Using machine learning for hydrocarbon prospecting in Reconcavo Basin, Brazil
Name
1031985504-MIT.pdf
Description
Full printable version
Size
2.04 MB
Format
Adobe PDF
Checksum (MD5)
b1b6b7a5efbf544e73590c7d873b1bbd
Author(s)
Zhakiya, Elezhan
Advisor(s)
Bradford Hager.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
Machine Learning techniques are being widely used in Social Sciences to find connections amongst various variables. Machine Learning connects features across different fields that do not seem to have known mathematical relationships with each other. In natural resource prospecting, machine learning can be applied to connect geochemical, geophysical, and geological variables. However, the biggest challenge in machine learning remains obtaining the data to train the ML algorithms. Here, we have applied machine learning on data extracted from maps via image processing. While the overall accuracy of prediction remains as low as 33% at this stage, we see places where the algorithm can be improved and the accuracy increased.
Description
Thesis: S.B., Massachusetts Institute of Technology, Department of Earth, Atmospheric, and Planetary Sciences, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages [28]).
Subjects
Earth, Atmospheric, and Planetary Sciences.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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