Integrated Digital Production Platform with the Reservoir Dynamic Models for a Giant Gas Condensate Reservoir in Real Time

2020 ◽  
Author(s):  
Ayesha Ahmed Abdulla Salem Alsaeedi ◽  
Fahed Ahmed AlHarethi ◽  
Eduard Latypov ◽  
Muhammad Ali Arianto ◽  
Nagaraju Reddicharla ◽  
...  
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Gas Condensate ◽  
Production Platform ◽  
Digital Production ◽  
Condensate Reservoir

Leveraging Business Intelligence and Data Analytics in an Integrated Digital Production Platform to Unlock Optimization Potentials

2021 ◽  
Author(s):  
Ayesha Ahmed Abdulla Salem Alsaeedi ◽  
Manar Maher Mohamed Elabrashy ◽  
Mohamed Ali Alzeyoudi ◽  
Mohamed Mubarak Albadi ◽  
Sandeep Soni ◽  
...  
Keyword(s):  
Real Time ◽  
Business Intelligence ◽  
Data Analytics ◽  
System Optimization ◽  
End Users ◽  
Production Optimization ◽  
Gas Condensate ◽  
Production Platform ◽  
Digital Production ◽  
Multiple Variables

Abstract This paper discusses business intelligence algorithms and data analytics capabilities of an integrated digital production platform implemented in a giant gas condensate field. The advanced workflow focuses on helping the user navigate through the bulk of data to identify patterns and make predictions utilizing exception-based intelligence alarming. This helps derive insightful findings and provides recommendations for users to make efficient business decisions for achieving field potential optimization objectives. An Integrated digital production platform within a giant gas condensate field is implemented with numerous production optimization workflows encompassing daily well and facility performance monitoring and surveillance. The data integration within the systems is enhanced by integration with powerful Business Intelligence (BI) tools, enabling users to create customized dashboards, KPI screens, and exception-based alarm screens. An additional integration to the production platform is carried out with data from real-time sources like PI Asset Framework and corporate databases, improving the integrated production system's daily well and facility surveillance capabilities. The advanced integration of BI tools provided users with various opportunities to identify bottlenecks, production improvement chances, and troubleshooting areas by capitalizing insights from various dashboards and business KPI screens. Further, integrating these dashboards with several corporate data sources and a real-time asset data framework enabled users to harness maximized information embedded in the bulk of data. This also enabled end-users to harness maximized system potential, with all information available under a single collaborative platform. The integration powered by various inbuilt complex algorithms extended scripting capabilities, and enhanced visualization assisted the asset in realizing business KPIs requirements. Business intelligence algorithms in user interface established a drill-down approach to utilize information associated with multiple variables on top of one another. This allowed for the quick identification of trends and patterns in data. The customization approach helped the user to draw maximum information out of data as per their engineering requirements and current practices. This advanced integration facilitated users to minimize their efforts in traditional data analysis such as gathering, mapping, filtering, and plotting. With the help of these powerful features embedded in an integrated platform, the user was able to drive more focus on optimization and minimize time and effort on system configuration. This unique integration was one of its kind. An online integrated digital production platform comprising of wells, networks, and various workflows was integrated with business intelligence tools, thereby providing end-users tremendous opportunities related to system optimization.

Accelerating Value Through Company Wide Digital Field Implementation: A Success Story

2021 ◽  
Author(s):  
Afdzal Hizamal Abu Bakar ◽  
Muhamad Nasri Jamaluddin ◽  
Rizwan Musa ◽  
Roberto Fuenmayor ◽  
Rajesh Trivedi ◽  
...  
Keyword(s):  
Real Time ◽  
High Performance ◽  
Business Processes ◽  
Time Data ◽  
Digital Platform ◽  
Production Platform ◽  
Digital Production ◽  
Real Time Data ◽  
Operational Data ◽  
The Way

Abstract PETRONAS Upstream is cognizant of the need to provide a unified digital production platform to the entire upstream community. The digital platform enables the community to perform analytical, collaborative monitoring & surveillance, and optimization to sustain production and improve our operations. Digital Fields (DF) is a modern digital platform, integrating data and analytical services, that provides smarter insights and shed light on potential insights that contribute to better-informed decision-making and improves the way of working. A big data ecosystem and strong infrastructure is the foundation of this digital platform, allied to existing business processes, it allows frictionless secure flow of data, high performance, scalability to support business operations. Digital Fields platform provides the following features: Scalable to all PETRONAS fields operated in Malaysia as well as International assets Solution architecture that allows fast implementation of new solutions and insights A common company-wide platform with the same familiar user interface and user experience The critical aspect to liven up a digital production platform is to identify first the available data sources. Some fields have the luxury of transmitters and sensors installed at the well head, topside facilities and export pipelines where the data can be easily retrieved from the SCADA system for data acquisition. Some other fields with less real-time data luxury would keep their operational data inside some operational reporting documents. A smart report ingestion solution was developed with the means to transform unstructured data from the operational reports, which empowers the users with multiple options for data upload and consumption while ensuring a single, traceable and auditable source of truth. Digital Fields leverages the combination of Daily Operation Reports (DOR), data with different frequency, such as real-time data, with engineering models changing the way the users consume data and provides proactive actionable insights to accelerate tangible values for the business organization. The solution establishes the foundational digital capabilities for field operations and speeds up data-driven value opportunities from operational data and analytics at scale.

A novel approach to the real-time modelling of large urban drainage systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 19-24 ◽  
Author(s):  
Richard Norreys ◽  
Ian Cluckie
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Drainage System ◽  
Radar Data ◽  
Urban Drainage ◽  
Real Time Control ◽  
Empirical Modelling ◽  
Time Control ◽  
Novel Approach ◽  
Non Linear Systems

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

Towards real-time fire data synthesis using numerical simulations

2021 ◽  
pp. 073490412199344
Author(s):  
Wolfram Jahn ◽  
Frane Sazunic ◽  
Carlos Sing-Long
Keyword(s):  
Real Time ◽  
Computational Fluid Dynamic ◽  
Dynamic Models ◽  
Fluid Dynamic ◽  
Near Field ◽  
Computing Time ◽  
Temperature Correction ◽  
Dynamic Simulations ◽  
Conservation Of Energy ◽  
Fire Scenarios

Synthesising data from fire scenarios using fire simulations requires iterative running of these simulations. For real-time synthesising, faster-than-real-time simulations are thus necessary. In this article, different model types are assessed according to their complexity to determine the trade-off between the accuracy of the output and the required computing time. A threshold grid size for real-time computational fluid dynamic simulations is identified, and the implications of simplifying existing field fire models by turning off sub-models are assessed. In addition, a temperature correction for two zone models based on the conservation of energy of the hot layer is introduced, to account for spatial variations of temperature in the near field of the fire. The main conclusions are that real-time fire simulations with spatial resolution are possible and that it is not necessary to solve all fine-scale physics to reproduce temperature measurements accurately. There remains, however, a gap in performance between computational fluid dynamic models and zone models that must be explored to achieve faster-than-real-time fire simulations.

Optimal control of a novel uninterrupted multi-speed transmission for hybrid electric mining trucks

2019 ◽  
Vol 233 (12) ◽  
pp. 3235-3245 ◽  
Author(s):  
Weiwei Yang ◽  
Jiejunyi Liang ◽  
Jue Yang ◽  
Nong Zhang
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Control Strategy ◽  
Dynamic Models ◽  
Fuel Efficiency ◽  
Real Time Control ◽  
Traction Motor ◽  
Time Control ◽  
Power Split ◽  
Shift Strategy

Considering the energy consumption and specific performance requirements of mining trucks, a novel uninterrupted multi-speed transmission is proposed in this paper, which is composed of a power-split device, and a three-speed lay-shaft transmission with a traction motor. The power-split device is adapted to enhance the efficiency of the engine by adjusting the gear ratio continuously. The three-speed lay-shaft transmission is designed based on the efficiency map of traction motor to guarantee the drivability. The combination of the power-split device and three-speed lay-shaft transmission can realize uninterrupted gear shifting with the proposed shift strategy, which benefits from the proposed adjunct function by adequately compensating the torque hole. The detailed dynamic models of the system are built to verify the effectiveness of the proposed shift strategy. To evaluate the maximum fuel efficiency that the proposed uninterrupted multi-speed transmission could achieve, dynamic programming is implemented as the baseline. Due to the “dimension curse” of dynamic programming, a real-time control strategy is designed, which can significantly improve the computing efficiency. The simulation results demonstrate that the proposed uninterrupted multi-speed transmission with dynamic programming and real-time control strategy can improve fuel efficiency by 11.63% and 8.51% compared with conventional automated manual transmission system, respectively.

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