Filter cake properties of water-based drilling fluids under static and dynamic conditions using computed tomography scan
DOI: 10.1115/1.4023483
Removal of water-based filter cake and stimulation of the formation in one-step using an environmentally friendly chelating agent
A new experimental method to prevent paraffin–wax formation on the crude oil wells: A field case study in Libya
Evaluation of Rock Mechanical Properties Alteration During Matrix Stimulation With Chelating Agents
DOI: 10.1115/1.4032546
Flow Rate-Dependent Skin in Water Disposal Injection Well
DOI: 10.1115/1.4033400
Development of lithology-based static Young's modulus correlations from log data based on data clustering technique
Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box)
Mixing chelating agents with seawater for acid stimulation treatments in carbonate reservoirs
Single stage filter cake removal of barite weighted water based drilling fluid
Evaluating the Chemical Reaction of Chelating Agents with Xanthan Gum
Real-Time Prediction of Rheological Parameters of KCl Water-Based Drilling Fluid Using Artificial Neural Networks
Modeling of Filter Cake Composition in Maximum Reservoir Contact and Extended Reach Horizontal Wells in Sandstone Reservoirs
DOI: 10.1115/1.4035022
Fabrication of kaolin-based cement plug for CO2 storage wells
Using high- and low-salinity seawater injection to maintain the oil reservoir pressure without damage
Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network
Integrated petrophysical and reservoir characterization workflow to enhance permeability and water saturation prediction
Development of chelating agent-based polymeric gel system for hydraulic fracturing
DOI: 10.3390/en11071663
Impact of sand content on filter cake and invert emulsion drilling fluid properties in extended reach horizontal wells
Optimizing the rheological properties of water-based drilling fluid using clays and nanoparticles for drilling horizontal and multi-lateral wells
Adaptive and Real-Time Optimal Control of Stick-Slip and Bit Wear in Autonomous Rotary Steerable Drilling
DOI: 10.1115/1.4038131
Development of a new correlation to determine the static Young’s modulus
Evaluation of using HEDTA chelating agent to clean up long horizontal heterogeneous sandstone wells without divergent
Impact of Surfactant on the Retention of CO2 and Methane in Carbonate Reservoirs
Development of a New Correlation for Bubble Point Pressure in Oil Reservoirs Using Artificial Intelligent Technique
Investigating the Compatibility of Enzyme with Chelating Agents for Calcium Carbonate Filter Cake Removal
Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique
Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data
DOI: 10.1115/1.4039613
Development of New Permeability Formulation from Well Log Data Using Artificial Intelligence Approaches
DOI: 10.1115/1.4039270
Effect of CO2 adsorption on enhanced natural gas recovery and sequestration in carbonate reservoirs
Enhancing the stability of invert emulsion drilling fluid for drilling in high-pressure high-temperature conditions
DOI: 10.3390/en11092393
Guidelines to define the critical injection flow rate to avoid formation damage during slurry injection into high permeability sandstone
Evaluation of the Reaction Kinetics of Diethylenetriaminepentaacetic Acid Chelating Agent and a Converter with Barium Sulfate (Barite) Using a Rotating Disk Apparatus
Development of New Mathematical Model for Compressional and Shear Sonic Times from Wireline Log Data Using Artificial Intelligence Neural Networks (White Box)
New Approach to Optimize the Rate of Penetration Using Artificial Neural Network
New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network
Reaction of Chelating Agents with Guar Gum Polymer for Completion Fluid
A self-adaptive artificial intelligence technique to predict oil pressure volume temperature properties
DOI: 10.3390/en11123490
New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs
Novel Technique to Eliminate Gas Condensation in Gas Condensate Reservoirs Using Thermochemical Fluids
A hybrid artificial intelligence model to predict the elastic behavior of sandstone rocks
DOI: 10.3390/su11195283
A new look into the prediction of static young’s modulus and unconfined compressive strength of carbonate using artificial intelligence tools
Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network
DOI: 10.3390/en12101880
Development of a new rate of penetration model using self-adaptive differential evolution-artificial neural network
Effect of arenite, calcareous, argillaceous, and ferruginous sandstone cuttings on filter cake and drilling fluid properties in horizontal wells
DOI: 10.1155/2019/1956715
Evaluating the Effect of Using Micronized Barite on the Properties of Water-Based Drilling Fluids
Influence of nanoclay content on cement matrix for oilwells subjected to cyclic steam injection
DOI: 10.3390/ma12091452
Mitigation of condensate banking using thermochemical treatment: Experimental and analytical study
DOI: 10.3390/en12050800
Clay minerals damage quantification in sandstone rocks using core flooding and NMR
Real-time determination of rheological properties of spud drilling fluids using a hybrid artificial intelligence technique
DOI: 10.1115/1.4042233
Removal of barite-scale and barite-weighted water- Or oil-based-drilling-fluid residue in a single stage
DOI: 10.2118/187122-PA
Well-placement optimization in heavy oil reservoirs using a novel method of in situ steam generation
DOI: 10.1115/1.4041613
A Robust Rate of Penetration Model for Carbonate Formation
DOI: 10.1115/1.4041840
Development of a homogenous cement slurry using synthetic modified phyllosilicate while cementing HPHT wells
DOI: 10.3390/su11071923
Thermochemical Upgrading of Calcium Bentonite for Drilling Fluid Applications
DOI: 10.1115/1.4041843
Formation damage avoidance by reducing invasion with sodium silicate-modified water-based drilling fluid
DOI: 10.3390/en12081485
A combined barite-ilmeniteweighting material to prevent barite sag in water-based drilling fluid
DOI: 10.3390/ma12121945
A Self-Adaptive Artificial Neural Network Technique to Predict Total Organic Carbon (TOC) Based on Well Logs
Enhancing the Rheological Properties of Water-Based Drilling Fluid Using Micronized Starch
Gas condensate treatment: A critical review of materials, methods, field applications, and new solutions
New approach to evaluate the equivalent circulating density (ECD) using artificial intelligence techniques
New Model for Pore Pressure Prediction While Drilling Using Artificial Neural Networks
Estimation of static Young’s modulus for sandstone formation using artificial neural networks
DOI: 10.3390/en12112125
Mitigation of barite sagging during the drilling of high-pressure high-temperature wells using an invert emulsion drilling fluid
An integrated approach for estimating static Young’s modulus using artificial intelligence tools
Mitigating CO2 reaction with hydrated oil well cement under geologic carbon sequestration using nanoclay particles
Comparative analysis of artificial intelligence techniques for formation pressure prediction while drilling
Real-time prediction of the rheological properties of water-based drill-in fluid using artificial neural networks
DOI: 10.3390/su11185008
Reaction kinetics and coreflooding study of high-temperature carbonate reservoir stimulation using GLDA in seawater
DOI: 10.3390/en12183407
Estimation of oil recovery factor for water drive sandy reservoirs through applications of artificial intelligence
DOI: 10.3390/en12193671
A hybrid artificial intelligence model to predict the elastic behavior of sandstone rocks
DOI: 10.3390/su11195286
Application of artificial neural network to predict formation bulk density while drilling
Evaluation of the total organic carbon (TOC) using different artificial intelligence techniques
DOI: 10.3390/su11205643
One-stage calcium carbonate oil-based filter cake removal using a new biodegradable acid system
DOI: 10.3390/su11205715
Prevention of Barite Sag in oil-based drilling fluids using a mixture of barite and ilmenite as weighting material
DOI: 10.3390/su11205617
Application of artificial intelligence techniques to predict the well productivity of fishbone wells
DOI: 10.3390/su11216083
New artificial neural networks model for predicting rate of penetration in deep shale formation
DOI: 10.3390/su11226527
WITHDRAWN: New approach to obtain the rheological properties of drill-in fluid on a real-time using artificial intelligence
Assessment of using copper nitrate for scavenging hydrogen sulfide while drilling sour horizontal wells
DOI: 10.1115/1.4043879
Cutting concentration prediction in horizontal and deviated wells using artificial intelligence techniques
Effect of pH on rheological and filtration properties of water-based drilling fluid based on bentonite
DOI: 10.3390/su11236714
Impact of methane adsorption on tight rock permeability measurements using pulse-decay
Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques
DOI: 10.3390/su11247020
New environmentally friendly acid system for iron sulfide scale removal
DOI: 10.3390/su11236727
New Robust Model to Estimate Formation Tops in Real Time Using Artificial Neural Networks (ANN)
The effect of weighting materials on oil-well cement properties while drilling deep wells
DOI: 10.3390/su11236776
Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors
DOI: 10.1155/2020/8851065
Evaluating the effect of using micronised barite on the properties of water-based drilling fluids
New computational artificial intelligence models for generating synthetic formation bulk density logs while drilling
DOI: 10.3390/su12020686
A novel solution for severe loss prevention while drilling deep wells
DOI: 10.3390/su12041339
Deep Illustration for Loss of Circulation While Drilling
Prediction of the rate of penetration while drilling horizontal carbonate reservoirs using the self-adaptive artificial neural networks technique
DOI: 10.3390/su12041376
Removal of calcium carbonate water-based filter cake using a green biodegradable acid
DOI: 10.3390/su12030994
A novel low-temperature non-corrosive sulfate/sulfide scale dissolver
DOI: 10.3390/su12062455
Application of machine learning in evaluation of the static young’s modulus for sandstone formations
DOI: 10.3390/su12051880
Artificial neural network models for real-time prediction of the rheological properties of NaCl mud
Enhancing the cement quality using polypropylene fiber
New hybrid hole cleaning model for vertical and deviated wells
DOI: 10.1115/1.4045169
Real-time prediction of rheological properties of invert emulsion mud using adaptive neuro-fuzzy inference system
DOI: 10.3390/s20061669
A new model for predicting rate of penetration using an artificial neural network
DOI: 10.3390/s20072058
Improving class G cement carbonation resistance for applications of geologic carbon sequestration using synthetic polypropylene fiber
Novel cake washer for removing oil-based calcium carbonate filter cake in horizontal wells
DOI: 10.3390/SU12083427
Prevention of barite sag in water-based drilling fluids by a urea-based additive for drilling deep formations
DOI: 10.3390/su12072719
Real-time determination of rheological properties of high over-balanced drilling fluid used for drilling ultra-deep gas wells using artificial neural network
Newly developed correlations to predict the rheological parameters of high-bentonite drilling fluid using neural networks
DOI: 10.3390/s20102787
Estimation of reservoir porosity from drilling parameters using artificial neural networks
Exposure time impact on the geomechanical characteristics of sandstone formation during horizontal drilling
Prevention of hematite settling using synthetic layered silicate while drilling high-pressure wells
Real-time prediction of rate of penetration in s-shape well profile using artificial intelligence models
DOI: 10.3390/s20123506
Application of artificial neural network to predict the rate of penetration for S-shape well profile
Barite–Micromax mixture, an enhanced weighting agent for the elimination of barite sag in invert emulsion drilling fluids
Correction to: Barite–Micromax mixture, an enhanced weighting agent for the elimination of barite sag in invert emulsion drilling fluids (Journal of Petroleum Exploration and Production Technology, (2020), 10, 6, (2427-2435), 10.1007/s13202-020-00892-7)
A review of different approaches for water-based drilling fluid filter cake removal
Effect of Formation Cutting’s Mechanical Properties on Drilling Fluid Properties During Drilling Operations
Prediction of Sonic Wave Transit Times From Drilling Parameters While Horizontal Drilling in Carbonate Rocks Using Neural Networks
Improving Saudi class G oil-well cement properties using the tire waste material
Influence of weighting materials on the properties of oil-well cement
Coupling rate of penetration and mechanical specific energy to Improve the efficiency of drilling gas wells
Improved durability of Saudi Class G oil-well cement sheath in CO2 rich environments using olive waste
A highlight on the application of industrial and agro wastes in cement-based materials
Effect of exposure time on the compressive strength and formation damage of sandstone while drilling horizontal wells
New Lightweight Cement Formulation for Shallow Oil and Gas Wells
Effect of the Filtrate Fluid of Water-Based Mud on Sandstone Rock Strength and Elastic Moduli
Enhancing Hematite-Based Invert Emulsion Mud Stability at High-Pressure High-Temperature Wells
Impact of Perlite on the Properties and Stability of Water-Based Mud in Elevated-Temperature Applications
The use of the granite waste material as an alternative for silica flour in oil-well cementing
Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs
DOI: 10.1155/2021/7390055
Applications of Artificial Intelligence for Static Poisson's Ratio Prediction while Drilling
DOI: 10.1155/2021/9956128
Applications of Biodiesel in Drilling Fluids
DOI: 10.1155/2021/5565897
Artificial neural network model for real-time prediction of the rate of penetration while horizontally drilling natural gas-bearing sandstone formations
Barium sulfate scale removal at low-temperature
DOI: 10.1155/2021/5527818
Effect of Bentonite Prehydration Time on the Stability of Lightweight Oil-Well Cement System
DOI: 10.1155/2021/9957159
Improved carbonation resistance and durability of Saudi Class G oil well cement sheath in CO2 rich environments using laponite
Intelligent Prediction for Rock Porosity while Drilling Complex Lithology in Real Time
DOI: 10.1155/2021/9960478
Retraction:Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application
DOI: 10.1155/2021/8865827
Retraction:Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray
DOI: 10.1155/2021/2486046
Real-Time Prediction of Equivalent Circulation Density for Horizontal Wells Using Intelligent Machines
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
Effect of Perlite Particles on Barite Cement Properties
The impact of weighting materials on carbonate pore system and rock characteristics
DOI: 10.1002/cjce.24092
Fracture Pressure Prediction Using Surface Drilling Parameters by Artificial Intelligence Techniques
DOI: 10.1115/1.4049125
Overview of the lightweight oil-well cement mechanical properties for shallow wells
Real-time prediction of rate of penetration while drilling complex lithologies using artificial intelligence techniques
Real-time static Poisson’s ratio prediction of vertical complex lithology from drilling parameters using artificial intelligence models
Enhancement of Static and Dynamic Sag Performance of Water-Based Mud Using a Synthetic Clay
Effect of perlite particles on the properties of oil-well class G cement
New correlations for better monitoring the all-oil mud rheology by employing artificial neural networks
Use of Machine Learning and Data Analytics to Detect Downhole Abnormalities while Drilling Horizontal Wells, with Real Case Study
DOI: 10.1115/1.4048070
The prediction of wellhead pressure for multiphase flow of vertical wells using artificial neural networks
Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters
Real-time prediction of acoustic velocities while drilling vertical complex lithology using ai technique
Effect of Different Weighting Agents on Drilling Fluids and Filter Cake Properties in Sandstone Formations
Improved Tracking of the Rheological Properties of Max-Bridge Oil-Based Mud Using Artificial Neural Networks
An Overview of the Common Water-Based Formulations Used for Drilling Onshore Gas Wells in the Middle East
Influence of mud filtrate on the pore system of different sandstone rocks
Stability Enhancing of Water-Based Drilling Fluid at High Pressure High Temperature
Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools
Application of machine learning models for real-time prediction of the formation lithology and tops from the drilling parameters
Insights into the application of surfactants and nanomaterials as shale inhibitors for water-based drilling fluid: A review
Applications of Artificial Intelligence to Predict Oil Rate for High Gas-Oil Ratio and Water-Cut Wells
Estimation of the rate of penetration while horizontally drilling carbonate formation using random forest
DOI: 10.1115/1.4050778
Investigation of dehydroxylated sodium bentonite as a pozzolanic extender in oil-well cement
DOI: 10.2118/205487-PA
Investigation of magnetite-based invert emulsion mud at high pressure high temperature
Novel empirical correlation for estimation of the total organic carbon in devonian shale from the spectral gamma-ray and based on the artificial neural networks
DOI: 10.1115/1.4050777
Rock strength prediction in real-time while drilling employing random forest and functional network techniques
DOI: 10.1115/1.4050843
Prevention of Hematite Settling in Water-Based Mud at High Pressure and High Temperature
The Role of Drilled Formation in Filter Cake Properties Utilizing Different Weighting Materials
Evaluation of calcined Saudi calcium bentonite as cement replacement in low-density oil-well cement system
Workflow to build a continuous static elastic moduli profile from the drilling data using artificial intelligence techniques
Machine learning models for equivalent circulating density prediction from drilling data
Recent Advances in Magnesia Blended Cement Studies for Geotechnical Well Construction—A Review
A review on clay chemistry, characterization and shale inhibitors for water-based drilling fluids
Artificial intelligence models for real-time synthetic gamma-ray log generation using surface drilling data in Middle East Oil Field
Machine learning application to predict in-situ stresses from logging data
Machine learning models for generating the drilled porosity log for composite formations
Prediction of oil rates using Machine Learning for high gas oil ratio and water cut reservoirs
Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
A Novel Artificial Neural Network-Based Correlation for Evaluating the Rate of Penetration in a Natural Gas Bearing Sandstone Formation: A Case Study in a Middle East Oil Field
DOI: 10.1155/2022/9444076
Application of Machine Learning to Predict Estimated Ultimate Recovery for Multistage Hydraulically Fractured Wells in Niobrara Shale Formation
DOI: 10.1155/2022/7084514
Application of Machine Learning to Predict the Failure Parameters from Conventional Well Logs
Artificial neural networks-based correlation for evaluating the rate of penetration in a vertical carbonate formation for an entire oil field
Geopolymer as the future oil-well cement: A review
Ilmenite Inclusion: A Solution towards Solid Sagging for Hematite-Based Invert Emulsion Mud
DOI: 10.1155/2022/7438163
Investigation of Using Various Quantities of Steelmaking Waste for Scavenging Hydrogen Sulfide in Drilling Fluids
DOI: 10.1155/2022/9098101
Machine Learning Applications to Predict Surface Oil Rates for High Gas Oil Ratio Reservoirs
DOI: 10.1115/1.4052485
Prediction of cohesion and friction angle from well-logging data using decision tree and random forest
Rate of penetration prediction while drilling vertical complex lithology using an ensemble learning model
Real-time prediction of in-situ stresses while drilling using surface drilling parameters from gas reservoir
Removal of Hematite Water-Based Filter Cake Using Hydrochloric Acid
DOI: 10.1155/2022/4924465
Utilization of adaptive neuro-fuzzy interference system and functional network in prediction of total organic carbon content
Utilization of Vermiculite for Solving Hematite Sagging in Water-Based Drilling Fluids
DOI: 10.1155/2022/5277126
Prediction of Water Saturation in Tight Gas Sandstone Formation Using Artificial Intelligence
A review of the various treatments of oil-based drilling fluids filter cakes
Drilling Data-Based Approach to Build a Continuous Static Elastic Moduli Profile Utilizing Artificial Intelligence Techniques
DOI: 10.1115/1.4050960
The Utilization of Steelmaking Industrial Waste of Silicomanganese Fume as Filtration Loss Control in Drilling Fluid Application
DOI: 10.1115/1.4051197
The Use of Graphite to Improve the Stability of Saudi Class G Oil-Well Cement against the Carbonation Process
Corrigendum to “Prediction of oil rates using Machine Learning for high gas-oil ratio and water cut reservoirs” [Flow Meas. Instrum. 82 (2021) 102065] (Flow Measurement and Instrumentation (2021) 82, (S0955598621001680), (10.1016/j.flowmeasinst.2021.102065))
Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques
DOI: 10.1115/1.4051298
Prevention of hematite settling using perlite in water-based drilling fluid
Vermiculite for enhancement of barite stability in water-based mud at elevated temperature
Effect of Elevated Temperature on the Microstructure of Metakaolin-Based Geopolymer
Bulk density prediction while drilling vertical complex lithology using artificial intelligence
Detection of Loss Zones While Drilling Using Different Machine Learning Techniques
DOI: 10.1115/1.4051553
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
New Empirical Correlations to Estimate the Least Principal Stresses Using Conventional Logging Data
Evaluation of Qusaiba Kaolinitic Shale as a Supplementary Cementitious Material in Lightweight Oil-Well Cement Formulation
Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud
Empirical correlation for formation resistivity prediction using machine learning
New generalized correlations for oil rate predictions through wellhead chokes for high GOR reservoirs
Application of Machine Learning Methods in Modeling the Loss of Circulation Rate while Drilling Operation
Application of Various Machine Learning Techniques in Predicting Water Saturation in Tight Gas Sandstone Formation
DOI: 10.1115/1.4053248
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
Sagging Prevention for Hematite-Based Invert Emulsion Mud
DOI: 10.1115/1.4052181
Improvement of Hydrogen Sulfide Scavenging via the Addition of Monoethanolamine to Water-Based Drilling Fluids
Artificial Intelligence Models for Real-Time Bulk Density Prediction of Vertical Complex Lithology Using the Drilling Parameters
Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data Quality
DOI: 10.1115/1.4053439
Formation Resistivity Prediction Using Decision Tree and Random Forest
Incorporating steel-industry waste in water based drilling fluids for hydrogen sulfide scavenging
Prediction Model Based on an Artificial Neural Network for Rock Porosity
Real-Time GR logs Estimation While Drilling Using Surface Drilling Data; AI Application
Real-Time Prediction of the Dynamic Young’s Modulus from the Drilling Parameters Using the Artificial Neural Networks
Using Manganese Tetroxide for Hematite Settling Prevention in Water-Based Mud
Curing Time Impacts on the Mechanical and Petrophysical Properties of a Laponite-Based Oil Well Cement
Effect of micro-magnesium oxide admixture on rheological and compressive strength properties of class G well cement
Data-Driven Approach for Resistivity Prediction Using Artificial Intelligence
DOI: 10.1115/1.4053954
Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations
DOI: 10.1115/1.4053846
Real-time prediction of tensile and uniaxial compressive strength from artificial intelligence-based correlations
Role of Rock Saturation Condition on Rock-Mud Interaction: Sandstone Geomechanics Study
Improving filter cake sealing properties for high-density ilmenite drilling fluid
Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms
A hybrid data-driven solution to facilitate safe mud window prediction
Improving the filtration properties for manganese tetroxide mud utilizing perlite particles to drill wide-range permeability sandstone formation
Real-time prediction of formation pressure gradient while drilling
The role of overbalance pressure on mud induced alteration of sandstone rock pore system
Durability of lightweight oil-well geopolymer system in sulfate environment
Evaluating the Effect of Claytone-EM on the Performance of Oil-Based Drilling Fluids
Evaluation of hematite and Micromax-based cement systems for high- density well cementing
Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters
The Combined Effect of Nanoclay Powder and Curing Time on the Properties of Class G Cement
DOI: 10.1155/2023/7316335
The impact of overbalance pressure on the alteration of sandstone geomechanical properties
Application of Silicomanganese Fume as a Novel Bridging Material for Water-Based Drilling Fluids
Primary Investigation of Barite-Weighted Water-Based Drilling Fluid Properties
Analysis of the Impact of Vermiculite on Hematite-Based Cement Systems
Primary Investigation of Ilmenite Dosage Effect on the Water-Based Drilling Fluid Properties
DOI: 10.1115/1.4054886
Effect of Hematite Dosage on Water-Based Drilling Fluid and Filter Cake Properties
DOI: 10.1115/1.4055209
Effect of Graphite on the Mechanical and Petrophysical Properties of Class G Oil Well Cement
Performance of Perlite as viscosifier in manganese tetroxide water based-drilling fluid
Data-driven EUR for multistage hydraulically fractured wells in shale formation using different machine learning methods
Review of underbalanced drilling techniques highlighting the advancement of foamed drilling fluids
The effect of polypropylene fiber on the curing time of class G oil well cement and its mechanical, petrophysical, and elastic properties
Development of Heavy-Weight Hematite-Based Geopolymers for Oil and Gas Well Cementing
Data-Driven Framework for Real-time Rheological Properties Prediction of Flat Rheology Synthetic Oil-Based Drilling Fluids
Effect of Qusaiba shale formation on high-pressure high-temperature drilling fluids properties
Synergy of retarders and superplasticizers for thickening time enhancement of hematite based fly ash geopolymers
Estimating electrical resistivity from logging data for oil wells using machine learning
Deep characterization of heavy-weighted oil well cement segregation by a novel nuclear magnetic resonance (NMR) technique
Evaluation of Granite Waste Powder as an Oil-Well Cement Extender
Evaluation of the wellbore drillability while horizontally drilling sandstone formations using combined regression analysis and machine learning models
Machine learning framework for estimating CO2 adsorption on coalbed for carbon capture, utilization, and storage applications
Resistivity Log Prediction in Horizontal Low Formation Quality Well Using Data-Driven Robust Models
Rheology Predictive Model Based on an Artificial Neural Network for Micromax Oil-Based Mud
Application of Organoclays in Oil-Based Drilling Fluids: A Review
The Effect of Olive Waste on the Rheological Properties, Thickening Time, Permeability, and Strength of Oil Well Cement
New application for Micromax in aqueous drilling fluids as a hydrogen sulfide scavenger
Photoelectric factor prediction using automated learning and uncertainty quantification
Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section
Evaluation of Using Fly Ash as a Weighing Material for Oil-Based Drilling Fluid
Exploring the potential of laser technology in oil well drilling: An overview
Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling
A New Method for Drill Cuttings Size Estimation Based on Machine Learning Technique
Data-driven models to predict shale wettability for CO2 sequestration applications
Detecting downhole vibrations through drilling horizontal sections: machine learning study
Estimation of rocks’ failure parameters from drilling data by using artificial neural network
Micronized calcium carbonate to enhance water-based drilling fluid properties
Mixed Micromax and hematite-based fly ash geopolymer for heavy-weight well cementing
Performance of the Hole Cleaning Factor in Predicting the Hole Cleaning Conditions in Vertical and Deviated Wells: Real Case Studies
Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods
Utilization of vermiculite particles in the enhancement of the ilmenite based oil well cement properties
Experimental Investigation of Using Manganese Monoxide as a Hydrogen Sulfide Scavenger for Aqueous Drilling Fluids
Estimation of tensile and uniaxial compressive strength of carbonate rocks from well-logging data: artificial intelligence approach
Experimental study on an eco-friendly gemini foaming agent for enhancing foam drilling applications
Minimizing the particles settling of ilmenite weighted oil well cement using laponite
Synthetic Clay Application to Reduce the Segregation of Barite-Based Oil-Well Cement
Impact of Pressure and Temperature on Foam Behavior for Enhanced Underbalanced Drilling Operations
Impact of Eco-Friendly Drilling Additives on Foaming Properties for Sustainable Underbalanced Foam Drilling Applications
Properties and Performance of Oil Well Slurry and Cement Sheath Incorporating Nano Silica: A Review
Foamed Cement Applications in Oil Industry Based on Field Experience: A Comprehensive Review
A New Empirical Correlation for Pore Pressure Prediction Based on Artificial Neural Networks Applied to a Real Case Study
DOI: 10.3390/pr12040664
Foam Stability Analysis at High pH and Saline Environments for Underbalanced Drilling Operations
Investigation of the Impact of Vermiculite on the Properties of Barite-Based Oil Well Cement
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))
A Volcanic Tephra-Based Non-Portland Cement System for Well Cementing Sustainability
DOI: 10.2118/221494-PA
Foam Properties Evaluation under Harsh Conditions: Implications for Enhanced Eco-Friendly Underbalanced Drilling Practices
DOI: 10.2118/223112-PA
Enhancing the properties of high-density oil well cement with Qusaiba kaolinite
Evaluation of using micronized saudi calcite in ilmenite-weighted water-based drilling fluid
Investigating the effect of perlite particles on ilmenite-based oil well cement
Investigating the efficacy of novel organoclay as a rheological additive for enhancing the performance of oil-based drilling fluids
Optimizing hematite filter cake treatment using reducing agents
Perlite incorporation for sedimentation reduction and improved properties of high-density geopolymer cement for oil well cementing
Retraction to: Micronized calcium carbonate to enhance water-based drilling fluid properties (Scientific Reports, (2023), 13, 1, (18295), 10.1038/s41598-023-45776-y)
Predicting the Rate of Penetration while Horizontal Drilling through Unconventional Reservoirs Using Artificial Intelligence
Evaluation of Claytone-ER as a novel rheological additive for enhancing oil-based drilling fluid performance under high-pressure high-temperature conditions
DOI: 10.1017/cmn.2024.39
Hematite Filter Cake Removal Using Oxalic Acid: Experimental Investigation
Predicting Equivalent Circulating Density While Primary Cementing Job by Employ-ing Machine Learning Techniques
Predicting Water Saturation in a Greek Oilfield with the Power of Artificial Neural Networks
Optimization of Drilling Parameters While Drilling Surface Holes Using Machine Learning and Differential Evolution
DOI: 10.2118/223965-PA
Revolutionizing High-Pressure Well Cementing: Enhancing Geopolymer Cement with Laponite for Sustainable and Sedimentation-Free Applications
DOI: 10.2118/224415-PA
Evaluation of the Effects of Kaolin Clay on the Performance of Barite-Weighted Oil-Based Drilling Fluid
Optimized Gradient Boosting Models for Adaptive Prediction of Uniaxial Compressive Strength in Carbonate Rocks Using Drilling Data
A review of the critical conditions required for effective hole cleaning while horizontal drilling
A study of a new polymeric retarder for enhanced rheology and its effects on properties of oil well cement
Prediction of Foam Half-Life Time Using Machine Learning Algorithms for Enhanced Oil Recovery and CO2 Sequestration
Enhancing Filter Cake Sealing in Ilmenite-Based Drilling Fluids Using Perlite: Performance Across Varied Densities and Filtration Media
Prediction of Formation Permeability While Drilling: Machine Learning Applications
Evaluating Diethylamine and Triethylamine as Alternative Amine-Based H2S Scavengers for Safe and Efficient Drilling in Sour Conditions
A novel approach to modeling breakdown pressure dynamics using machine learning
Damage Tendency of HCl-Based Solutions During Hematite-Based Filter-Cake Removal in Carbonate Formations
DOI: 10.2118/228408-PA
Advances in drilling fluid technology: Recent innovations, performance enhancements, and future trends in high-performance and eco-friendly formulations
From Hematite to Magnetite: A Comparative Study on Weighting Materials in Well Cementing
DOI: 10.2118/230303-PA
Development of an ANN-Driven Empirical Equation for Real-Time Prediction of Natural Gas Flow through Surface Well Chokes
Application of Qusaiba Kaolinite Clays as Secondary Cementitious Material in Oil Well Cementing
Assessing the influence of olive waste on the filtration properties, elasticity, porosity, and strength of oil well cement
Efficient and formation-friendly removal of hematite filter cake using DTPA-based chelating solution
Enhanced performance of oil-based drilling fluids under HPHT conditions using an organophilic phyllosilicate
Novel Approach to Enhancing Oil-Based Drilling Fluids Properties Using Combined Organoclays
Retraction note: Saudi calcium bentonite: a novel modifier for enhanced foamed underbalanced drilling performance (Scientific Reports, (2025), 15, 1, (4850), 10.1038/s41598-025-87019-2)
Saudi calcium bentonite: a novel modifier for enhanced foamed underbalanced drilling performance
Utilizing Saudi volcanic scoria in lightweight geopolymer for enhanced wellbore cementing
A Two-Organoclay Formulation Approach for Enhanced Performance of Oil-Based Drilling Fluids
Foam systems for underbalanced and geothermal drilling: a critical review of stability challenges and research frontiers
A Mineralogical Synergy Approach to Optimizing Oil-Based Drilling Fluids under High-Pressure and High-Temperature Conditions
Perlite-modified micromax-based drilling fluids: improved filtration control and rheological performance
Modeling Gas–Brine Surface Tension Using Data-Driven Techniques for Underground Hydrogen Storage: A Focus on Depleted Gas Reservoirs
Foam Stability Evaluation of a Biodegradable Surfactant with Green Polymeric Stabilizers for Underbalanced Drilling Fluids
Structure–function analysis of an illite–montmorillonite organoclay blend for HPHT oil-based drilling fluids
Investigating Primary and Secondary Formation Damage in Carbonate Reservoirs through Filter Cake Build-Up and Removal with HCl–Oxalic Acid Solutions
Formation damage assessment in carbonate reservoirs after removing hematite-water-based filter cake using HCl