F. Shen, Kuanzhi Zhao, Yintao Zhang, Y. Yu, Jingliang Li
Karst reservoirs in the Tarim Basin, northwestern China, were formed by subaerial exposure and karstification from the Ordovician formation and represent the main plays. Predicting the storage capacity and quantifying permeability heterogeneities are challenging while important for field development planning. In this paper we present a hierarchical approach to modeling karst and fractures with geoscience and engineering data for selecting locations of new wells and for the reservoir simulation. Karst and fractures at multiple scales contribute significantly to reservoir volumes in place and well productivity. Fracture-karst units in wells were determined using log-based electrofacies validated against core data, image logs and drilling data to quantify different karst features and fracture patterns hosted in units. A 3-D architecture model of karst system was constructed with extracted karst features at the seismic-scale based on multi-attribute seismic facies analysis. The karst network model was generated with karst-fracture units at wells, inverted seismic impedance volume, and 3-D karst architecture model. Porosity estimates of the karst system were conditioned with log data, mud loss data, seismic impedance volume and karst network model. Karst horizontal and vertical conduits were modeled and their permeabilities were empirically derived. Based on fracture length relative to the seismic resolution, fractures were modeled at two scales. Diffuse fractures at a small scale were modeled stochastically conditioned with image log data and the karst fracture unit model. A discrete fracture network (DFN) model at a large scale was deterministically built by meshing fracture lineaments automatically tracked from the curvature enhanced attribute. The DFN model was embedded into a geocellular grid model in which geometries of the large fractures were maintained explicitly. The calculation of effective horizontal and vertical permeabilities of the fracture system was scale dependent and decoupled. Fracture geometric parameters and permeabilities were calibrated against well test data. Streamline simulation was performed in the static model to calibrate spatial fracture densities. After two-step conditioning, fracture models were updated and then upscaled. Flow properties of karst and fractures from the wellbore to the seismic scales were combined based on their impacts on fluid flow. Integration of karst network model and history match of water cut and bottom hole pressure using streamline simulation helped the oil/water contact (OWC) assessment and allowed the identification of dynamic compartments. Combing karst networks, dynamic compartments and modeled geological scenarios allowed targeting potential highly productive zones where new well locations could be selected. The case study demonstrated that the hierarchical approach to karst and fracture modeling and calibration allowed building a realistic reservoir model and better understanding of
{"title":"Hierarchical Approach to Modeling Karst and Fractures in Carbonate Karst Reservoirs in the Tarim Basin","authors":"F. Shen, Kuanzhi Zhao, Yintao Zhang, Y. Yu, Jingliang Li","doi":"10.2118/197264-ms","DOIUrl":"https://doi.org/10.2118/197264-ms","url":null,"abstract":"\u0000 Karst reservoirs in the Tarim Basin, northwestern China, were formed by subaerial exposure and karstification from the Ordovician formation and represent the main plays. Predicting the storage capacity and quantifying permeability heterogeneities are challenging while important for field development planning. In this paper we present a hierarchical approach to modeling karst and fractures with geoscience and engineering data for selecting locations of new wells and for the reservoir simulation.\u0000 Karst and fractures at multiple scales contribute significantly to reservoir volumes in place and well productivity. Fracture-karst units in wells were determined using log-based electrofacies validated against core data, image logs and drilling data to quantify different karst features and fracture patterns hosted in units. A 3-D architecture model of karst system was constructed with extracted karst features at the seismic-scale based on multi-attribute seismic facies analysis. The karst network model was generated with karst-fracture units at wells, inverted seismic impedance volume, and 3-D karst architecture model. Porosity estimates of the karst system were conditioned with log data, mud loss data, seismic impedance volume and karst network model. Karst horizontal and vertical conduits were modeled and their permeabilities were empirically derived. Based on fracture length relative to the seismic resolution, fractures were modeled at two scales. Diffuse fractures at a small scale were modeled stochastically conditioned with image log data and the karst fracture unit model. A discrete fracture network (DFN) model at a large scale was deterministically built by meshing fracture lineaments automatically tracked from the curvature enhanced attribute. The DFN model was embedded into a geocellular grid model in which geometries of the large fractures were maintained explicitly. The calculation of effective horizontal and vertical permeabilities of the fracture system was scale dependent and decoupled. Fracture geometric parameters and permeabilities were calibrated against well test data. Streamline simulation was performed in the static model to calibrate spatial fracture densities. After two-step conditioning, fracture models were updated and then upscaled. Flow properties of karst and fractures from the wellbore to the seismic scales were combined based on their impacts on fluid flow.\u0000 Integration of karst network model and history match of water cut and bottom hole pressure using streamline simulation helped the oil/water contact (OWC) assessment and allowed the identification of dynamic compartments. Combing karst networks, dynamic compartments and modeled geological scenarios allowed targeting potential highly productive zones where new well locations could be selected.\u0000 The case study demonstrated that the hierarchical approach to karst and fracture modeling and calibration allowed building a realistic reservoir model and better understanding of ","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83117988","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}
The next time you are tempted to scold your son or daughter for spending too many hours playing videogames, think twice: they may be training to be the best workers of the 21st century and even replace your position… Collaborative Work Environments (CWE) combined with Telepresence and Mixed Reality technologies are revolutionizing the design, engineering and building large petrochemical projects. This paper provides an overview of the technologies and describes how the design, implementation and control processes in these projects can be performed more safely and accurately at lower cost. Over three decades ago, businesses experienced a leap in performance, code reusability and maintainability when their information technologies moved from numbered line to object-oriented programming (OOP). We are now poised at the cusp of another quantum change in efficiency as a result of technology. In this new era data travels from "cradle to grave." From design, construction or assembly, to use, service and final dismantling of refineries and industrial facilities, the physical world of discrete elements will have an accurate digital equivalent. Thanks to powerful computing and Big Data warehousing, complex structures with millions of individual parts can now be tracked and displayed like intelligent LEGO® structures. The vision is that by adopting an open, agree-upon, and already-existing platform for technical communication among different software vendors, huge improvements in efficiency results from enabling a platform or "communal space" that interacts seamlessly with remote presence tools, and a global talent pool working side-by-side with local workers and designers in a virtual fashion. The technology for these real time virtual worlds is already commonplace in online video gaming. Together with the ability to log the activities in this parallel virtual but completely accurate digital world using Big Data, an exhaustive register from initial design through construction, operation and eventual dismantling may be used for detailed analysis, training and automation of preventive maintenance and safety, resulting in lower costs from improved efficiency and better management and enhanced safety. Extending this model and common language of data communication to include various industries, such as engineering, construction, aviation and military operations provides economies of scale in the adoption of an open, global and flexible platform for use by all, but without restricting innovation or compromising security. 3D provides spatial information as in existing CAD systems, adding the time element ‘4D’ incorporates project management and logistics. The next logical step includes cost and supplier information for informed complete life-cycle management of the equipment, project or facility, or ‘5D’. Uniquely tagging each object is ‘6D’ for real-time RFID asset management and Facilities Management using Big Data enhanced prognosis of maintenance an
{"title":"Digital Twins and Industry 4.0: Videogamers Will Staff and Manage Industrial Projects in the Near Future","authors":"J. Novack","doi":"10.2118/197538-ms","DOIUrl":"https://doi.org/10.2118/197538-ms","url":null,"abstract":"\u0000 \u0000 \u0000 The next time you are tempted to scold your son or daughter for spending too many hours playing videogames, think twice: they may be training to be the best workers of the 21st century and even replace your position…\u0000 Collaborative Work Environments (CWE) combined with Telepresence and Mixed Reality technologies are revolutionizing the design, engineering and building large petrochemical projects. This paper provides an overview of the technologies and describes how the design, implementation and control processes in these projects can be performed more safely and accurately at lower cost.\u0000 Over three decades ago, businesses experienced a leap in performance, code reusability and maintainability when their information technologies moved from numbered line to object-oriented programming (OOP). We are now poised at the cusp of another quantum change in efficiency as a result of technology. In this new era data travels from \"cradle to grave.\" From design, construction or assembly, to use, service and final dismantling of refineries and industrial facilities, the physical world of discrete elements will have an accurate digital equivalent. Thanks to powerful computing and Big Data warehousing, complex structures with millions of individual parts can now be tracked and displayed like intelligent LEGO® structures.\u0000 The vision is that by adopting an open, agree-upon, and already-existing platform for technical communication among different software vendors, huge improvements in efficiency results from enabling a platform or \"communal space\" that interacts seamlessly with remote presence tools, and a global talent pool working side-by-side with local workers and designers in a virtual fashion. The technology for these real time virtual worlds is already commonplace in online video gaming. Together with the ability to log the activities in this parallel virtual but completely accurate digital world using Big Data, an exhaustive register from initial design through construction, operation and eventual dismantling may be used for detailed analysis, training and automation of preventive maintenance and safety, resulting in lower costs from improved efficiency and better management and enhanced safety.\u0000 Extending this model and common language of data communication to include various industries, such as engineering, construction, aviation and military operations provides economies of scale in the adoption of an open, global and flexible platform for use by all, but without restricting innovation or compromising security.\u0000 3D provides spatial information as in existing CAD systems, adding the time element ‘4D’ incorporates project management and logistics. The next logical step includes cost and supplier information for informed complete life-cycle management of the equipment, project or facility, or ‘5D’. Uniquely tagging each object is ‘6D’ for real-time RFID asset management and Facilities Management using Big Data enhanced prognosis of maintenance an","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81744700","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}
Amna Yaaqob Khamis Salem Aladsani, Afra Hamad Alghafli, Sultan Hamdan Al Kaabi, K. Mcneilly, M. M. Akhtar, Deepak Tripathi, Hamda Alkuwaiti, Sandeep Soni, Jose Isambertt
This paper discusses a production efficiency improvement (PEI) case study using an Integrated Asset Model (IAM) in a super-giant brown field consisting of more than a thousand well strings producing from multi-layered reservoir with different properties. This paper discusses various scenarios that were considered to carry out production efficiency improvement and system bottleneck identification using IAM model integrated within digital framework consisting of automated workflows and advanced data integration. IAM solution was implemented in a super-giant brown field to help users to carry out complete system-analysis to assist in delivering production-mandates, identifying sustainability and removing potential bottlenecks for improvements. This solution incorporates integration of validated well and network models within a digital-layer, in which various analytical-processes and workflows are automated and integrated with multiple corporate-data-sources. This centralized production-optimization based collaborative-platform enables user to carry out various scenarios while taking into account different operating constraints. Validated and calibrated well and network models were integrated within these workflows, updating them on daily basis, thereby providing representative well and network performance parameters. This paper discusses several case studies that were carried out utilizing an integrated asset model, thereby achieving fundamental business objective of production efficiency improvement. For this purpose, full field network models consisting of more than a thousand calibrated well strings were analyzed within a digital IAM framework. Various what-if scenarios were adapted to conceptualize an engineering approach in which various reservoir, well and facility level guidelines were incorporated for identifying true potentials of the system. This holistic approach provided users the capability to carry out a detailed analysis to achieve various key production objectives such as reducing production deferrals, compensating production shortfalls, identifying total system capacity and thereby enhancing production efficiency. Key challenges and recommendations for improving production efficiency and establishing standardized well potential determination methodology were also highlighted from the case study. Lastly, identification of the true production limits of the reservoir, wells, and the surface network were made possible which is fundamental to the delivery of the long term field development plan. Identifying true capacity at the well and field level is a challenging task in a field with more than ten development area with completely different fluid properties and production capacities. A standardized IAM solution approach made this estimation possible. This approach also helped in minimizing potential production deferment thereby leading to cost optimization of total system.
{"title":"Actual Well Performance Identification and Production Efficiency Enhancement and Sustainability in a Brown Field","authors":"Amna Yaaqob Khamis Salem Aladsani, Afra Hamad Alghafli, Sultan Hamdan Al Kaabi, K. Mcneilly, M. M. Akhtar, Deepak Tripathi, Hamda Alkuwaiti, Sandeep Soni, Jose Isambertt","doi":"10.2118/197383-ms","DOIUrl":"https://doi.org/10.2118/197383-ms","url":null,"abstract":"\u0000 This paper discusses a production efficiency improvement (PEI) case study using an Integrated Asset Model (IAM) in a super-giant brown field consisting of more than a thousand well strings producing from multi-layered reservoir with different properties. This paper discusses various scenarios that were considered to carry out production efficiency improvement and system bottleneck identification using IAM model integrated within digital framework consisting of automated workflows and advanced data integration.\u0000 IAM solution was implemented in a super-giant brown field to help users to carry out complete system-analysis to assist in delivering production-mandates, identifying sustainability and removing potential bottlenecks for improvements.\u0000 This solution incorporates integration of validated well and network models within a digital-layer, in which various analytical-processes and workflows are automated and integrated with multiple corporate-data-sources. This centralized production-optimization based collaborative-platform enables user to carry out various scenarios while taking into account different operating constraints. Validated and calibrated well and network models were integrated within these workflows, updating them on daily basis, thereby providing representative well and network performance parameters.\u0000 This paper discusses several case studies that were carried out utilizing an integrated asset model, thereby achieving fundamental business objective of production efficiency improvement. For this purpose, full field network models consisting of more than a thousand calibrated well strings were analyzed within a digital IAM framework.\u0000 Various what-if scenarios were adapted to conceptualize an engineering approach in which various reservoir, well and facility level guidelines were incorporated for identifying true potentials of the system. This holistic approach provided users the capability to carry out a detailed analysis to achieve various key production objectives such as reducing production deferrals, compensating production shortfalls, identifying total system capacity and thereby enhancing production efficiency.\u0000 Key challenges and recommendations for improving production efficiency and establishing standardized well potential determination methodology were also highlighted from the case study. Lastly, identification of the true production limits of the reservoir, wells, and the surface network were made possible which is fundamental to the delivery of the long term field development plan.\u0000 Identifying true capacity at the well and field level is a challenging task in a field with more than ten development area with completely different fluid properties and production capacities. A standardized IAM solution approach made this estimation possible. This approach also helped in minimizing potential production deferment thereby leading to cost optimization of total system.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79820573","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}
C. Danquigny, G. Massonnat, Cédric Mermoud, J. Rolando
In carbonates, the geological facies is a key driver for populating reservoir models with petrophysical properties. Conventionnal core analysis mainly contributes to establish relationships between facies, petrophysics and geophysics. However, populating gridblocks reservoir models with petrophysics requires parsimonious facies classifications and effective relationships at larger scales that field studies rarely investigate. Studying outcrop analogues helps filling the gap between lab measurements and effective upscaled properties of models, and considerably improves the modelling workflows. The ALBION R&D project developed an innovative framework for multi-physics and multi-scales characterization of Barremian-Aptian carbonates from south-eastern France. These outcropping rudist-rich limestones constitute an analogue of Middle-East reservoirs. Petrophysical and geophysical properties were measured on plugs from cores and outcrops but also at larger scales thanks to original experiments on cores, in and between boreholes. Indeed the analogue includes several experimental areas, where hydraulic tests in sealed wells sections and tomographies between very close boreholes allowed investigating petrophysical and geophysical rock properties at intermediate decimetric to decametric scales. Thanks to the resulting database, this paper aims quantifying the variability of multi-physics data (e.g. porosity, permeability, and P-wave velocity) at different scales in regards of an updated and unified facies classification. The latter is only based on sedimentary origin and fabrics. Other available properties affecting petrophysics are used to cluster facies associations in sub-classes. Consequently the facies classification does not allow discriminating the distributions of porosity, permeability, nor p-wave velocity. For the rudist facies, that is the most sampled, texture subclasses do not help this work. Reversely, the place of sampling, that is likely a proxy of diagenesis and age, cluster the petrophysical distributions. The results remind us that a proper facies definition should consider both sedimentary origin, fabrics, texture, diagenesis and tectonics. They also point out the relative importance of each characteristics in regards of the scale of interest and the difficulty to infer upscaled relationships between rock properties from CCAL because the representative elementary volume of carbonates is usually higher than the plug and even the core volumes.
{"title":"Intra- and Inter-Facies Variability of Multi-Physics Data in Carbonates. New Insights from Database of ALBION R&D Project","authors":"C. Danquigny, G. Massonnat, Cédric Mermoud, J. Rolando","doi":"10.2118/197836-ms","DOIUrl":"https://doi.org/10.2118/197836-ms","url":null,"abstract":"\u0000 In carbonates, the geological facies is a key driver for populating reservoir models with petrophysical properties. Conventionnal core analysis mainly contributes to establish relationships between facies, petrophysics and geophysics. However, populating gridblocks reservoir models with petrophysics requires parsimonious facies classifications and effective relationships at larger scales that field studies rarely investigate. Studying outcrop analogues helps filling the gap between lab measurements and effective upscaled properties of models, and considerably improves the modelling workflows.\u0000 The ALBION R&D project developed an innovative framework for multi-physics and multi-scales characterization of Barremian-Aptian carbonates from south-eastern France. These outcropping rudist-rich limestones constitute an analogue of Middle-East reservoirs. Petrophysical and geophysical properties were measured on plugs from cores and outcrops but also at larger scales thanks to original experiments on cores, in and between boreholes. Indeed the analogue includes several experimental areas, where hydraulic tests in sealed wells sections and tomographies between very close boreholes allowed investigating petrophysical and geophysical rock properties at intermediate decimetric to decametric scales. Thanks to the resulting database, this paper aims quantifying the variability of multi-physics data (e.g. porosity, permeability, and P-wave velocity) at different scales in regards of an updated and unified facies classification. The latter is only based on sedimentary origin and fabrics. Other available properties affecting petrophysics are used to cluster facies associations in sub-classes.\u0000 Consequently the facies classification does not allow discriminating the distributions of porosity, permeability, nor p-wave velocity. For the rudist facies, that is the most sampled, texture subclasses do not help this work. Reversely, the place of sampling, that is likely a proxy of diagenesis and age, cluster the petrophysical distributions. The results remind us that a proper facies definition should consider both sedimentary origin, fabrics, texture, diagenesis and tectonics. They also point out the relative importance of each characteristics in regards of the scale of interest and the difficulty to infer upscaled relationships between rock properties from CCAL because the representative elementary volume of carbonates is usually higher than the plug and even the core volumes.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86525916","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}
To increase the likelihood of success of acid stimulation in limestone reservoirs, the treatment has to evenly cover the desired zone to allow controlled reaction rates that can result in a uniform conductivity pattern, or wormholes development radially across the pay zone. To achieve this ultimate goal, effective fluid diversion is required to reorient fluid path, from high to low injectivity areas. The selection of the right diversion technique is the key to obtaining successful stimulation results. Therefore, The objective of this work is to evaluate, and compare the stimulation efficiency of several diversion scenarios based on a highly reliable physics-based tool capable of simulating multiple completion types. This work will be focused on two typical diversion methods applicable to perforated completions, such as: 1) ball sealers, and 2) bio-degradable particles. A coupled model that consists of wellbore and reservoir flow is used to simulate acid, and limestone rock interactions for each diversion method. The model simulates fluid hydraulics in the wellbore, couples it with transient reservoir flow, and accounts for the formation skin effects derived from each diversion technique. The model also considers the effect of induced wormholes generation and the created injection profile along the completed reservoir zone. A horizontal well completion is presented to demonstrate the impact of each diversion approach in order to assess the effectiveness of a stimulation design. The most effective sensitivity combination of each diversion method is the focus of this work, and the treatment invasion distribution across the completed interval is compared to determine the best diversion approach. Different ball sealers geometries are considered to model irregular-shape perforation plugging efficiency and subsequent fluid diversion. The enhanced ball sealers model considers several physics parameters such as: inertial force, drag force, and ball-holding force along the wellbore during stimulation. On the other hand, the particulate diversion model includes an engineering model that is integrated into the wellbore-reservoir model to simulate the particle diversion. The particulate diversion model is a binary system that consists of: (1) large particles agglomerate along the tapered path of wormhole, and perforations, and (2) small particles jamming effect to create a temporary sealed structure that reduces the permeability of flow path and builds a temporary filter cake on perforations that is capable of holding up necessary differential pressures to divert fluid to other low-injectivity zones. The results show that the diversion efficiency depends basically on the length, perforationsconfiguration, and the reservoir heterogeneity. This case study demonstrates that particulate diversion offers the best alternative in terms of economic feasibility, and ease of application. The current tool has the unique capability combined with an integrated appr
{"title":"Optimizing Limestone Acidizing Treatments in Perforated Horizontal Completions by Implementing a Physics-Based Tool","authors":"Oswaldo Perez, F. Fragachán, Andrew G. Babey","doi":"10.2118/197719-ms","DOIUrl":"https://doi.org/10.2118/197719-ms","url":null,"abstract":"\u0000 To increase the likelihood of success of acid stimulation in limestone reservoirs, the treatment has to evenly cover the desired zone to allow controlled reaction rates that can result in a uniform conductivity pattern, or wormholes development radially across the pay zone. To achieve this ultimate goal, effective fluid diversion is required to reorient fluid path, from high to low injectivity areas. The selection of the right diversion technique is the key to obtaining successful stimulation results. Therefore, The objective of this work is to evaluate, and compare the stimulation efficiency of several diversion scenarios based on a highly reliable physics-based tool capable of simulating multiple completion types.\u0000 This work will be focused on two typical diversion methods applicable to perforated completions, such as: 1) ball sealers, and 2) bio-degradable particles. A coupled model that consists of wellbore and reservoir flow is used to simulate acid, and limestone rock interactions for each diversion method. The model simulates fluid hydraulics in the wellbore, couples it with transient reservoir flow, and accounts for the formation skin effects derived from each diversion technique. The model also considers the effect of induced wormholes generation and the created injection profile along the completed reservoir zone.\u0000 A horizontal well completion is presented to demonstrate the impact of each diversion approach in order to assess the effectiveness of a stimulation design. The most effective sensitivity combination of each diversion method is the focus of this work, and the treatment invasion distribution across the completed interval is compared to determine the best diversion approach. Different ball sealers geometries are considered to model irregular-shape perforation plugging efficiency and subsequent fluid diversion. The enhanced ball sealers model considers several physics parameters such as: inertial force, drag force, and ball-holding force along the wellbore during stimulation. On the other hand, the particulate diversion model includes an engineering model that is integrated into the wellbore-reservoir model to simulate the particle diversion.\u0000 The particulate diversion model is a binary system that consists of: (1) large particles agglomerate along the tapered path of wormhole, and perforations, and (2) small particles jamming effect to create a temporary sealed structure that reduces the permeability of flow path and builds a temporary filter cake on perforations that is capable of holding up necessary differential pressures to divert fluid to other low-injectivity zones.\u0000 The results show that the diversion efficiency depends basically on the length, perforationsconfiguration, and the reservoir heterogeneity. This case study demonstrates that particulate diversion offers the best alternative in terms of economic feasibility, and ease of application.\u0000 The current tool has the unique capability combined with an integrated appr","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88238231","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}
Recent years have seen extensive applications of deep learning, especially in identification and analysis of images, audios and texts, but incipient applications in petroleum industry. Shapes of loops in power cards of a pumping unit are valuable indicators for pump troubles. These troubles may cause engineering accidents, increase operation costs and reduce operation efficiency. This paper applies image recognition technique based on Convolution Neural Network (CNN) to well pump troubleshooting via power cards. Recent years have seen extensive applications of deep learning, especially in identification and analysis of images, audios and texts, but incipient applications in petroleum industry. Shapes of loops in power cards of a pumping unit are valuable indicators for pump troubles. These troubles may cause engineering accidents, increase operation costs and reduce operation efficiency. This paper applies image recognition technique based on Convolution Neural Network (CNN) to well pump troubleshooting via power cards. Firstly, we establish mathematical models both for displacements of the polished rod clamp of a pump and for loads of the polished rod during a reciprocating movement, and preset input parameters corresponding to pump trouble types and severity levels. Ideal benchmarking power cards as the media for pump troubleshooting are generated by simulating complete pumping processes via running the mathematical models with the preset pumping parameters. Secondly, we establish a power card classification model with the AlexNet method. Then we train it with the ideal benchmarking power cards to develop its function of pump troubleshooting and increase the classification accuracy. This model gains robustness and universality from manually presetting parameters for and full coverage of trouble types and severity levels. Thirdly, we train the classification model with real power cards and obtain the preliminary classification results. A further training makes it more practical and applicable to local operations of pump troubleshooting. In the further training, we localize the ideal benchmarking power cards via manual inspection and local expertiseby adjusting the preliminary classification results honoring field expertise. Finally, we randomly divide the localized benchmarking power cards into one training set and one testing set, and then train the classification model with the training set and then apply it to the testing set. The final classification results revealthe high accuracy and practicability of the classification model. It is recommended that GPU should be used for calculation with the classification model to satisfy clients' requirements for higher speeds and efficiency. It provides a feasible method to exploit the potential value of oilfield data assets. The work in this paper will function as a stepping stone in applying ideas, algorithms and models of artificial intelligence to more extensive and thorough aims.
{"title":"Application of CNN Deep Learning to Well Pump Troubleshooting via Power Cards","authors":"Xiangguang Zhou, Chuanfeng Zhao, Xiao-hua Liu","doi":"10.2118/197733-ms","DOIUrl":"https://doi.org/10.2118/197733-ms","url":null,"abstract":"\u0000 Recent years have seen extensive applications of deep learning, especially in identification and analysis of images, audios and texts, but incipient applications in petroleum industry. Shapes of loops in power cards of a pumping unit are valuable indicators for pump troubles. These troubles may cause engineering accidents, increase operation costs and reduce operation efficiency. This paper applies image recognition technique based on Convolution Neural Network (CNN) to well pump troubleshooting via power cards.\u0000 Recent years have seen extensive applications of deep learning, especially in identification and analysis of images, audios and texts, but incipient applications in petroleum industry. Shapes of loops in power cards of a pumping unit are valuable indicators for pump troubles. These troubles may cause engineering accidents, increase operation costs and reduce operation efficiency. This paper applies image recognition technique based on Convolution Neural Network (CNN) to well pump troubleshooting via power cards.\u0000 Firstly, we establish mathematical models both for displacements of the polished rod clamp of a pump and for loads of the polished rod during a reciprocating movement, and preset input parameters corresponding to pump trouble types and severity levels. Ideal benchmarking power cards as the media for pump troubleshooting are generated by simulating complete pumping processes via running the mathematical models with the preset pumping parameters.\u0000 Secondly, we establish a power card classification model with the AlexNet method. Then we train it with the ideal benchmarking power cards to develop its function of pump troubleshooting and increase the classification accuracy. This model gains robustness and universality from manually presetting parameters for and full coverage of trouble types and severity levels.\u0000 Thirdly, we train the classification model with real power cards and obtain the preliminary classification results. A further training makes it more practical and applicable to local operations of pump troubleshooting. In the further training, we localize the ideal benchmarking power cards via manual inspection and local expertiseby adjusting the preliminary classification results honoring field expertise.\u0000 Finally, we randomly divide the localized benchmarking power cards into one training set and one testing set, and then train the classification model with the training set and then apply it to the testing set. The final classification results revealthe high accuracy and practicability of the classification model.\u0000 It is recommended that GPU should be used for calculation with the classification model to satisfy clients' requirements for higher speeds and efficiency. It provides a feasible method to exploit the potential value of oilfield data assets.\u0000 The work in this paper will function as a stepping stone in applying ideas, algorithms and models of artificial intelligence to more extensive and thorough aims.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88546267","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}
Luca Cadei, A. Corneo, D. Milana, D. Loffreno, Lorenzo Lancia, M. Montini, Gianmarco Rossi, Elisabetta Purlalli, Piero Fier, Francesco Carducci
The current oil and gas market is characterized by low prices, high uncertainties and a subsequent reduction in new investments. This leads to an ever-increasing attention towards more efficient asset management. The fouling effect is considered one of the main problems drastically affecting asset integrity/efficiency and heat exchanger performances of critical machineries in upstream production plants. This paper illustrates the application of advanced big data analytics and innovative machine learning techniques to face this challenge. The optimal maintenance scheduling and the early identification of workflow-blocking events strongly impact the overall production, as they heavily contribute to the reduction of down-times. While, machine learning techniques proved to introduce significant advantages to these problems, they are fundamentally data-driven. In industry scenarios, where dealing with a limited amount of data is standard practice, this means forcing the use of simpler models that are often not able to disentangle the real dynamics of the phenomenon. The lack of data is generally caused by frequent changes in operating conditions/field layout or an insufficient instrumentation system. Moreover, the intrinsic long duration of many physical phenomena and the ordinary asset maintenance lifecycle, cause a critical reduction in the number of relevant events that can be learned. In this work, the fouling problem has been explored leveraging only limited data. The attention is focused on two different equipment: heat exchangers and re-boilers. While the formers involve slower dynamics, the latter are characterized by a steady phase followed by an abrupt deterioration. Moreover, the first ones allow a proper scheduling of cleaning interventions in advance. On the other hand, the second forces a much quicker plant stop. Finally, heat exchangers are characterized by few episodes of comparable deterioration, while re-boilers present only a single episode. Regarding heat exchangers, a dual approach has been followed, merging a short-term, time-series-based model, and a long-term one based on linear regression. After having isolated a number of training regions related to the fouling episodes that showed a characteristic behavior, it is possible to obtain accurate results in the short-term and to capture the general trend in the long-term. In the case of re-boilers, a novelty detection approach has been adopted: first, the model learns the equipment normal behavior, then it uses the features learned to detect anomalies. This continuous training-predicting iteration also leverages the user feedback to adapt to new operating conditions. Results show that in an "young digital" industry, the use of limited data together with simpler machine learning techniques, can successfully become an automatic diagnostics tool supporting the operators to improve traditional maintenance activities as well as optimize the production rate, and finally the asset e
{"title":"Advanced Analytics for Predictive Maintenance with Limited Data: Exploring the Fouling Problem in Heat Exchanging Equipment","authors":"Luca Cadei, A. Corneo, D. Milana, D. Loffreno, Lorenzo Lancia, M. Montini, Gianmarco Rossi, Elisabetta Purlalli, Piero Fier, Francesco Carducci","doi":"10.2118/197355-ms","DOIUrl":"https://doi.org/10.2118/197355-ms","url":null,"abstract":"\u0000 The current oil and gas market is characterized by low prices, high uncertainties and a subsequent reduction in new investments. This leads to an ever-increasing attention towards more efficient asset management. The fouling effect is considered one of the main problems drastically affecting asset integrity/efficiency and heat exchanger performances of critical machineries in upstream production plants. This paper illustrates the application of advanced big data analytics and innovative machine learning techniques to face this challenge.\u0000 The optimal maintenance scheduling and the early identification of workflow-blocking events strongly impact the overall production, as they heavily contribute to the reduction of down-times. While, machine learning techniques proved to introduce significant advantages to these problems, they are fundamentally data-driven. In industry scenarios, where dealing with a limited amount of data is standard practice, this means forcing the use of simpler models that are often not able to disentangle the real dynamics of the phenomenon. The lack of data is generally caused by frequent changes in operating conditions/field layout or an insufficient instrumentation system. Moreover, the intrinsic long duration of many physical phenomena and the ordinary asset maintenance lifecycle, cause a critical reduction in the number of relevant events that can be learned.\u0000 In this work, the fouling problem has been explored leveraging only limited data. The attention is focused on two different equipment: heat exchangers and re-boilers. While the formers involve slower dynamics, the latter are characterized by a steady phase followed by an abrupt deterioration. Moreover, the first ones allow a proper scheduling of cleaning interventions in advance. On the other hand, the second forces a much quicker plant stop. Finally, heat exchangers are characterized by few episodes of comparable deterioration, while re-boilers present only a single episode. Regarding heat exchangers, a dual approach has been followed, merging a short-term, time-series-based model, and a long-term one based on linear regression. After having isolated a number of training regions related to the fouling episodes that showed a characteristic behavior, it is possible to obtain accurate results in the short-term and to capture the general trend in the long-term. In the case of re-boilers, a novelty detection approach has been adopted: first, the model learns the equipment normal behavior, then it uses the features learned to detect anomalies. This continuous training-predicting iteration also leverages the user feedback to adapt to new operating conditions.\u0000 Results show that in an \"young digital\" industry, the use of limited data together with simpler machine learning techniques, can successfully become an automatic diagnostics tool supporting the operators to improve traditional maintenance activities as well as optimize the production rate, and finally the asset e","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88096598","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}
Zhang Hongying, Huihui Chen, Feng Liu, Na Wang, Huang Yanfu, Zhimeng Fang, X. Pan
A novel design of PDC bit with hollowed cutters is presented that uses the principle of hydraulic lubricating and water jetting mechanism to improve ROP. Engineering results show its advantage compared to solid fixed cutters which are commonly used in today’s drilling industry. The structure of the new PDC bit is as follows: each cutter contains a specific fluid channel, which is tailor made; the bit body contains many fluid channels; Compared with the existing conventional PDC bit, the main distinctive features are: the cutters are hollowed, each cutter contains a fluid channel in the centre of itself; and the bit body has fluid passages which are communicating with each hollowed cutter, allowing the drilling fluid flows from the inside of the bit to the outside of each cutter. The failures of a PDC bit are mostly due to premature wear or cracks of compound cutters; the wear or cracks are due to mechanical and thermal effects. To improve the service life of cutters can effectively increase the life of the drill bit. According to thermal stress analysis, the position where the frictional heat concentrated is in the centre of the cutter, which will result in the generation and expansion of thermal cracks, which in turn leads to failure of the cutter and loss of ROP. Therefore, the cutter with fluid passage will improve the way of thermal concentration and expansion, thereby prolonging the life of the drill bit, reducing the number of trips, improving single run drilling footage, therefore the drilling efficiency is increased. During the drilling operation, the rock cuttings cannot flow out timely, and may accumulate on the drill bit, which is commonly referred to as mud balling. The presence of mud balling will reduce the cutting capability of the drill bit and decrease the ROP. The new PDC bit with hollowed cutters has self-cooling and self-cleaning functions to mitigate the thermal effect, while the pressurized flow from the micro-hole of the cutter has the effect of water jetting, which in turn increases the ROP. The novelty of the new PDC bit is in the capability to solve the issues of low ROP and short service life of today’s tough drilling conditions, to meet the requirement of a single trip to complete the total depth.
{"title":"New PDC Bit with Hollowed Cutters: with Increased ROP and Longer Service Life","authors":"Zhang Hongying, Huihui Chen, Feng Liu, Na Wang, Huang Yanfu, Zhimeng Fang, X. Pan","doi":"10.2118/197771-ms","DOIUrl":"https://doi.org/10.2118/197771-ms","url":null,"abstract":"\u0000 A novel design of PDC bit with hollowed cutters is presented that uses the principle of hydraulic lubricating and water jetting mechanism to improve ROP. Engineering results show its advantage compared to solid fixed cutters which are commonly used in today’s drilling industry.\u0000 The structure of the new PDC bit is as follows: each cutter contains a specific fluid channel, which is tailor made; the bit body contains many fluid channels; Compared with the existing conventional PDC bit, the main distinctive features are: the cutters are hollowed, each cutter contains a fluid channel in the centre of itself; and the bit body has fluid passages which are communicating with each hollowed cutter, allowing the drilling fluid flows from the inside of the bit to the outside of each cutter.\u0000 The failures of a PDC bit are mostly due to premature wear or cracks of compound cutters; the wear or cracks are due to mechanical and thermal effects. To improve the service life of cutters can effectively increase the life of the drill bit. According to thermal stress analysis, the position where the frictional heat concentrated is in the centre of the cutter, which will result in the generation and expansion of thermal cracks, which in turn leads to failure of the cutter and loss of ROP. Therefore, the cutter with fluid passage will improve the way of thermal concentration and expansion, thereby prolonging the life of the drill bit, reducing the number of trips, improving single run drilling footage, therefore the drilling efficiency is increased. During the drilling operation, the rock cuttings cannot flow out timely, and may accumulate on the drill bit, which is commonly referred to as mud balling. The presence of mud balling will reduce the cutting capability of the drill bit and decrease the ROP. The new PDC bit with hollowed cutters has self-cooling and self-cleaning functions to mitigate the thermal effect, while the pressurized flow from the micro-hole of the cutter has the effect of water jetting, which in turn increases the ROP.\u0000 The novelty of the new PDC bit is in the capability to solve the issues of low ROP and short service life of today’s tough drilling conditions, to meet the requirement of a single trip to complete the total depth.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82238938","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}
M. Battashi, Saada Al Shukaili, Sa'ud Al Balushi, Khalid Al Hatmi, As'ad Al Mashrafi
Crude oil production from ageing oil fields is normally sustained by various enhanced oil recovery (EOR) ways such as water injection, polymer injection and alkaline surfactant polymer injection (ASP). One of the main ageing fields (90% water cut) in Oman is considered for this study. This field is being operated with waterflood for more than 15 years. In order to enhance the oil recovery in this field, chemical enhanced oil recovery (cEOR) using polymer flood was implemented in 2010 by Petroleum Development Oman (PDO) Company. ASP is a recovery method planned as a final resort of cEOR to recover more oil from the studied field. ASP breakthrough is expected to impact the performance of deoiling facilities in this field. Results showed that using ceramic membrane (100 nm pore size) managed to remove oil from produced water completely for high and low OiW concentrations, however only 2% recovery factor was achieved. Using aluminum sulfate chemical as a coagulant to treat the oily produced water was only effective at concentration higher 500 mg/L. In comparison, aluminum sulfate was very effective in treating the produced water contaminated by polymer (500 ppm of polymer concentration) and at 150 mg/L of aluminum sulfate, the outlet OiW reached 39 ppm (v). When Aluminum sulfate was used at concentration of 500 mg/l, the OiW concentration reached 2 ppm (v), which is lower than the polishing unit in the water treatment system. When ASP was introduced to the produced water, the oil droplet stability has increased and at 500 mg/L of aluminum sulfate, the outlet OiW in the treated stream was around 65 ppm (v) however at 700 mg/L of aluminum sulfate, zero ppm of OiW was achieved. Introducing gas bubbles (N2) as flotation with the help of the coagulant agent had improved OiW removal efficiency by almost 15% for the PW with ASP.
{"title":"Treatment of Produced Water with Back Produced ASP","authors":"M. Battashi, Saada Al Shukaili, Sa'ud Al Balushi, Khalid Al Hatmi, As'ad Al Mashrafi","doi":"10.2118/197658-ms","DOIUrl":"https://doi.org/10.2118/197658-ms","url":null,"abstract":"\u0000 Crude oil production from ageing oil fields is normally sustained by various enhanced oil recovery (EOR) ways such as water injection, polymer injection and alkaline surfactant polymer injection (ASP). One of the main ageing fields (90% water cut) in Oman is considered for this study. This field is being operated with waterflood for more than 15 years. In order to enhance the oil recovery in this field, chemical enhanced oil recovery (cEOR) using polymer flood was implemented in 2010 by Petroleum Development Oman (PDO) Company. ASP is a recovery method planned as a final resort of cEOR to recover more oil from the studied field. ASP breakthrough is expected to impact the performance of deoiling facilities in this field.\u0000 Results showed that using ceramic membrane (100 nm pore size) managed to remove oil from produced water completely for high and low OiW concentrations, however only 2% recovery factor was achieved. Using aluminum sulfate chemical as a coagulant to treat the oily produced water was only effective at concentration higher 500 mg/L. In comparison, aluminum sulfate was very effective in treating the produced water contaminated by polymer (500 ppm of polymer concentration) and at 150 mg/L of aluminum sulfate, the outlet OiW reached 39 ppm (v). When Aluminum sulfate was used at concentration of 500 mg/l, the OiW concentration reached 2 ppm (v), which is lower than the polishing unit in the water treatment system. When ASP was introduced to the produced water, the oil droplet stability has increased and at 500 mg/L of aluminum sulfate, the outlet OiW in the treated stream was around 65 ppm (v) however at 700 mg/L of aluminum sulfate, zero ppm of OiW was achieved. Introducing gas bubbles (N2) as flotation with the help of the coagulant agent had improved OiW removal efficiency by almost 15% for the PW with ASP.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72709807","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}
Galymzhan Konysbekuly, Biju James, A. Lomov, D. Gumich, Gaukharbek Ungaliev
Finding a safe and efficient approach to drilling in challenging applications is a difficult task for drillers because each field is unique. The industry's common ambition is to use various technologies to increase the mechanical penetration rate and reduce overall drilling time. Most recent studies show that drilling bits play an important role in drilling optimization and help to overcome most of the challenges connected with the rock destruction process and tool lifecycle. In recent years, 3D PDC cutters such as ridge diamond elements (RDE), rolling PDC cutters (RC), and conical diamond elements (CDE) have helped to further improve the drilling efficiency in a majority of applications worldwide. The PDC bit brazed with unique 3D cutters moved the industry set benchmark performance standards to the next level by improving cutter durability and efficiency in drilling. These cutters, depending on the shape, can improve ROP, durability, and can improve overall cutting efficiency. Field tests were conducted in multiple applications with multiple customers in RCA and the authors will present several case studies that will document performance improvement in challenging drilling applications. The results clearly show that the combination of this unique 3D cutter has helped operators to bring a step change in performance by improving ROP and footage drilled. In some cases, operators were able to drill the entire section with the bits equipped with 3D cutter combinations where traditionally more than one bit was used to complete the section. Customization of 3D cutters in the appropriate location of the bit is key to this success.
{"title":"Unique Fit for Purpose Combination of Three Dimensional Cutters Enabled Operators to Reduce Drilling Cost by Improving ROP and Footage Drilled","authors":"Galymzhan Konysbekuly, Biju James, A. Lomov, D. Gumich, Gaukharbek Ungaliev","doi":"10.2118/197542-ms","DOIUrl":"https://doi.org/10.2118/197542-ms","url":null,"abstract":"\u0000 Finding a safe and efficient approach to drilling in challenging applications is a difficult task for drillers because each field is unique. The industry's common ambition is to use various technologies to increase the mechanical penetration rate and reduce overall drilling time. Most recent studies show that drilling bits play an important role in drilling optimization and help to overcome most of the challenges connected with the rock destruction process and tool lifecycle.\u0000 In recent years, 3D PDC cutters such as ridge diamond elements (RDE), rolling PDC cutters (RC), and conical diamond elements (CDE) have helped to further improve the drilling efficiency in a majority of applications worldwide.\u0000 The PDC bit brazed with unique 3D cutters moved the industry set benchmark performance standards to the next level by improving cutter durability and efficiency in drilling. These cutters, depending on the shape, can improve ROP, durability, and can improve overall cutting efficiency.\u0000 Field tests were conducted in multiple applications with multiple customers in RCA and the authors will present several case studies that will document performance improvement in challenging drilling applications. The results clearly show that the combination of this unique 3D cutter has helped operators to bring a step change in performance by improving ROP and footage drilled. In some cases, operators were able to drill the entire section with the bits equipped with 3D cutter combinations where traditionally more than one bit was used to complete the section. Customization of 3D cutters in the appropriate location of the bit is key to this success.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73245719","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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