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References from Data-driven real-time prediction of cutting's transport index in directional wellbores. Local targets link to admitted publications; unresolved targets remain external evidence.
Selecting drilling fluid properties and flow rates for effective hole cleaning in high-angle and horizontal Wells
2000 · External reference
Unresolved reference
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Cutting concentration prediction in horizontal and deviated wells using artificial intelligence techniques
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Application of descriptive data analytics: how to properly select the best ranges of viscosity and flow rate for optimal hole cleaning?
2019 · External reference
A novel model for the real-time evaluation of hole-cleaning conditions with case studies
10.3390/en16134934 · 2023 · External reference
A novel efficient borehole cleaning model for optimizing drilling performance in real time
10.3390/app13137751 · 2023 · External reference
A novel automated model for evaluation of the efficiency of hole cleaning conditions during drilling operations
10.3390/app13116464 · 2023 · External reference
Hole cleaning during drilling oil and gas Wells: a review for hole-cleaning chemistry and engineering parameters
10.1155/2023/6688500 · 2023 · External reference
Hole-cleaning performance in non-vertical wellbores: a review of influences, models, drilling fluid types, and real-time applications
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Unresolved reference
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Unresolved reference
2007 · External reference
Unresolved reference
2006 · External reference
Computational prediction of the drilling rate of penetration (ROP): a comparison of various machine learning approaches and traditional models
10.1016/j.petrol.2021.110033 · 2022 · External reference
Multivariable functional interpolation and adaptive networks
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Radial basis function neural network based maximum power point tracking for photovoltaic brushless DC motor connected water pumping system
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A hybrid fuzzy logic/genetic algorithm model based on experimental data for estimation of cuttings concentration during drilling
10.1016/j.geoen.2023.212387 · 2023 · External reference
Estimation of downhole cuttings concentration from experimental data – Comparison of empirical and fuzzy logic models
10.1016/j.petrol.2021.109910 · 2022 · External reference
Physical and chemical characterization of drill cuttings: a review
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Advancement of artificial intelligence applications in hydrocarbon well drilling technology: a review
10.1016/j.asoc.2025.113129 · 2025 · External reference
Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids
10.1016/j.engappai.2023.106459 · 2023 · External reference
Unresolved reference
2025 · External reference
New optimizer using particle swarm theory
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Drillpipe eccentricity prediction during drilling directional Wells
2006 · External reference
Enhancing intrusion detection systems for IoT and cloud environments using a growth optimizer algorithm and conventional neural networks
10.3390/s23094430 · 2023 · External reference
Quadruple parameter adaptation growth optimizer with integrated distribution, confrontation, and balance features for optimization
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Unresolved reference
External reference
Cuttings bed height prediction in microhole horizontal Wells with artificial intelligence models
10.3390/en15228389 · 2022 · External reference
Multilayer feedforward networks are universal approximators
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Experimental evaluation of different influencing parameters on cutting transport performance (CTP) in deviated wells
10.1016/j.geogeo.2022.100110 · 2023 · External reference
Cuttings lifting coefficient model: a criteria for cuttings lifting and hole cleaning quality of mud in drilling optimization
2022 · External reference
Wellbore hydraulics and hole cleaning: optimization and digitalization
2021 · External reference
Deep learning
10.1038/nature14539 · 2015 · External reference
Prediction of the wall factor of arbitrary particle settling through various fluid media in a cylindrical tube using artificial intelligence
2014 · External reference
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2006 · External reference
The design of neuro-fuzzy networks using particle swarm optimization and recursive singular value decomposition
10.1016/j.neucom.2006.12.016 · 2007 · External reference
Flow-rate predictions for cleaning deviated Wells
1992 · External reference
Simple charts to determine hole cleaning requirements in deviated Wells
1994 · External reference
New hybrid hole cleaning model for vertical and deviated wells
10.1115/1.4045169 · 2020 · External reference
Data mining and knowledge discovery handbook, second
2005 · External reference
Well construction hydraulics in challenging environments
2011 · External reference
Unresolved reference
1992 · External reference
Fast learning in networks of locally-tuned processing units
10.1162/neco.1989.1.2.281 · 1989 · External reference
Review of cuttings transport in directional well drilling: systematic approach
2010 · External reference
Hydraulic conveying of solids in vertical pipes
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Application of a particle swarm optimization algorithm for determining optimum well location and type
10.1007/s10596-009-9142-1 · 2009 · External reference
Analysis of bed height in horizontal and highly-inclined wellbores by using artificial neural networks
2002 · External reference
Universal approximation using radial-basis-function networks
10.1162/neco.1991.3.2.246 · 1991 · External reference
Simplifying particle swarm optimization
10.1016/j.asoc.2009.08.029 · 2010 · External reference
Impact of drilling fluid viscosity, velocity and hole inclination on cuttings transport in horizontal and highly deviated wells
10.1007/s13202-012-0031-0 · 2012 · External reference
Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms
10.1016/j.petrol.2021.108425 · 2021 · External reference
Hole cleaning in large, high-angle wellbores
1994 · External reference
Modeling of gas viscosity at high pressure-high temperature conditions: integrating radial basis function neural network with evolutionary algorithms
10.1016/j.petrol.2021.109328 · 2022 · External reference
Effect of hole cleaning on drilling rate and performance
2004 · External reference
Hole cleaning prediction in foam drilling using artificial neural network and multiple linear regression
10.4236/gm.2014.41005 · 2014 · External reference
Learning representations by back-propagating errors
10.1038/323533a0 · 1986 · External reference
Hybrid particle swarm optimization and GMDH system
2009 · External reference
Drill cutting transport in full scale vertical annuli
10.2118/4514-pa · 1974 · External reference
Comparison of computational intelligence models for cuttings transport in horizontal and deviated wells
10.1016/j.petrol.2016.07.022 · 2016 · External reference
Unresolved reference
2010 · External reference
Unresolved reference
2025 · External reference
Fluid flow and efficient development technologies in unconventional reservoirs: state-of-the-art methods and future perspectives
10.46690/ager.2024.06.07 · 2024 · External reference
Variable interaction empirical relationships and machine learning provide complementary insight to experimental horizontal wellbore cleaning results
10.46690/ager.2023.09.05 · 2023 · External reference
Comparison between traditional neural networks and radial basis function networks
2011 · External reference
Optimization of hole cleaning in horizontal and inclined wellbores: a study with computational fluid dynamics
10.1016/j.petrol.2021.108993 · 2021 · External reference
Short-term electricity-load forecasting using a TSK-based extreme learning machine with knowledge representation
10.3390/en10101613 · 2017 · External reference
Improving hole cleaning using low density polyethylene beads at different mud circulation rates in different hole angles
10.1016/j.jngse.2018.11.012 · 2019 · External reference
Advantages of radial basis function networks for dynamic system design
10.1109/tie.2011.2164773 · 2011 · External reference
An experimental study of hole cleaning under simulated downhole conditions
2007 · External reference
A switching delayed PSO optimized extreme learning machine for short-term load forecasting
10.1016/j.neucom.2017.01.090 · 2017 · External reference
Understanding deep learning (still) requires rethinking generalization
10.1145/3446776 · 2021 · External reference
Growth optimizer: a powerful metaheuristic algorithm for solving continuous and discrete global optimization problems
10.1016/j.knosys.2022.110206 · 2023 · External reference
Cuttings transport: back reaming analysis based on a coupled layering-sliding mesh method via CFD
10.1016/j.petsci.2023.06.009 · 2023 · External reference
