Geomaterials

Geomaterials

ISSN Print: 2161-7538
ISSN Online: 2161-7546
www.scirp.org/journal/gm
E-mail: gm@scirp.org
"Hole Cleaning Prediction in Foam Drilling Using Artificial Neural Network and Multiple Linear Regression"
written by Reza Rooki, Faramarz Doulati Ardejani, Ali Moradzadeh,
published by Geomaterials, Vol.4 No.1, 2014
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] Rigorous modeling of frictional pressure loss in inclined annuli using artificial intelligence methods
Journal of Petroleum …, 2022
[2] A comprehensive study on artificial intelligence in oil and gas sector
Environmental Science and Pollution Research, 2022
[3] Machine learning for drilling applications: A review
Journal of Natural Gas Science and …, 2022
[4] Cuttings Bed Height Prediction in Microhole Horizontal Wells with Artificial Intelligence Models
Energies, 2022
[5] Physic Based Approach for Solid Transport in Deviated and Horizontal Well
International …, 2022
[6] Optimization of Flow Rate and Pipe Rotation Speed Considering Effective Cuttings Transport Using Data-Driven Models. Energies 2021, 14, 1484
2021
[7] Optimization of Flow Rate and Pipe Rotation Speed Considering Effective Cuttings Transport Using Data-Driven Models
2021
[8] Application of Response Surface Methodology and Box–Behnken Design for the Optimization of the Stability of Fibrous Dispersion Used in Drilling and Completion …
2021
[9] A systematic review of data science and machine learning applications to the oil and gas industry
Journal of Petroleum …, 2021
[10] Method for predicting cuttings transport using artificial neural networks in foam drilling
2020
[11] Cuttings Transport Modeling in Wellbore Annulus in Oil Drilling Operation Using Evolutionary Fuzzy System
2020
[12] Cutting concentration prediction in horizontal and deviated wells using artificial intelligence techniques
2019
[13] A review of technological advances and open challenges for oil and gas drilling systems engineering
2019
[14] OIL WELL PACK OFF DETECTION METHOD
2019
[15] Application of Machine Learning and Fuzzy Logic in Drilling and Estimating Rock and Fluid Properties
5th International Conference on Applied Research in Electrical, Mechanical & Mechatronics Engineering, 2019
[16] 人体足部跖围尺寸预测方法的比较研究
2019
[17] An Innovative System Architecture for Real-Time Monitoring and Alarming for Cutting Transport in Oil Well Drilling
2019
[18] Geomechanical Property Computation from Digital Rock Models and Comparison with Core Measurements
SPE Kingdom of Saudi Arabia …, 2018
[19] Development of Sustainable Methodologies in Product Design, Manufacturing and Education
Doctoral Thesis, 2018
[20] Self-learning control of automated drilling operations
2018
[21] Cuttings-transport modeling–part 1: specification of benchmark parameters with a Norwegian-continental-shelf perspective
2018
[22] Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies.
Strojniski Vestnik/Journal of Mechanical Engineering, 2018
[23] Artificial intelligence techniques and their applications in drilling fluid engineering: A review
Journal of Petroleum Science and Engineering, 2018
[24] Prediction of Cutting Concentration in Horizontal and Deviated Wells Using Support Vector Machine
2018
[25] Pressure Loss Estimation of Three-Phase Flow in Inclined Annuli for Underbalanced Drilling Condition using Artificial Intelligence
2017
[26] Prediction of frictional pressure loss for multiphase flow in inclined annuli during Underbalanced Drilling operations
Natural Gas Industry B, 2017
[27] Analysis of Hole Cleaning for a Vertical Well
2017
[28] Application of Artificial Intelligence Techniques in Drilling System Design and Operations: A State of the Art Review and Future Research Pathways
2016
[29] Cuttings Transport Modeling-Part 1: Specification of Benchmark Parameters with a Norwegian Continental Shelf Perspective
2016
[30] Cuttings transport modeling in underbalanced oil drilling operation using radial basis neural network
Egyptian Journal of Petroleum, 2016
[31] Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art
Journal of Artificial Intelligence and Soft Computing Research, 2015
[32] Application of SVM Algorithm for Frictional Pressure Loss Calculation of Three Phase Flow in Inclined Annuli
Journal of Petroleum & Environmental Biotechnology, 2014
[33] Using Machine Learning to Improve Drilling of Unconventional Resources
[34] Cuttings Transport Modeling in Wellbore a Annulus in Oil Drilling using Evolutionary Fuzzy System
R Rookia, M Kazemib, E Hadavandib
Free SCIRP Newsletters
Copyright © 2006-2024 Scientific Research Publishing Inc. All Rights Reserved.
Top