Improved Oil Recovery Estimation with Data Analytic Methods: A Workflow for Open Data Analysis

Author(s):  
Chad E. Kronkosky ◽  
Blake C. Kronkosky ◽  
Amin Ettehadtavakkol
2017 ◽  
Vol 27 (1) ◽  
pp. 126-134 ◽  
Author(s):  
Warren W. Tryon ◽  
Thanos Patelis ◽  
Michael Chajewski ◽  
Charles Lewis

Data analysis should be neutral relative to theory construction in order to be unbiased. Data analysis should not strongly favor one form of theory construction over others. Traditional approaches to theory construction prioritize simplicity of explanation based on parsimony using a few prominent statistically significant variables. Alternative web of causation explanations prioritize comprehensive explanations based on complexity using many small effects. This article presents argument and empirical evidence that contemporary data analytic methods are problematic for all theory construction approaches. They are especially biased against web of causation approaches to theory construction. Mathematical arguments and new empirical evidence that supports web of causation explanations are presented.


2019 ◽  
Author(s):  
Rachel A Searston ◽  
Matthew B Thompson ◽  
Samuel Gebhard Robson ◽  
Brooklyn Corbett ◽  
Gianni Ribeiro ◽  
...  

Across research areas, general issues of low statistical power, publication bias, undisclosed flexibility in data analysis, and researcher degrees of freedom, can be recipes for irreproducibility. To address the problem, a reform movement known as the ‘credibility revolution’ emphasises the need for greater transparency in how research is conducted. In this article, we describe a general approach to creating a culture of openness—tailored for expertise researchers—and describe how and why practices such as ‘preregistration,’ ‘open notebooks,’ ‘open data,’ ‘open materials,’ and ‘open communication,’ might be applied to research on experts. We argue that adopting these practices helps to connect end-users with the entire research lifecycle, and helps to reconnect researchers with the process of gaining knowledge. By sharing notes about our predictions and plans along the way, we are forced to confront their merits. By documenting design and data analytic decisions ahead of time, and by sharing data and materials, we make errors and insights more discoverable. And by inviting research partners, expert practitioners, and the public into the lab, we stand the best chance of successfully translating research into practice.


2017 ◽  
Vol 3 (3) ◽  
pp. 33-38 ◽  
Author(s):  
А.V. Аntuseva ◽  
Е.F. Kudina ◽  
G.G. Pechersky ◽  
Y.R. Kuskildina ◽  
А.V., Melgui ◽  
...  

2020 ◽  
Vol 7 ◽  
pp. 116-119
Author(s):  
R.N. Fakhretdinov ◽  
◽  
D.F. Selimov ◽  
A.A. Fatkullin ◽  
S.A. Tastemirov ◽  
...  

2020 ◽  
Author(s):  
Hala Abdulkareem Rasheed ◽  
Mohammed Abdulmunem Abdulhameed ◽  
Rasha Al Sahlanee

AAPG Bulletin ◽  
2017 ◽  
Vol 101 (01) ◽  
pp. 1-18 ◽  
Author(s):  
Mark Person ◽  
John L. Wilson ◽  
Norman Morrow ◽  
Vincent E.A. Post

2021 ◽  
Author(s):  
E. Joonaki ◽  
R. Burgass ◽  
B. Tohidi

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