ABSTRACT: The rock petrophysical and geomechanical characteristics are highly required for different applications in the petroleum industry as reservoir modeling, drilling operation design, production, and field development plans. The sonic data is one of the common sources to determine the rock elastic properties and acquiring the sonic data from the lab experimental work, logging, and correlations are not effective way due to the time and cost besides the low accuracy for the correlation approach. Consequently, this paper targets to proposed an intelligent approach for determining the sonic logs from the drilling data using machine learning tools. The study proposes a new approach for utilizing the random forest technique for developing a sonic prediction model for real-time deployment in drilling operation. The model was developed using drilling and sonic data for composite drilled formations with different lithology, while the drilling data is the model inputs and compressional and shear velocities are the outputs. The results showed a strong prediction capability for the developed model as the correlation of coefficient is higher than 0.9 and the average absolute percentage error is below 1% between the actual and predicted values.
Skip Nav Destination
Sonic Logs Prediction in Real Time by Using Random Forest Technique
Hany Gamal;
Hany Gamal
King Fahd University of Petroleum & Minerals, Dhahran
Search for other works by this author on:
Ahmed Alsaihati;
Ahmed Alsaihati
King Fahd University of Petroleum & Minerals, Dhahran
Search for other works by this author on:
Salaheldin Elkatatny;
Salaheldin Elkatatny
King Fahd University of Petroleum & Minerals, Dhahran
Search for other works by this author on:
Abdulazeez Abdulraheem
Abdulazeez Abdulraheem
King Fahd University of Petroleum & Minerals, Dhahran
Search for other works by this author on:
Paper presented at the ARMA/DGS/SEG International Geomechanics Symposium, Virtual, November 2021.
Paper Number:
ARMA-IGS-21-106
Published:
November 01 2021
Citation
Gamal, Hany, Alsaihati, Ahmed, Elkatatny, Salaheldin, and Abdulazeez Abdulraheem. "Sonic Logs Prediction in Real Time by Using Random Forest Technique." Paper presented at the ARMA/DGS/SEG International Geomechanics Symposium, Virtual, November 2021.
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.
Could not validate captcha. Please try again.
Pay-Per-View Access
$20.00
Advertisement
76
Views
Advertisement
Suggested Reading
Advertisement