Drilling Dataset Exploration, Processing and Interpretation Using Volve Field Data

2020 ◽  
Author(s):  
Andrzej T. Tunkiel ◽  
Tomasz Wiktorski ◽  
Dan Sui
Keyword(s):  
Case Studies ◽  
Field Data ◽  
Data Science ◽  
Data Accessibility ◽  
Work Related ◽  
Energy Company ◽  
Science Projects ◽  
File Formats ◽  
Meaningful Analysis

Abstract In 2018 Equinor made an unprecedented step for an energy company and made a multi-terabyte dataset from Volve field open. However, there is a long way from downloading data to executing meaningful analysis. With no way of quickly evaluating the data due to its size and unfamiliar file formats the use of Volve data was so far limited. This paper presents our exploratory work related to the realtime drilling part of the dataset. We provide description of common obstacles and approaches for overcoming them. We also describe specific contents of the dataset for others to gauge the potential for case studies. We hope that this will lower the bar for Volve field data accessibility, promote research, and become a catalyst for other data science projects.

scholarly journals Citizens, Scientists, and Enablers: A Tripartite Model for Citizen Science Projects

Diversity ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 309
Author(s):  
Rhian A. Salmon ◽  
Samuel Rammell ◽  
Myfanwy T. Emeny ◽  
Stephen Hartley
Keyword(s):  
Case Studies ◽  
Nature Of Science ◽  
Citizen Science ◽  
Public Engagement ◽  
Science Communication ◽  
Tripartite Model ◽  
Science Project ◽  
Science Projects ◽  
Emerging Issues ◽  
Citizen Science Project

In this paper, we focus on different roles in citizen science projects, and their respective relationships. We propose a tripartite model that recognises not only citizens and scientists, but also an important third role, which we call the ‘enabler’. In doing so, we acknowledge that additional expertise and skillsets are often present in citizen science projects, but are frequently overlooked in associated literature. We interrogate this model by applying it to three case studies and explore how the success and sustainability of a citizen science project requires all roles to be acknowledged and interacting appropriately. In this era of ‘wicked problems’, the nature of science and science communication has become more complex. In order to address critical emerging issues, a greater number of stakeholders are engaging in multi-party partnerships and research is becoming increasingly interdisciplinary. Within this context, explicitly acknowledging the role and motivations of everyone involved can provide a framework for enhanced project transparency, delivery, evaluation and impact. By adapting our understanding of citizen science to better recognise the complexity of the organisational systems within which they operate, we propose an opportunity to strengthen the collaborative delivery of both valuable scientific research and public engagement.

Divide and recombine (D&R) data science projects for deep analysis of big data and high computational complexity

2018 ◽  
Vol 1 (1) ◽  
pp. 139-156 ◽  
Author(s):  
Wen-wen Tung ◽  
Ashrith Barthur ◽  
Matthew C. Bowers ◽  
Yuying Song ◽  
John Gerth ◽  
...  
Keyword(s):  
Big Data ◽  
Computational Complexity ◽  
Data Science ◽  
Science Projects ◽  
High Computational Complexity

Reserves and Resources Management: A Continuous Improvement of the Business Processes

2021 ◽  
Author(s):  
Aen Nuril Hadi ◽  
Stephen Leonardo ◽  
Khairul Anwar ◽  
Tuan Manotar Aritonang ◽  
Devialina Puspita Dewi ◽  
...  
Keyword(s):  
Portfolio Management ◽  
Business Processes ◽  
Oil And Gas ◽  
Data Accessibility ◽  
Resources Management ◽  
Energy Company ◽  
Petroleum Resources ◽  
Management And Development ◽  
International Assets ◽  
Analysis Platform

Abstract Managing oil and gas reserves and resources of Pertamina, an Indonesia state owned energy company, has always been challenging processes as the company's portfolio is sparsely located throughout Indonesia. Moreover, since November 2013 the company has also managed its international assets spread across three regions, namely Africa, Asia, and Middle East. In total, there are ~480 fields and ~870 geological structures with different degree of geological background, environment, uncertainty, and maturity of the fields/projects. Obviously, to cope with all those complexities, Reserves Management Department need to figure out its way to properly manage reserves and resources of the company. Reserves and resources of oil and gas is a key strategic priority of the company. It is aligned with both portfolio management and development and production of the assets. Company decision in terms of work program and budget is also mainly derived from reserves and resources potential of the projects. Therefore, to obtain a standardized reserves and resources management, the company has launched company guideline which is mainly influenced by SPE Petroleum Resources Management System (SPE-PRMS, 2007). SPE-PRMS is a project-based system, where reserves and resources estimation, categorization, and classification are on project basis. To properly manage all the projects, since 2019, Reserves Management Department introduced a tool named Project Box which enable reserves analyst to properly map all the projects, evaluate the portfolio, and monitor the progress of the project both in development and exploration phase. Furthermore, to be aligned with company's digitalization campaign, all the reserves and resources data, Project Box, and many other strategic information are stored and maintained in an in-house software named Promyst which will be launched in early 2021. This tool will enhance data accessibility for both management and analyst, increase data integrity and security, provide data analysis platform, moreover this software could also generate cost efficiency for the company. In 2017, along with the final version of the company guideline, the company has fully adapted PRMS. This paper will explain how the company adapt PRMS to its company guideline in which some necessary adjustments took place with respect to company's business processes, the application of Project Box throughout all company's subsidiaries, and implementation of Promyst along with its features and future projection of the software. The result and benefit of implementing those items will be explained in this paper. These extensive works performed by the Reserves Management Department contribute significantly to the company not only for technical but also for commercial aspects therefore promoting a good corporate strategic planning and decision making.

Analytics-as-a-Service (AaaS)

Web Services ◽  
2019 ◽  
pp. 1262-1281
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

Analytics-as-a-Service (AaaS)

2017 ◽  
pp. 26-45
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

Data Science Projects

2021 ◽  
pp. 325-357
Author(s):  
Vineet Raina ◽  
Srinath Krishnamurthy
Keyword(s):  
Data Science ◽  
Science Projects

VP115 Practical Issues Of Using Real-World Data In Effectiveness Research

2017 ◽  
Vol 33 (S1) ◽  
pp. 202-203
Author(s):  
Amr Makady ◽  
Heather Stegenga ◽  
Alexandre Joyeux ◽  
Michael Lees ◽  
Pall Jonsson
Keyword(s):  
Case Studies ◽  
Real World ◽  
Qualitative Content Analysis ◽  
Study Data ◽  
Effectiveness Research ◽  
Real World Data ◽  
Data Accessibility ◽  
World Data ◽  
Drug Effectiveness ◽  
Potential Use

INTRODUCTION:The Innovative Medicines Initiative, IMI-GetReal project aimed to explore incorporation of robust methods for real-world data (RWD) collection and synthesis earlier in the medicines development process, both by pharmaceutical companies and healthcare decision makers. The focus was on the potential use of RWD, alone or in combination with randomized controlled trials (RCTs), to demonstrate effectiveness of new interventions. Four case studies were conducted in multiple disease areas to examine methods for predicting drug effectiveness and the perspectives of different stakeholders on these methods. This study aimed to identify practical obstacles in accessing and using RWD and RCT data for effectiveness research conducted as part of these case studies.METHODS:Qualitative content analysis was conducted to identify and characterize key issues relating to accessing and analysing study data from external sources, both RWD and RCTs.RESULTS:Accessing RWD from registries proved difficult due to multiple reasons, including: complex and non-transparent application procedures, resistance from registry owners to discuss applications and datasets not being research-ready within project timeframes. There were also issues with the RWD eventually accessed, including a lack of individual participant data (IPD) and incomplete data. Where access to IPD from RCTs was obtainable, there were restrictions imposed on how it could be used. For example, it could not be used to target analysis on an individual product, but rather explore methodologies for data synthesis in a product-anonymised setting. This condition encouraged additional data sharing by other stakeholders.CONCLUSIONS:Despite the collaborative, multi-stakeholder nature of IMI-GetReal and proper disclosures with data owners, access to data proved challenging. Such barriers to data accessibility can delay effectiveness research, restrict opportunities for the development of methods incorporating RWD and diminish the potential use of RWD in decision making. Where data is intended to be used for this purpose, sufficient attention should be paid to these potential barriers.

Sign in / Sign up

Export Citation Format

Share Document