture precise characterization, data generation and expansion, prediction and calibration, can be readily applied in oilfield to obtain fracture parameters with less time. In addition, time-series and small samples can be enlarged and excavated, especially for the in-proper records in production history.","PeriodicalId":148855,"journal":{"name":"Day 4 Thu, June 09, 2022","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127814262","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}
Accurate estimation of reservoir parameters such as fluid saturations and porosity is important for assessing petroleum volumes, economics and decisionmaking. Such parameters are derived from interpretation of petrophysical logs or time-consuming, expensive core analyses. Not all wells are cored in a field, and the number of fully cored wells is limited. In this study, a time-efficient and economical method to estimate porosity, water saturation and hydrocarbon saturation is employed. Two Least Squares Support Vector Machine (LSSVM) machine learning models, optimized with Particle Swarm Optimization (PSO), were developed to predict these reservoir parameters, respectively. The models were developed based on data from five wells in the Varg field, Central North Sea, Norway where the data were randomized and split into an unseen fraction (10%) and a fraction used to train the models (90%). In addition to the unseen fraction, a sixth well from the Varg field was used to assess the models. The samples are mainly sandstone with different contents of shale, while fluids water, oil and gas were present. The ‘seen’ data were randomized into calibration, validation and testing sets during the model development. The petrophysical logs in the study were Gamma-ray, Self-potential, Acoustic, Neutron porosity, bulk density, caliper, deep resistivity, and medium resistivity. The log based inputs were made more linear (via log operations) when relevant and normalized to be more comparable in the algorithms. Feature selection was conducted to identify the most relevant petrophysical logs and remove those that are considered less relevant. Three and four of the eight logs were sufficient, to reach optimum performance of porosity and saturation prediction, respectively. Porosity was predicted with R2 = 0.79 and 0.70 on the model development set and unseen set, for saturation it was 0.71 and 0.61, a similar performance as on the training and testing sets at the development stage. The R2 was close to zero on the new well, although the predicted values were physical and within the observed data scatter range as the model development set. Possible improvements were identified in dataset preparation and feature selection to get more robust models.
{"title":"Machine Learning Based Prediction of Porosity and Water Saturation from Varg Field Reservoir Well Logs","authors":"P. Andersen, Miranda Skjeldal, C. Augustsson","doi":"10.2118/209659-ms","DOIUrl":"https://doi.org/10.2118/209659-ms","url":null,"abstract":"\u0000 Accurate estimation of reservoir parameters such as fluid saturations and porosity is important for assessing petroleum volumes, economics and decisionmaking. Such parameters are derived from interpretation of petrophysical logs or time-consuming, expensive core analyses. Not all wells are cored in a field, and the number of fully cored wells is limited. In this study, a time-efficient and economical method to estimate porosity, water saturation and hydrocarbon saturation is employed. Two Least Squares Support Vector Machine (LSSVM) machine learning models, optimized with Particle Swarm Optimization (PSO), were developed to predict these reservoir parameters, respectively.\u0000 The models were developed based on data from five wells in the Varg field, Central North Sea, Norway where the data were randomized and split into an unseen fraction (10%) and a fraction used to train the models (90%). In addition to the unseen fraction, a sixth well from the Varg field was used to assess the models.\u0000 The samples are mainly sandstone with different contents of shale, while fluids water, oil and gas were present. The ‘seen’ data were randomized into calibration, validation and testing sets during the model development. The petrophysical logs in the study were Gamma-ray, Self-potential, Acoustic, Neutron porosity, bulk density, caliper, deep resistivity, and medium resistivity. The log based inputs were made more linear (via log operations) when relevant and normalized to be more comparable in the algorithms. Feature selection was conducted to identify the most relevant petrophysical logs and remove those that are considered less relevant. Three and four of the eight logs were sufficient, to reach optimum performance of porosity and saturation prediction, respectively. Porosity was predicted with R2 = 0.79 and 0.70 on the model development set and unseen set, for saturation it was 0.71 and 0.61, a similar performance as on the training and testing sets at the development stage. The R2 was close to zero on the new well, although the predicted values were physical and within the observed data scatter range as the model development set. Possible improvements were identified in dataset preparation and feature selection to get more robust models.","PeriodicalId":148855,"journal":{"name":"Day 4 Thu, June 09, 2022","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128030363","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}
Production from the Schoonebeek heavy oil steam flood in northeast Netherlands was historically curtailed because of limits on H2S and water production. The interdependence of various permit and facility constraints makes production optimisation for Schoonebeek extremely challenging. So much so, that the conventional IPSM approach does not apply. To understand the field's production potential and to reach it, the team developed a novel Production System Optimisation (PSO) workflow using techniques from machine learning and operations research. In this paper we explain the details of this PSO workflow, the mathematics behind it, and share our results and learnings. The algorithm runs in 5 minutes and is used in daily optimisation. The application of this new workflow in combination with the successful deployment of a novel H2S scavenger, resulted in a Schoonebeek production uplift of 50%.
{"title":"Artificial Intelligence for Production Optimization in Schoonebeek Thermal EOR Field","authors":"Mezlul Arfie, N. Ghodke, Kasper Groenbroek","doi":"10.2118/209670-ms","DOIUrl":"https://doi.org/10.2118/209670-ms","url":null,"abstract":"\u0000 Production from the Schoonebeek heavy oil steam flood in northeast Netherlands was historically curtailed because of limits on H2S and water production. The interdependence of various permit and facility constraints makes production optimisation for Schoonebeek extremely challenging. So much so, that the conventional IPSM approach does not apply. To understand the field's production potential and to reach it, the team developed a novel Production System Optimisation (PSO) workflow using techniques from machine learning and operations research. In this paper we explain the details of this PSO workflow, the mathematics behind it, and share our results and learnings. The algorithm runs in 5 minutes and is used in daily optimisation. The application of this new workflow in combination with the successful deployment of a novel H2S scavenger, resulted in a Schoonebeek production uplift of 50%.","PeriodicalId":148855,"journal":{"name":"Day 4 Thu, June 09, 2022","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129401915","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}
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