A Statistical
Factor Analysis Approach to Reservoir Quality Prediction in the Tertiary Fluviolacustrine Sandstones of the Yaojin
II Field in the
Xu, Tianguang1,
Xiaoru Li2, Jincheng
Liu2 (1) IHS Energy,
An integrated geological and statistical
Factor Analysis method is applied in this study to quantitatively evaluate and
predict Tertiary fluviolacustrine sandstones of the Yaojin II field in western
Core samples from six wells are analyzed,
and resulted geological and petrophysical data are
used for the study of sedimentary facies, diagenesis, and wireline logging
correlation. Parameters, including porosity, permeability, and net thickness,
are selected to conduct the Factor Analysis. A total of 15,350 row data are
derived from wireline logging from more than 170
wells, and these row data are correlated with the core data. The following
steps are performed to complete the Factor Analysis: 1) calculate descriptive
statistics; 2) calculate a correlation matrix of all variables to be used in
the analysis; 3) extract principal components; 4) rotate factors to create a
more understandable factor structure; 5) assign factor scores; and 6)
interpretation.
The results of Factor Analysis indicate
that porosity and permeability are the principle components with a total
variance contribution of 91.28%. Reservoirs are classified as four categories
according to calculated factor scores. Good quality reservoirs are delta front
sandstones with an average factor score of 36.66, followed by delta plain and
marginal lacustrine sandstones with an average factor
score of 29.97 and 23.56, respectively. Diagensis,
especially compaction and cementation, also explains the distribution and trend
of factor scores.
AAPG Search and Discover Article #90063©2007 AAPG Annual Convention, Long Beach, California