Static Poisson’s ratio plays a vital role in calculating the minimum and maximum horizontal stresses which are required to alleviate the risks associated with the drilling and production operations. Incorrect estimation of Static Poisson’s ratio may wrongly lead to inappropriate field development plans which consequently result in heavy investment decisions. Static Poisson’s ratio can be determined by retrieving cores throughout the depth of the reservoir section and performing laboratory tests, which are extremely expensive as well as time consuming. The objective of this paper is to develop a robust and an accurate model for estimating static Poisson’s ratio based on 610 core sample measurements and their corresponding wireline logs data using artificial neural network. The obtained results showed that the developed ANN model was able to predict the static Poisson’s ratio based on log data; bulk density, compressional time, and shear time. The developed ANN model can be used to estimate static Poisson’s ratio with high accuracy; the correlation coefficient was 0.98 and the average absolute error was 1.3%. In the absence of core data, the developed technique will help engineers to estimate a continuous profile of the static Poisson’s ratio and hence reduce the overall cost of the well.
Skip Nav Destination
51st U.S. Rock Mechanics/Geomechanics Symposium
June 25–28, 2017
San Francisco, California, USA
An Artificial Intelligent Approach to Predict Static Poisson's Ratio
Z. A. Abdulraheem Abdelwahab
;
Z. A. Abdulraheem Abdelwahab
KFUPM
Search for other works by this author on:
I. M. Mohamed
I. M. Mohamed
Advantek Waste Management Services
Search for other works by this author on:
Paper presented at the 51st U.S. Rock Mechanics/Geomechanics Symposium, San Francisco, California, USA, June 2017.
Paper Number:
ARMA-2017-0771
Published:
June 25 2017
Citation
Elkatatny, S. M., Tariq, Z., Mahmoud, M. A., Abdulraheem Abdelwahab, Z. A., Woldeamanuel, M., and I. M. Mohamed. "An Artificial Intelligent Approach to Predict Static Poisson's Ratio." Paper presented at the 51st U.S. Rock Mechanics/Geomechanics Symposium, San Francisco, California, USA, June 2017.
Download citation file:
Sign in
Don't already have an account? Register
Personal Account
You could not be signed in. Please check your username and password and try again.
Pay-Per-View Access
$20.00
Advertisement
0
Views
0
Citations
Advertisement
Suggested Reading
Advertisement