Application of machine learning models for real-time prediction of the formation lithology and tops from the drilling parameters

AA Mahmoud, S Elkatatny, A Al-AbdulJabbar - Journal of Petroleum …, 2021 - Elsevier
Lithology changes significantly affect the drilling program and the total cost of drilling an oil
well, therefore, it is very important to detect the lithology variation and formation tops while …

Rate of penetration prediction while drilling vertical complex lithology using an ensemble learning model

A Alsaihati, S Elkatatny, H Gamal - Journal of Petroleum Science and …, 2022 - Elsevier
The rate of penetration (ROP) accounts for a substantial portion of the overall drilling cost.
The drilling optimization process, which mostly involves the adjustment of the mechanical …

Application of artificial neural network to predict the rate of penetration for S-shape well profile

A Al-Abduljabbar, H Gamal, S Elkatatny - Arabian Journal of Geosciences, 2020 - Springer
The rate of penetration (ROP) is defined as the required speed to break the drilled rock by
the bit action. The existing established models for estimating the rate of penetration include …

[HTML][HTML] Hybrid data driven drilling and rate of penetration optimization

AM Alali, MF Abughaban, BM Aman… - Journal of Petroleum …, 2021 - Elsevier
Optimizing the drilling process for cost and efficiency requires faster drilling with a higher
rate of penetration (ROP). A high ROP usually indicates fast and cost-efficient drilling …

Real-time prediction of Poisson's ratio from drilling parameters using machine learning tools

O Siddig, H Gamal, S Elkatatny, A Abdulraheem - Scientific Reports, 2021 - nature.com
Rock elastic properties such as Poisson's ratio influence wellbore stability, in-situ stresses
estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson's …

Rock drillability intelligent prediction for a complex lithology using artificial neural network

H Gamal, S Elkatatny, A Abdulraheem - Abu Dhabi International …, 2020 - onepetro.org
The fourth industrial revolution and its vision for developing and governing the technologies
supported artificial intelligence (AI) applications in the different petroleum industry …

Application of machine learning in evaluation of the static young's modulus for sandstone formations

AA Mahmoud, S Elkatatny, D Al Shehri - Sustainability, 2020 - mdpi.com
Prediction of the mechanical characteristics of the reservoir formations, such as static
Young's modulus (Estatic), is very important for the evaluation of the wellbore stability and …

Artificial neural network model for real-time prediction of the rate of penetration while horizontally drilling natural gas-bearing sandstone formations

A Al-AbdulJabbar, AA Mahmoud… - Arabian Journal of …, 2021 - Springer
Rate of penetration (ROP) is a critical parameter affecting the total cost of drilling an oil well.
This study introduces an empirical equation developed based on the optimized artificial …

Applying different artificial intelligence techniques in dynamic Poisson's ratio prediction using drilling parameters

O Siddig, H Gamal, S Elkatatny… - Journal of Energy …, 2022 - asmedigitalcollection.asme.org
Rock geomechanical properties impact wellbore stability, drilling performance, estimation of
in situ stresses, and design of hydraulic fracturing. One of these properties is Poisson's ratio …

A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models

M Najjarpour, H Jalalifar… - Advances in Geo-Energy …, 2021 - sciopen.com
Rate of penetration management is a matter of importance in drilling operations and it has
been used in some research studies. Although conventional approaches for rate of …