Rock strength prediction in real-time while drilling employing random forest and functional network techniques
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 …
geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by …
Log data-driven model and feature ranking for water saturation prediction using machine learning approach
Log-based reservoir characterization is one of the widely used techniques to estimate the
reservoir properties and make decisions about future plans for hydrocarbon production. Use …
reservoir properties and make decisions about future plans for hydrocarbon production. Use …
Machine learning models for equivalent circulating density prediction from drilling data
Equivalent circulating density (ECD) is considered a critical parameter during the drilling
operation, as it could lead to severe problems related to the well control such as fracturing …
operation, as it could lead to severe problems related to the well control such as fracturing …
Development of new mathematical model for compressional and shear sonic times from wireline log data using artificial intelligence neural networks (white box)
Compressional (P-wave) and shear (S-wave) velocities are used to estimate the dynamic
geomechanical properties including: Poisson's ratio, Young's modulus, and Lamé …
geomechanical properties including: Poisson's ratio, Young's modulus, and Lamé …
[HTML][HTML] New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs
The porosity of the petroleum reservoirs is considered one of the most important parameters
in reserve estimation because it determines the effective volume of the hydrocarbon that is …
in reserve estimation because it determines the effective volume of the hydrocarbon that is …
Estimation of static young's modulus for sandstone formation using artificial neural networks
AA Mahmoud, S Elkatatny, A Ali, T Moussa - Energies, 2019 - mdpi.com
In this study, we used artificial neural networks (ANN) to estimate static Young's modulus
(Estatic) for sandstone formation from conventional well logs. ANN design parameters were …
(Estatic) for sandstone formation from conventional well logs. ANN design parameters were …
Neural network based mechanical earth modelling (MEM): a case study in Hassi Messaoud Field, Algeria
Accurate estimation of in-situ stresses is of great importance in the oil and gas industry from
the exploration to the field development and production phases. The collected logs and mini …
the exploration to the field development and production phases. The collected logs and mini …
A new technique to develop rock strength correlation using artificial intelligence tools
Unconfined compressive strength (UCS) is the key parameter to; estimate the insitu stresses
of the rock, design optimal hydraulic fracture geometry and avoid drilling problems like …
of the rock, design optimal hydraulic fracture geometry and avoid drilling problems like …
Rock drillability intelligent prediction for a complex lithology using artificial neural network
The fourth industrial revolution and its vision for developing and governing the technologies
supported artificial intelligence (AI) applications in the different petroleum industry …
supported artificial intelligence (AI) applications in the different petroleum industry …
Machine learning derived correlation to determine water saturation in complex lithologies
MR Khan, Z Tariq, A Abdulraheem - SPE Kingdom of Saudi Arabia …, 2018 - onepetro.org
Possibly the most underrated petrophysical parameter, the importance of water saturation
cannot be emphasized enough with a whole range of petrophysical as well as reservoir …
cannot be emphasized enough with a whole range of petrophysical as well as reservoir …