Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
Abstract Rock elastic properties such as Poisson’s ratio influence wellbore stability, in-situ stresses estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson’s ratio estimation requires either laboratory experiments or derived from sonic logs, the main concerns o...
Main Authors: | Osama Siddig, Hany Gamal, Salaheldin Elkatatny, Abdulazeez Abdulraheem |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-06-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-021-92082-6 |
Similar Items
-
Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time
by: Hany Gamal, et al.
Published: (2021-01-01) -
Applications of Artificial Intelligence for Static Poisson’s Ratio Prediction While Drilling
by: Ashraf Ahmed, et al.
Published: (2021-01-01) -
Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System
by: Ahmed Alsabaa, et al.
Published: (2020-03-01) -
Exposure Time Impact on the Geomechanical Characteristics of Sandstone Formation during Horizontal Drilling
by: Hany Gamal, et al.
Published: (2020-05-01) -
Data-Driven Framework to Predict the Rheological Properties of CaCl<sub>2</sub> Brine-Based Drill-in Fluid Using Artificial Neural Network
by: Ahmed Gowida, et al.
Published: (2019-05-01)