Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools

A Gowida, S Elkatatny, H Gamal - Neural Computing and Applications, 2021 - Springer
Unconfined compressive strength (UCS) is a major mechanical parameter of the rock which
has an essential role in developing geomechanical models. It can be estimated directly by …

Rock strength prediction in real-time while drilling employing random forest and functional network techniques

H Gamal, A Alsaihati, S Elkatatny… - Journal of …, 2021 - asmedigitalcollection.asme.org
The rock unconfined compressive strength (UCS) is one of the key parameters for
geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by …

[PDF][PDF] Survey of measurement-while-drilling technology for small-diameter drilling machines

Z Li, KI Itakura, Y Ma - Electronic Journal of Geotechnical …, 2014 - researchgate.net
This paper surveys the field of measurement-while-drilling (MWD) technology for
smalldiameter drilling machines. Using this technology, mechanical data (eg, torque, thrust …

Geomechanical model and wellbore stability analysis utilizing acoustic impedance and reflection coefficient in a carbonate reservoir

H Bagheri, AA Tanha, F Doulati Ardejani… - Journal of Petroleum …, 2021 - Springer
One of the most important oil and gas drilling studies is wellbore stability analysis. The
purpose of this research is to investigate wellbore stability from a different perspective. To …

Application of artificial neural networks in prediction of uniaxial compressive strength of rocks using well logs and drilling data

A Asadi - Procedia Engineering, 2017 - Elsevier
It is critical to obtain the rock strength along the wellbore to control drilling problems such as
pipe sticking, tight hole, collapse and sand production. The purpose of this research is to …

Geomechanical Study and Wellbore Stability Analysis for Potential CO2 Storage into Devonian and Silurian Formations of Delaware Basin

ST Nguyen, TC Nguyen, H Yoo… - SPE Oklahoma City Oil …, 2023 - onepetro.org
The objective of this project is to construct a 1D mechanical earth model for the prospective
geological sequestration of carbon dioxide (CO2) into carbonate formations. The study …

Machine learning models for generating the drilled porosity log for composite formations

H Gamal, S Elkatatny, AA Mahmoud - Arabian Journal of Geosciences, 2021 - Springer
Determining the porosity of the drilled formation is a significant task for formation evaluation
purposes for further implementation in petroleum reservoir simulation and estimating the …

An improved drilling simulator for operations, research and training

VC Kelessidis, S Ahmed, A Koulidis - SPE Middle East Oil and Gas …, 2015 - onepetro.org
This work describes the functionalities of the drilling simulator to optimize well drilling
utilizing offset data offering also training modules for novice and experienced drillers. It fully …

Coal identification using neural networks with real-time coalbed methane drilling data

R Zhong, R Johnson, Z Chen, N Chand - The APPEA Journal, 2019 - CSIRO Publishing
Currently, coal is identified using coring data or log interpretation. Coring is the most
dependable methodology, but it is costly and its characterisation is expensive and time …

Machine Learning Techniques for Real-Time Prediction of Essential Rock Properties Whilst Drilling

KW Amadi, MT Alsaba, I Iyalla, R Prabhu… - SPE Nigeria Annual …, 2023 - onepetro.org
Wellbore instability is the most significant incident during the drilling of production sections
of most wells. Common problems such as wellbore collapse, tight hole, mechanical sticking …