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Settling velocity of drill cuttings in drilling fluids: A review of experimental, numerical simulations and artificial intelligence studies

Powder technology, 2018-11, Vol.339, p.728-746

2018

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Rice husk and saw dust as filter loss control agents for water-based muds

Heliyon, 2019-07, Vol.5 (7), p.e02059-e02059, Article e02059

2019

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Artificial neural network model for predicting drill cuttings settling velocity

Petroleum, 2020-12, Vol.6 (4), p.340-352

2020

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Explicit Data-Based Model for Predicting Oil-Based Mud Viscosity at Downhole Conditions

ACS omega, 2024-02, Vol.9 (6), p.6684-6695

2024

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Modeling the downhole density of drilling muds using multigene genetic programming

Upstream Oil and Gas Technology, 2021-02, Vol.6, p.100030, Article 100030

2021

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Potentials of waste seashells as additives in drilling muds and in oil well cements

Cleaner Engineering and Technology, 2020-12, Vol.1, p.100008, Article 100008

2020

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Experimental Analysis of Persea americana as Filtration Loss Control Additive for Non-Aqueous Drilling Fluid

International journal of engineering research in Africa (Print), 2019-01, Vol.44, p.8-21

2019

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Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells

Journal of petroleum exploration and production technology, 2020-03, Vol.10 (3), p.1081-1095

2020

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Applications of artificial intelligence algorithms in artificial lift systems: A critical review

Flow measurement and instrumentation, 2024-07, Vol.97, p.102613, Article 102613

2024

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Explicit and Explainable Artificial Intelligent Model for Prediction of CO2 Molecular Diffusion Coefficient in Heavy Crude Oils and Bitumen

Results in engineering, 2024-11, p.103328, Article 103328

2024

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Carbon capture using ionic liquids: An explicit data driven model for carbon (IV) Oxide solubility estimation

Journal of cleaner production, 2024-09, Vol.472, p.143508, Article 143508

2024

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Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure

Chemical engineering research & design, 2024-05, Vol.205, p.672-694

2024

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Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines

Geoenergy Science and Engineering, 2024-02, Vol.233, p.212584, Article 212584

2024

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Drilling fluid design for depleted zone drilling: An integrated review of laboratory, field, modelling and cost studies

Geoenergy Science and Engineering, 2023-07, Vol.226, p.211706, Article 211706

2023

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Evaluation of influential parameters for supersonic dehydration of natural gas: Machine learning approach

Petroleum Research, 2022-09, Vol.7 (3), p.372-383

2022

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Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps

Journal of petroleum exploration and production technology, 2024-05, Vol.14 (5), p.1205-1233

2024

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Artificial intelligence techniques and their applications in drilling fluid engineering: A review

Journal of petroleum science & engineering, 2018-08, Vol.167, p.300-315

2018

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Using agro-waste materials as possible filter loss control agents in drilling muds: A review

Journal of petroleum science & engineering, 2018-04, Vol.163, p.185-198

2018

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Ultrasonic thermophysical enhancement of a novel nanodoped vapour compression fluid for high cooling efficiency

Thermal science and engineering progress, 2022-12, Vol.36, p.101485, Article 101485

2022

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Modelling oil and gas flow rate through chokes: A critical review of extant models

Journal of petroleum science & engineering, 2022-01, Vol.208, p.109775, Article 109775

2022

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Activation of Local Bentonitic Clays for Use as Viscosifiers in Water-based Drilling Fluids

Journal of scientific research and reports, 2016-01, Vol.12 (2), p.1-11

2016

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A critical review of drilling mud rheological models

Journal of petroleum science & engineering, 2021-08, Vol.203, p.108659, Article 108659

2021

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Optimized Matrix Acidizing Treatment Design to Enhance Well Productivity in Sandstone Reservoirs

International Journal of Engineering and Modern Technology, 2023-10, Vol.9 (2), p.147-169

2023

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Simplified Neural Network-Based Models for Oil Flow Rate Prediction

Petroleum Science and Engineering, 2024-09, Vol.8 (2), p.70-99

2024

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Phytochemical Screening and Antimicrobial Activity of Ethanoic Extract of Passiflora edulis var. flavicarpa Seed on Selected Pathogens

Universal journal of microbiology research, 2017-10, Vol.5 (3), p.35-39

2017

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Settling velocity of drill cuttings in drilling fluids: A review of experimental, numerical simulations and artificial intelligence studies

Rice husk and saw dust as filter loss control agents for water-based muds

Artificial neural network model for predicting drill cuttings settling velocity

Explicit Data-Based Model for Predicting Oil-Based Mud Viscosity at Downhole Conditions

Modeling the downhole density of drilling muds using multigene genetic programming

Potentials of waste seashells as additives in drilling muds and in oil well cements

Experimental Analysis of Persea americana as Filtration Loss Control Additive for Non-Aqueous Drilling Fluid

Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells

Applications of artificial intelligence algorithms in artificial lift systems: A critical review

Explicit and Explainable Artificial Intelligent Model for Prediction of CO2 Molecular Diffusion Coefficient in Heavy Crude Oils and Bitumen

Carbon capture using ionic liquids: An explicit data driven model for carbon (IV) Oxide solubility estimation

Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure

Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines

Drilling fluid design for depleted zone drilling: An integrated review of laboratory, field, modelling and cost studies

Evaluation of influential parameters for supersonic dehydration of natural gas: Machine learning approach

Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps

Artificial intelligence techniques and their applications in drilling fluid engineering: A review

Using agro-waste materials as possible filter loss control agents in drilling muds: A review

Ultrasonic thermophysical enhancement of a novel nanodoped vapour compression fluid for high cooling efficiency

Modelling oil and gas flow rate through chokes: A critical review of extant models

Activation of Local Bentonitic Clays for Use as Viscosifiers in Water-based Drilling Fluids

A critical review of drilling mud rheological models

Optimized Matrix Acidizing Treatment Design to Enhance Well Productivity in Sandstone Reservoirs

Simplified Neural Network-Based Models for Oil Flow Rate Prediction

Phytochemical Screening and Antimicrobial Activity of Ethanoic Extract of Passiflora edulis var. flavicarpa Seed on Selected Pathogens

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