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Document type:
Zeitschriftenaufsatz 
Author(s):
Liu, Wendi; Ikonnikova, Svetlana; Scott Hamlin, H.; Sivila, Livia; Pyrcz, Michael J. 
Non-TUM Co-author(s):
ja 
Title:
Demonstration and Mitigation of Spatial Sampling Bias for Machine-Learning Predictions 
Abstract:
Summary Machine learning provides powerful methods for inferential and predictive modeling of complicated multivariate relationships to support decision-making for spatial problems such as optimization of unconventional reservoir development. Current machine-learning methods have been widely used in exhaustive spatial data sets like satellite images. However, geological subsurface characterization is significantly different because it is conditioned by sparse, nonrepresentative sampling. These...    »
 
Intellectual Contribution:
Contribution to Practice 
Journal title:
SPE Reservoir Evaluation & Engineering 
Year:
2021 
Journal volume:
24 
Month:
February 
Journal issue:
01 
Pages contribution:
262--274 
Language:
en 
Fulltext / DOI:
Print-ISSN:
1094-6470, 1930-0212 
Peer reviewed:
Ja 
Technology:
Ja 
Interdisciplinarity:
Ja 
Mission statement:
Ethics and Sustainability:
Nein 
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