Day 2 Wed, September 04, 2019最新文献

{"title":"When to Go with Cloud or Edge Computing in Offshore Oil and Gas","authors":"S. Settemsdal, B. Bishop","doi":"10.2118/195758-MS","DOIUrl":"https://doi.org/10.2118/195758-MS","url":null,"abstract":"\u0000 This paper will discuss when it is advantageous (in the context of an offshore oil and gas environment) to process data at the network edge (in close proximity to equipment assets) or to stream data to a cloud-based Internet of Things (IoT) platform for analysis. It will offer an objective assessment of both approaches and provide recommendations for securing data in both cases, as part of an overarching cybersecurity strategy.\u0000 IoT has opened the door to significant efficiency gains in the oil and gas industry. This is particularly the case in the offshore sector, where there is a pressing need to reduce costs and maximize equipment availability. In some cases, it is advantageous to process data in close proximity to equipment assets (i.e., at the edge). In others, it makes more sense to securely stream data to a cloud- based IoT platform and harness artificial intelligence (AI) to aid in decision making. In certain cases, both architectures can be utilized in compliment to one another.\u0000 Many factors need to be taken into consideration when evaluating an edge or cloud-based approach. Some of these include data volume, transmission and processing speed, control of data, cost, etc. Edge computing can be used to streamline and enhance the efficiency of data analytics. In certain applications, this can mean the difference between analyzing a performance failure after the fact, and pre-empting it in the first place, which in the offshore environment could potentially translate into millions of dollars per day.\u0000 On the other hand, there are situations where it is beneficial to store large volumes of data on a cloud-based platform. For example, if the goal is to leverage advanced IoT-based industrial analytics to optimize an entire fleet of a certain type of equipment, the cloud may be the best solution. Cybersecurity is another consideration. Attacks on critical infrastructure have risen significantly over the course of the past year. As more Intelligent Electronic Devices (IEDs) are deployed in the oil and gas industry to optimize efficiency, Industrial Control Systems (ICSs) are increasingly vulnerable. As a result, the threat extends beyond proprietary data to mission-critical operational technology (OT) assets and equipment.\u0000 Cybersecurity standards and layered, defense-in-depth models have grown in response to the frequency and sophistication of cyber attacks. Additionally, recent advances in cyber defense technology incorporate small, kilobit-sized embedded software agents to monitor networks for anomalies that could signal an intrusion. This paper will explore new cybersecurity threats to oil and gas assets, as well as strategies operators can employ to defend against them, whether using an edge or cloud-based platform, or both.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126977931","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":"Saving Lives with Statistics - An Introduction to Data Science in Workplace Safety","authors":"Marek Danis","doi":"10.2118/195737-MS","DOIUrl":"https://doi.org/10.2118/195737-MS","url":null,"abstract":"\u0000 Workplace safety is a main objective of any company working in the oil and gas business. The processes have been developed and established over the past decades based on individual experiences and causal pathways. The exhaustion of technical and administrative barriers has led to the introduction of behavioral safety. Recent advances in data technology and machine learning have disrupted many businesses and processes and can lead to a new paradigm in workplace safety as well.\u0000 In this case study we demonstrate the application of data science and predictive analytics to aid the HSE function and prevent accidents. We have analyzed operational and accident data from the past 10 years at a leading oil and gas company to quantify the effectiveness of their safety programs.\u0000 We have determined how many accidents each program actually prevents, and is able to prevent in an optimal setting. We have determined the optimal level of engagement for each program, and at what level diminishing returns set in.\u0000 We have further developed a predictive model to forecast the occurrence of accidents one month ahead of time. In this way the HSE function is able to focus on 15% of locations to control 69% of the accidents. The forecast was also able to predict accidents at locations where one would traditionally not expect accidents to happen, such as locations with low activity.\u0000 This paper shows the potential for improvement that is possible with the emerging big data, artificial intelligence and machine learning tools specifically in the field of workplace safety.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127004446","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":"Ubiquitous Sensing Network for Continuous Monitoring and Quantification of Methane Emissions","authors":"N. Alkadi, Joni Chow, K. Howe, R. Potyrailo, A. Abdilghanie, Balaji Jayaraman, Rakshit Allamraju, John Westerheide, J. Corcoran, Valeria Di Filippo, P. Kazempoor, B. Zoghbi, A. El-Messidi, Jianmin Zhang, G. Parkes","doi":"10.2118/195703-ms","DOIUrl":"https://doi.org/10.2118/195703-ms","url":null,"abstract":"\u0000 This paper presents our progress in developing, testing, and implementing a Ubiquitous Sensing Network (USN) for real-time monitoring of methane emissions. This newsensor technology supports environmental management of industrial sites through a decision support system. Upon detection of specific inputs, data is processed before passing it on for appropriate actions (Data→Insight→Actions). The technology integrates wireless methane sensor nodes, weather sensors, edge-based devices and is powered by a self- contained solar-battery powered system. A cloud-based data analytics IoT solution is included for handling continuous sensor monitoring. A sample of results from an in-house simulated well site are presented within the body of this paper. Preliminary predictions seem to correlate well with the true emission rate as indicated by the proximity of the predictions to the forty-five-degree line. Running more tests should allow us to further estimate the error distribution as well as the prediction interval width and the overall emission rate prediction trend. The initial results demonstrate that the developed technology can quantify the emission rate (scfh) within 1% and 45% error, and a localization error within six feet to fifty feet given a test area of 10,000 square feet. This integrated solution is being ruggedized and the analytics are being optimized for continuous monitoring of methane emissions at customer sites for safety, product loss prevention, and regulatory compliance.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131132708","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":"Improving the Quality and Efficiency of Operational Planning and Risk Management with ML and NLP","authors":"C. Birnie, Jennifer Sampson, Eivind Sjaastad, Bjarte Johansen, Lars Egil Obrestad, Ronny Larsen, Ahmed Khamassi","doi":"10.2118/195750-MS","DOIUrl":"https://doi.org/10.2118/195750-MS","url":null,"abstract":"\u0000 To ensure safe and efficient operations, all offshore operations follow a plan devised to take into account current operation conditions and identify the optimum workflow with the minimum risk potential. Previously, planners had to manually consult eight data sources, each with a separate UI, and summarise the plan in a.pdf document. Equinor's Operation Planning Tool (OPT) has been developed to easily present the planners with the technical conditions of a platform, identify potentially dangerous combinations of concurrent activities, and propose learnings from eight years’ worth of incident recordings – all relevant to the current list of planned activities. The tool aims to answer questions such as ‘are other activities planned for the same time which would make this activity unsafe?’ or ‘have incidents previously occurred whilst performing similar tasks on this equipment type?’.\u0000 This paper details the development of the OPT with a particular focus on the application of Natural Language Understanding for extracting equipment types and tasks involved in previous incidents and relating these to planned activities. Utilising natural language processing techniques, a system has been developed that mines the content of Equinor's incident database, and assigns context to incidents, by identifying the systems, activities and equipment involved and the conditions on the asset at the time of the incident. The same context is also discovered from the content of planned activities. These key concepts are organised into a knowledge graph synthesising Equinor's institutional safety and operational experience.\u0000 The OPT has reduced time spent planning by providing a single interface detailing a plant's technical conditions, all planned work orders and relevant lessons learned from previous incidents. By reducing the reliance on personal experience, the tool has provided subjectively improved risk identification and handling, plus faster knowledge transfer to new employees as well as focussed cross-platform knowledge sharing. The success of the tool highlights the strength of combining data and leveraging the vast quantities of historic data available both in unstructured and structured forms to create a safe, offshore work environment.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125177030","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":"Adaptive Artificial Neural Networks for Water-Cut Estimation Using Near-Infrared Optical Sensors","authors":"Qin Li, K. Fjalestad","doi":"10.2118/195719-MS","DOIUrl":"https://doi.org/10.2118/195719-MS","url":null,"abstract":"\u0000 In this paper, we present a water-cut estimator utilizing the function approximation capability of an artificial neural network (ANN). The inputs to the ANN are optical sensor readings in a Red-Eye water-cut meter, which features the near-infrared (NIR) absorption spectroscopy technology. The initial training of the ANNwas done with a data set acquired from our multi-phase flow-loop test facility, which was filled with live oil, water and gas. The test fluid stream was adjusted with good ranges of water-cut and gas-volume fractions which were supposed to cover the situations that can be foreseen in real production. However, clear discrepancies between the outputs of the ANN and the water-cut values from BS&W measurmentswere observedwhen the ANN was applied to actual production data measured by Red-Eye meters installed at two offshore wells. To address this issue and equip the ANN with self-adapting capability in real application, we propose a Bayesian approach to update the parameters of the ANN based on both initial flow-loop data and collected field data. The performance of the adapted ANN on both the data sets shows the effectiveness of the method.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124521931","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":"Offshore Infrastructure Reuse Contribution to Decarbonisation","authors":"Hayleigh Pearson, Christopher Pearson, Luca Corradi, A. Almeida","doi":"10.2118/195772-MS","DOIUrl":"https://doi.org/10.2118/195772-MS","url":null,"abstract":"\u0000 The UK and the international community have an increasing interest in the benefits of a hydrogen-based economy. Existing and emerging technologies that are inherently carbon-neutral and potentially carbon-negative are increasingly attractive, given the challenge of meeting climate targets to prevent climate change and build a clean growth strategy. The integration of clean energy technologies across the UK Continental Shelf (UKCS) can increase the flexibility of the energy system, driving efficiency, cost reduction and enhancing the value of natural resources.\u0000 There are over 250 platforms and 45,000 kilometres of pipeline installed within the United Kingdom Continental Shelf (UKCS). As these assets near the end of their economic life oil and gas operators are planning to decommission these facilities in an efficient and cost-effective manner. Current cost forecasts for this activity exceed £58bn with approximately 50% borne by the operators and 50% borne by UK taxpayers.\u0000 The Hydrogen Offshore Production (HOP) project identifies an alternative to decommissioning by providing re-use options for offshore infrastructure while addressing the national challenge of a low carbon energy supply. In doing so, the project will prove the feasibility of several decentralised hydrogen generation, storage and distribution options that collectively provide a scalable offshore hydrogen production solution, whilst offsetting a portion of decommissioning costs that are currently forecast for all offshore assets and infrastructure.\u0000 HOP will tackle the challenge of bulk hydrogen production by (1) proposing viable environmental and economic technology solutions to be deployed offshore, (2) developing a new Industrial Hydrogen Production test site to both prove the industrial benefits and to aid commercialisation of emerging technology and, (3) conducting market analysis and producing the business case for the transformation of existing offshore infrastructure, re-purposing assets and demonstrating the viability for decentralised generation of hydrogen.\u0000 As part of the project, an Industrial Hydrogen Production test site will be established with Flotta (Orkney Islands) being proposed as the location. This will provide a test bed for technology, fast-tracking its development and providing a route for accelerated commercial deployment. Within a region of considerable renewable energy generation, the island of Flotta is ideally placed to benefit from local expertise, existing supply chain and advanced technology solutions. For example, the Industrial Hydrogen Production test site would greatly benefit from lessons learnt at the nearby Orkney Water Testing Centre.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127924837","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":"Using Advanced Analytics to Identify the Most Probable Locations of Corrosion Under Insulation","authors":"Nivedita K. Kumar, B. Mackenzie, Kjersti Løken","doi":"10.2118/195733-MS","DOIUrl":"https://doi.org/10.2118/195733-MS","url":null,"abstract":"\u0000 Over 20 percent of major oil and gas (O&G) incidents reported within the European Union (EU) since 1984 have been associated with corrosion under insulation (CUI) [1]. Challenges are particularly acute when the source of risk is hidden, as in the case of CUI. With data being continuously generated, significant effort is required to manage data and mitigate risk.\u0000 Using bayesian networks (BNs) Oceaneering has developed a decision support system for effective CUI risk management. The Bayesian model can be incorporated into existing risk-based assessment (RBA) systems. A key feature of the model is the ability to predict corrosion hotspots while quantifying uncertainties. The model uses probabilities based on objective data as well as subject matter expertise, which makes analytical techniques in business accessible to a wide range of users.\u0000 With a case study we illustrate how BNs can be used to assess the risk of a fuel gas line on a live asset in the North sea. The most likely estimated remaining life (ERL) is forecasted in the range of 13 to 24 years, with a worst case of 6.7 years and best case of 40 years. By comparison, the customer CUI tracker reported an ERL of 9.7 years. BNs increase flexibility for scheduling inspection intervals, enabling more targeted inspection planning. This is a significant advancement from current RBA methodologies.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116029383","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":"Digital Initiatives for Condition Based Maintenance Using Monitoring Solutions with Data Analytics","authors":"K. Bhalla, K. Fisher, Jason E. Waligura","doi":"10.2118/195707-MS","DOIUrl":"https://doi.org/10.2118/195707-MS","url":null,"abstract":"\u0000 Presently, drilling riser joints are inspected every five years. This is usually accomplished by rotating 20% onshore every year to be dis-assembled and inspected. This requires extensive boat trips from a mobile operating drilling unit (MODU) to onshore and trucking of the riser to the inspection facility. Typically, 20 riser joints from each riser system are transported on a boat and one riser per truck to an inspection facility each year, making the logistics of performing a drilling inspection complex and costly.\u0000 A laser-based measurement for inspection together with monitoring of riser systems has been implemented with a new standard process for collecting critical riser data that is ABS approved. The aim is to mitigate the costs and time associated with essential MODU drilling riser inspections, by empowering operators to reliably determine the condition of drilling riser joints, consistently predict when vital components will require service and accurately assess remaining component life.\u0000 The approach utilizes a life cycle condition based monitoring, maintenance and inspection system that can be deployed on a MODU, enabling resources to be deployed only when necessary, instead of on a calendar interval. The solution consists of: Performing a baseline inspection on the riser joints to assess their present state, Collecting the environmental and operating data when the rig is on site drilling, Feeding the environmental and operating data into a digital twin. The tuned digital twin can be used to predict future damage.\u0000 The approach removes uncertainties surrounding damage of riser joints and will allow the owner to determine whether riser should be redeployed or replaced. This is the only process that is ABS approved for condition based monitoring of drilling riser systems. The system is compatible with all present owners’ maintenance programs and ensures that maintenance requirements are supported with robust engineering.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116356578","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":"AR and VR Applications Improve Engineering Collaboration, Personnel Optimization, and Equipment Accuracy for Separation Solutions","authors":"S. Clarke, Ketan Kapila, Mark Stephen","doi":"10.2118/195720-MS","DOIUrl":"https://doi.org/10.2118/195720-MS","url":null,"abstract":"\u0000 With the most recent industry downturn still fresh in many minds, the oil and gas E&P sector is approaching this recovery with a commitment to long-term cost discipline. As a result, augmented reality (AR) and virtual reality (VR) technologies are being adopted by operators and service companies alike as a means of cost savings while driving operational efficiency.\u0000 AR technologies employ enhanced visualization hardware, techniques, and methodologies to create new environments wherein digital and physical objects and their data coexist and interact with one another, enhancing the user experience of the real world (Kunkel and Soechti 2017). VR refers to the full immersion of the user intoand interaction with a completely digital environment. Together, these technologies form the core of immersive experience and a new paradigm in industrial interaction.\u0000 Until recently, these technologies were primarily applied as enhanced entertainment products, most notably within the gaming industry. However, during the past several years, and thanks to the introduction of hands-free, head-mounted display (HMD) technologies, such as Microsoft® HoloLens™ and now HoloLens 2, AR and VR are migrating into the enterprise sector.\u0000 While the oil field has not been as quick to integrate AR and VR as other sectors, such as medicine, defense, and aeronautics, operators and service providers alike have increased adoption overthe past 12 months. Motivated by a mandate to keep operating costs low and improve efficiencies in terms of field processes, operators have begun implementing AR/VR applications as collaborative problem-solving, planning, and design tools.\u0000 For example, some operators are initiating ARconcepts to promote internal use development and prototyping for both oilfield applications and remote refinery inspections. Additionally, service companies are embracing the use of smart glasses and wearable technologies to help improve remote work and collaboration to help increase in-field safety and reduce downtime.\u0000 As part of its strategy to help drive the oil and gas industry's digital transformation, one major service provider is developing AR/VR applications to create digital representations of physical oilfield assets on the Microsoft® HoloLens device. One area of focus is the planning, design, and deployment of solids control, fluid separation, and handling technologies for offshore drilling applications.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116830458","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":"Applying Energy Storage Solutions ESS in Offshore Oil and Gas to Reduce Emissions and Costs","authors":"S. Settemsdal","doi":"10.2118/195777-MS","DOIUrl":"https://doi.org/10.2118/195777-MS","url":null,"abstract":"\u0000 This paper will focus on the application of lithium-ion energy storage solutions (ESS) for offshore oil and gas (O&G) installations. It will discuss the benefits that can be achieved by integrating energy storage in hybrid power plants, using the West Mira semisubmersible installation in the North Sea as a representative case study. West Mira will be the world's first modern drilling rig to operate a low-emission hybrid (diesel- electric) power plant using lithium-ion batteries.\u0000 The integration of energy storage with the power supply and distribution system of a drilling rig represents an important step towards improving the environmental sustainability of the offshore oil and gas industry by reducing emissions and paving the way to harnessing clean but intermittent renewables, such as offshore wind. Offshore rigs have highly variable power consumption for drilling and dynamic positioning. By incorporating energy storage, it is possible to reduce the runtime of combustion engines and also keep them operating on an optimized combustion level. The installation of an ESS on West Mira will result in an estimated 42% reduction in the runtime of on-platform diesel engines, reducing CO2 emissions by 15 percent and NOx emissions by 12 percent, which is equivalent to annual emissions from approximately 10,000 automobiles.\u0000 The batteries on West Mira will be charged from the rig's diesel-electric generators and used for supplying power during peak load times. In addition, they will serve as backup to prevent blackout situations and provide power to the thrusters in the unlikely event of loss of all running machinery. The energy storage solution is based on field-proven technology, which has been installed in more than 60 marine vessels worldwide, including the world's first all-electric ferry boat in Norway.","PeriodicalId":113290,"journal":{"name":"Day 2 Wed, September 04, 2019","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114559553","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}