Data mining and well logging interpretation: application to a conglomerate reservoir

Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We...

Full description

Saved in:
Bibliographic Details
Published in:Applied geophysics 2015-06, Vol.12 (2), p.263-272
Main Authors: Shi, Ning, Li, Hong-Qi, Luo, Wei-Ping
Format: Article
Language:English
Subjects:
Algorithms
Data logging
Data mining
Data transmission
Earth and Environmental Science
Earth Sciences
Feature extraction
Fuzzy
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Lithology
Nonlinear systems
Reservoirs
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and-bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.
ISSN:1672-7975
1993-0658
DOI:10.1007/s11770-015-0490-4