Effect of pH on rheological and filtration properties of water-based drilling fluid based on bentonite
DOI: 10.3390/su11236714
New environmentally friendly acid system for iron sulfide scale removal
DOI: 10.3390/su11236727
A novel solution for severe loss prevention while drilling deep wells
DOI: 10.3390/su12041339
A novel low-temperature non-corrosive sulfate/sulfide scale dissolver
DOI: 10.3390/su12062455
Real-time prediction of rheological properties of invert emulsion mud using adaptive neuro-fuzzy inference system
DOI: 10.3390/s20061669
Exposure time impact on the geomechanical characteristics of sandstone formation during horizontal drilling
Application of artificial neural network to predict the rate of penetration for S-shape well profile
Effect of exposure time on the compressive strength and formation damage of sandstone while drilling horizontal wells
Effect of the Filtrate Fluid of Water-Based Mud on Sandstone Rock Strength and Elastic Moduli
Barium sulfate scale removal at low-temperature
DOI: 10.1155/2021/5527818
Intelligent Prediction for Rock Porosity while Drilling Complex Lithology in Real Time
DOI: 10.1155/2021/9960478
Development of a Unique Organic Acid Solution for Removing Composite Field Scales
Investigating the Alteration of Sandstone Pore System and Rock Features by Role of Weighting Materials
The impact of weighting materials on carbonate pore system and rock characteristics
DOI: 10.1002/cjce.24092
Triple-shell NiO hollow sphere for p-type dye-sensitized solar cell with superior light harvesting
New correlations for better monitoring the all-oil mud rheology by employing artificial neural networks
Optimization of the process factors affecting extraction of uranium from acidic solution using activated carbon and kinetics studies of the adsorption process
Effect of Different Weighting Agents on Drilling Fluids and Filter Cake Properties in Sandstone Formations
Influence of mud filtrate on the pore system of different sandstone rocks
Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools
Rock strength prediction in real-time while drilling employing random forest and functional network techniques
DOI: 10.1115/1.4050843
The Role of Drilled Formation in Filter Cake Properties Utilizing Different Weighting Materials
Machine learning models for equivalent circulating density prediction from drilling data
Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
Rate of penetration prediction while drilling vertical complex lithology using an ensemble learning model
Utilization of adaptive neuro-fuzzy interference system and functional network in prediction of total organic carbon content
The Utilization of Steelmaking Industrial Waste of Silicomanganese Fume as Filtration Loss Control in Drilling Fluid Application
DOI: 10.1115/1.4051197
Bulk density prediction while drilling vertical complex lithology using artificial intelligence
Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach
DOI: 10.1115/1.4051737
Predicting the Rock Sonic Logs While Drilling by Random Forest and Decision Tree-Based Algorithms
DOI: 10.1115/1.4051670
Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud
Applying Different Artificial Intelligence Techniques in Dynamic Poisson’s Ratio Prediction Using Drilling Parameters
DOI: 10.1115/1.4052185
Intelligent Model for Predicting Downhole Vibrations Using Surface Drilling Data during Horizontal Drilling
DOI: 10.1115/1.4052794
Artificial Intelligence Models for Real-Time Bulk Density Prediction of Vertical Complex Lithology Using the Drilling Parameters
Prediction Model Based on an Artificial Neural Network for Rock Porosity
Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations
DOI: 10.1115/1.4053846
Role of Rock Saturation Condition on Rock-Mud Interaction: Sandstone Geomechanics Study
Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms
The role of overbalance pressure on mud induced alteration of sandstone rock pore system
The impact of overbalance pressure on the alteration of sandstone geomechanical properties
Evaluation of the wellbore drillability while horizontally drilling sandstone formations using combined regression analysis and machine learning models
Rheology Predictive Model Based on an Artificial Neural Network for Micromax Oil-Based Mud
Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section
Exploring the potential of laser technology in oil well drilling: An overview
Detecting downhole vibrations through drilling horizontal sections: machine learning study
Corrigendum to “Exploring the potential of laser technology in oil well drilling: An overview” [Geoenergy Sci. Eng. J., 230, November 2023, 212278] (Geoenergy Science and Engineering (2023) 230, (S2949891023008655), (10.1016/j.geoen.2023.212278))