Related references
Note: Only part of the references are listed.Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm
Mohamed Riad Youcefi et al.
EARTH SCIENCE INFORMATICS (2020)
Rain optimization algorithm (ROA): A new metaheuristic method for drilling optimization solutions
Ali Reza Moazzeni et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2020)
Development of a new rate of penetration model using self-adaptive differential evolution-artificial neural network
Salaheldin Elkatatny
ARABIAN JOURNAL OF GEOSCIENCES (2019)
Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network
Mohammad Anemangely et al.
JOURNAL OF GEOPHYSICS AND ENGINEERING (2018)
Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models
Chiranth Hegde et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2017)
Estimating the Penetration Rate in Diamond Drilling in Laboratory Works Using the Regression and Artificial Neural Network Analysis
S. Kahraman
NEURAL PROCESSING LETTERS (2016)
Artificial Neural Network Model for Prediction of Drilling Rate of Penetration and Optimization of Parameters
Mahmood Bataee et al.
JOURNAL OF THE JAPAN PETROLEUM INSTITUTE (2014)
Improved Drilling Efficiency Technique Using Integrated PDM and PDC Bit Parameters
H. R. Motahhari et al.
JOURNAL OF CANADIAN PETROLEUM TECHNOLOGY (2010)
Bit Selection Optimization Using Artificial Intelligence Systems
S. Edalatkhah et al.
PETROLEUM SCIENCE AND TECHNOLOGY (2010)
A new approach based on ant colony optimization for daily Volt/Var control in distribution networks considering distributed generators
Taher Niknam
ENERGY CONVERSION AND MANAGEMENT (2008)
Ant colony optimization theory: A survey
M Dorigo et al.
THEORETICAL COMPUTER SCIENCE (2005)