TITLE:
Prediction of Shear Wave Velocity Using Artificial Neural Network Technique, Multiple Regression and Petrophysical Data: A Case Study in Asmari Reservoir (SW Iran)
AUTHORS:
Habib Akhundi, Mohammad Ghafoori, Gholam-Reza Lashkaripour
KEYWORDS:
Shear Wave Velocity, Petrophysical Logs, Neural Networks, Multiple Regression, Asmari Reservoir
JOURNAL NAME:
Open Journal of Geology,
Vol.4 No.7,
July
21,
2014
ABSTRACT:
Shear wave velocity has
numerous applications in geomechanical, petrophysical and geophysical studies
of hydrocarbon reserves. However, data related to shear wave velocity isn’t
available for all wells, especially old wells and it is very important to estimate
this parameter using other well logging. Hence, lots of methods have been
developed to estimate these data using other available information of
reservoir. In this study, after processing and removing inappropriate
petrophysical data, we estimated petrophysical properties affecting shear wave
velocity of the reservoir and statistical methods were used to establish
relationship between effective petrophysical properties and shear wave
velocity. To predict (VS), first we used empirical
relationships and then multivariate regression methods and neural networks were
used. Multiple regression method is a powerful method that uses correlation
between available information and desired parameter. Using this method, we can
identify parameters affecting estimation of shear wave velocity. Neural
networks can also be trained quickly and present a stable model for predicting
shear wave velocity. For this reason, this method is known as “dynamic
regression” compared with multiple regression. Neural network used in this
study is not like a black box because we have used the results of multiple
regression that can easily modify prediction of shear wave velocity through
appropriate combination of data. The same information that was intended for
multiple regression was used as input in neural networks, and shear wave
velocity was obtained using compressional wave velocity and well logging data
(neutron, density, gamma and deep resistivity) in carbonate rocks. The results
show that methods applied in this carbonate reservoir was successful, so that
shear wave velocity was predicted with about 92 and 95 percents of correlation
coefficient in multiple regression and neural network method, respectively.
Therefore, we propose using these methods to estimate shear wave velocity in wells
without this parameter.