TITLE:
Determine Stability Wellbore Utilizing by Artificial Intelligence Systems and Estimation of Elastic Coefficients of Reservoir Rock
AUTHORS:
Habib Akhundi, Mohammad Ghafoori, Gholam-Reza Lashkaripour
KEYWORDS:
Elastic Coefficients, Borehole Stability, Shear Wave Velocity, Petrophysical Logs, Neural Networks, Caliper Log
JOURNAL NAME:
Open Journal of Geology,
Vol.5 No.3,
March
4,
2015
ABSTRACT: Rock elastic properties
such as Young’s modulus, Poisson?s ratio, plays an important role in various
stages upstream of such as borehole stability, hydraulic fracturing in
laboratory scale for observing mechanical properties of the reservoir rock
usually using conventional cores sample that obtained from underground in
reservoir condition. This method is the most common and most reliable way to
get the reservoir rock properties, but it has some weaknesses. Currently,
neural network techniques have replaced usual laboratory methods because they
can do a similar operation faster and more accurately. To obtain the elastic
coefficient, we should have compressional wave velocity (VP),
shear wave (Vs) and density bulk due to high cost of (Vs)
measurement and low real ability of estimation through the (Vp)
and porosity. Therefore in this study, neural networks were used as a suitable method for
estimating shear wave, and then elastic coefficients of reservoir rock using
different relationships were predicted. Neural network used in this study was
not like a black box because we used the results of multiple regression that
could easily modify prediction of (Vs) through appropriate
combination of data. The same information that were intended for multiple
regression were used as input in neural networks, and shear wave velocity was
obtained using (Vp) and well logging data in carbonate rocks.
The results showed that methods applied in this carbonate reservoir was
successful, so that shear wave velocity was predicted with about 92% and 95%
correlation coefficient in multiple regression and neural network method,
respectively.