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.

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

Data Analytics Tools: A User Perspective

2019 ◽  
Vol 18 (01) ◽  
pp. 1950002 ◽  
Author(s):  
Paul Town ◽  
Fadi Thabtah
Keyword(s):  
Data Analysis ◽  
Business Intelligence ◽  
Data Analytics ◽  
End Users ◽  
Point Of View ◽  
Future Research ◽  
User Perspective ◽  
Evaluative Criteria ◽  
Decision Making Processes ◽  
User Groups

Business Intelligence Tools (BI Tools) can be an intelligent way for individuals to undertake data analysis and reporting for guiding decision-making processes. There are many different BI Tools available in the market today, as well as information to assist organisations in evaluating their effectiveness. This paper focusses on two commercially available BI Tools: Tableau and Microsoft Power BI. It aims to determine which BI Tool is better for data analysis and reporting from an end user’s point of view. This paper undertakes an evaluation of both tools and compares which is more suitable for students using interface (navigation), cost, presence in the market, and available training and help as the evaluative criteria. Results produced in this paper found that overall, Tableau was more highly ranked than Power BI based on the evaluative criteria for end users for data analysis and reporting at least among the samples of the study. Tableau ranked higher than Power BI with its presence in the market, and available training and help. Power BI was rated more highly on its interface and both BI Tools were ranked the same in terms of cost to end users. This research is exploratory and may assist in formulating future research on BI Tools for specific user groups.

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.

Digital Production Optimization Boost for North Caspian Field With 3-Phase Real-Time Downhole Flow Metering System From Enhanced Intelligent Completion

2020 ◽  
Author(s):  
Mikhail Yur'evich Golenkin ◽  
Igor Aleksandrovich Bulygin ◽  
Alexey Petrovich Shapovalov ◽  
Aleksandr Aleksandrovich Zavyalov ◽  
Anna Andreevna Zhirkina ◽  
...  
Keyword(s):  
Real Time ◽  
Production Optimization ◽  
Flow Metering ◽  
Digital Production

Digital Production Optimization Boost for North Caspian Field With 3-Phase Real-Time Downhole Flow Metering System From Enhanced Intelligent Completion (Russian)

2020 ◽  
Author(s):  
Mikhail Yur'evich Golenkin ◽  
Igor Aleksandrovich Bulygin ◽  
Alexey Petrovich Shapovalov ◽  
Aleksandr Aleksandrovich Zavyalov ◽  
Anna Andreevna Zhirkina ◽  
...  
Keyword(s):  
Real Time ◽  
Production Optimization ◽  
Flow Metering ◽  
Digital Production

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

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