Constitutive model for characterizing dilatancy in rocks Rock Mechanics as a Multidisciplinary Science: Proc 32nd US Symposium, Norman, 10–12 July 1991 P531–538. Publ Rotterdam: A A Balkema, 1991
Numerical simulation of a compacting reservoir
A finite-element model for Ekofisk field subsidence
A two characteristic model for cohesionless soil and its calibration using optimization
Pore collapse in weakly cemented and porous rocks
DOI: 10.1115/1.2906024
Chapter 8 Reservoir compaction and surface subsidence in the North Sea Ekofisk field
Experimental investigation of the fractal dimension of the pore surface of sedimentary rocks under pressure
Fractal and genetic aspects of Khuff reservoir stylolites, Eastern Saudi Arabia
Controls of grain-size distribution on geomechanical properties of reservoir rock-A case study: Cretaceous Khafji Member, Zuluf Field, offshore Arabian Gulf
Analysis of wellbore instability in vertical, directional, and horizontal wells using field data
Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization
Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems
Support vector regression and functional networks for viscosity and GAS/oil ratio curves estimation
Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir
A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction
Hydraulic unit prediction using support vector machine
A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction
Prediction of bubble point pressure from composition of black oils using artificial neural network
A simple geometric model of sedimentary rock to connect transfer and acoustic properties
Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density
Recent advances in the application of computational intelligence techniques in oil and gas reservoir characterisation: A comparative study
Non-linear feature selection-based hybrid computational intelligence models for improved natural gas reservoir characterization
Artificial intelligence based estimation of water saturation in complex reservoir systems
Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines
Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
The relationship between lithological and geomechanical properties of tight carbonate rocks from Upper Jubaila and Arab-D Member outcrop analog, Central Saudi Arabia
A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization
DOI: 10.1155/2016/2580169
Long-Term Effects of CO2 Sequestration on Rock Mechanical Properties
DOI: 10.1115/1.4032011
Effect of Sand Content on the Filter Cake Properties and Removal during Drilling Maximum Reservoir Contact Wells in Sandstone Reservoir
DOI: 10.1115/1.4032121
Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models
Development of lithology-based static Young's modulus correlations from log data based on data clustering technique
A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir
Ensemble machine learning: An untapped modeling paradigm for petroleum reservoir characterization
Single stage filter cake removal of barite weighted water based drilling fluid
Evaluation of Barium Sulfate (Barite) Solubility Using Different Chelating Agents at a High Temperature
Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead
PVT correlations for Pakistani crude oils using artificial neural network
Prediction of non-hydrocarbon gas components in separator by using Hybrid Computational Intelligence models
Toward a Complete Removal of Barite (Barium Sulfate BaSO 4 ) Scale Using Chelating Agents and Catalysts
Investigating the effect of training–testing data stratification on the performance of soft computing techniques: an experimental study
Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network
Reservoir heterogeneity and quality of Khuff carbonates in outcrops of central Saudi Arabia
Development of a new correlation to determine the static Young’s modulus
Development of New Permeability Formulation from Well Log Data Using Artificial Intelligence Approaches
DOI: 10.1115/1.4039270
Development of New Mathematical Model for Compressional and Shear Sonic Times from Wireline Log Data Using Artificial Intelligence Neural Networks (White Box)
New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network
A rigorous data-driven approach to predict Poisson's ratio of carbonate rocks using a functional network
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
A new look into the prediction of static young’s modulus and unconfined compressive strength of carbonate using artificial intelligence tools
Assessment of unsteady Brinkman’s model for flow in karst aquifers
Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network
DOI: 10.3390/en12101880
A parametric study of machine learning techniques in petroleum reservoir permeability prediction by integrating seismic attributes and wireline data
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
An integrated approach for estimating static Young’s modulus using artificial intelligence tools
Total organic carbon characterization using neural-network analysis of XRF data
Comparative analysis of artificial intelligence techniques for formation pressure prediction while drilling
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 competitive ensemble model for permeability prediction in heterogeneous oil and gas reservoirs
Application of artificial neural network to predict formation bulk density while drilling
Comparative analysis of static and dynamic mechanical behavior for dry and saturated cement mortar
DOI: 10.3390/ma12203299
Evaluation of the total organic carbon (TOC) using different artificial intelligence techniques
DOI: 10.3390/su11205643
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
Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction
Cutting concentration prediction in horizontal and deviated wells using artificial intelligence techniques
Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques
DOI: 10.3390/su11247020
Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors
DOI: 10.1155/2020/8851065
New computational artificial intelligence models for generating synthetic formation bulk density logs while drilling
DOI: 10.3390/su12020686
Optimization of choke size for two-phase flow using artificial intelligence
Real-time prediction of rheological properties of invert emulsion mud using adaptive neuro-fuzzy inference system
DOI: 10.3390/s20061669
An environment friendly approach to reduce the breakdown pressure of high strength unconventional rocks by cyclic hydraulic fracturing
DOI: 10.1115/1.4045317
An experimental study to reduce the breakdown pressure of the unconventional carbonate rock by cyclic injection of thermochemical fluids
Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques
New empirical equation to estimate the soil moisture content based on thermal properties using machine learning techniques
Real-time determination of rheological properties of high over-balanced drilling fluid used for drilling ultra-deep gas wells using artificial neural network
Exposure time impact on the geomechanical characteristics of sandstone formation during horizontal drilling
An intelligent data-driven model for Dean–Stark water saturation prediction in carbonate rocks
Improvement of petrophysical properties of tight sandstone and limestone reservoirs using thermochemical fluids
Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks
Imidazolium-Based Ionic Liquids as Clay Swelling Inhibitors: Mechanism, Performance Evaluation, and Effect of Different Anions
Thermochemical acid fracturing of tight and unconventional rocks: Experimental and modeling investigations
Application of Artificial Intelligence to Estimate Oil Flow Rate in Gas-Lift Wells
Polyoxyethylene quaternary ammonium gemini surfactants as a completion fluid additive to mitigate formation damage
DOI: 10.2118/201207-PA
Polyoxyethylene Quaternary Ammonium Gemini Surfactants as a Completion Fluid Additive to Mitigate Formation Damage
Effect of the Filtrate Fluid of Water-Based Mud on Sandstone Rock Strength and Elastic Moduli
Intelligent Prediction for Rock Porosity while Drilling Complex Lithology in Real Time
DOI: 10.1155/2021/9960478
New approach to evaluate the performance of highly deviated water injection wells using artificial neural network
Real-Time Prediction of Equivalent Circulation Density for Horizontal Wells Using Intelligent Machines
Productivity Enhancement in Multilayered Unconventional Rocks Using Thermochemicals
DOI: 10.1115/1.4047976
Real-time static Poisson’s ratio prediction of vertical complex lithology from drilling parameters using artificial intelligence models
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
Simulation of wellbore erosion and sand transport in long horizontal wells producing gas at high velocities
The prediction of wellhead pressure for multiphase flow of vertical wells using artificial neural networks
Control of Lithofacies and Geomechanical Characteristics on Natural Fracture Systems in Qusaiba Shale, Rub’ Al-Khali Basin, Saudi Arabia
Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters
Dicationic Surfactants as an Additive in Fracturing Fluids to Mitigate Clay Swelling: A Petrophysical and Rock Mechanical Assessment
Machine Learning-Based Propped Fracture Conductivity Correlations of Several Shale Formations
A Data-Driven Machine Learning Approach to Predict the Natural Gas Density of Pure and Mixed Hydrocarbons
DOI: 10.1115/1.4051259
Rock strength prediction in real-time while drilling employing random forest and functional network techniques
DOI: 10.1115/1.4050843
A review on non-aqueous fracturing techniques in unconventional reservoirs
Artificial intelligence models for real-time synthetic gamma-ray log generation using surface drilling data in Middle East Oil Field
Machine learning-based improved pressure-volume-temperature correlations for black oil reservoirs
DOI: 10.1115/1.4050579
A systematic review of data science and machine learning applications to the oil and gas industry
Evolving strategies for shear wave velocity estimation: smart and ensemble modeling approach
Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
Generation of Synthetic Sonic Slowness Logs From Real-Time Drilling Sensors Using Artificial Neural Network
DOI: 10.1115/1.4052412
Lithological Parameters Controlling Rock Strength and Elastic Properties of Peritidal and Deep Marine Carbonate Mudrocks, Lower to Upper Jurassic Succession, Central Saudi Arabia
Drilling Data-Based Approach to Build a Continuous Static Elastic Moduli Profile Utilizing Artificial Intelligence Techniques
DOI: 10.1115/1.4050960
Data-driven machine learning approach to predict mineralogy of organic-rich shales: An example from Qusaiba Shale, Rub’ al Khali Basin, Saudi Arabia
Study of the Mechanical Behavior of Organic Matters Contained in Source Rocks: New Insights into the Role of Bitumen
Detection of Loss Zones While Drilling Using Different Machine Learning Techniques
DOI: 10.1115/1.4051553
Ionic Liquids as Clay Swelling Inhibitors: Adsorption Study
Impact of HCl Acidizing Treatment on Mechanical Integrity of Carbonaceous Shale
A Game-Theoretic Approach to Improve Energy-Related Data
Ionic liquids as completion fluids to mitigate formation damage
Hyperparameter Tuning of Artificial Neural Networks for Well Production Estimation Considering the Uncertainty in Initialized Parameters
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
Artificial Intelligence-Based Model of Mineralogical Brittleness Index Based on Rock Elemental Compositions
Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data Quality
DOI: 10.1115/1.4053439
Statistical Methods to Improve the Quality of Real-Time Drilling Data
DOI: 10.1115/1.4053519
Geomechanics of Organic Matters Contained in Shales: A Molecular-Level Investigation
Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches
Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations
DOI: 10.1115/1.4053846
Shale brittleness prediction using machine learning - A Middle East basin case study
DOI: 10.1306/12162120181
Predicting the performance of constant volume depletion tests for gas condensate reservoirs using artificial intelligence techniques
Real-time prediction of formation pressure gradient while drilling
Ionic liquids as clay stabilizer additive in fracturing fluid
Estimating electrical resistivity from logging data for oil wells using machine learning
Prediction of Yield Sooting Index Utilizing Artificial Neural Networks and Adaptive-Network-Based Fuzzy Inference Systems
Resistivity Log Prediction in Horizontal Low Formation Quality Well Using Data-Driven Robust Models
Stability of Carbonate Rocks Containing Acid Wormholes Under High Confining Pressures
Photoelectric factor prediction using automated learning and uncertainty quantification
Enhancing Fracture Conductivity in Soft Chalk Formations With Diammonium Phosphate Treatment: A Study at High Temperature, Pressure, and Stresses
DOI: 10.2118/215857-PA
Machine learning accelerated approach to infer nuclear magnetic resonance porosity for a middle eastern carbonate reservoir
Microstructural and strength variations in natural sands exposed to diverse environmental conditions
Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods
Acoustic impedance prediction based on extended seismic attributes using multilayer perceptron, random forest, and extra tree regressor algorithms
Stability of Carbonate Rocks Containing Acid Wormholes Under High Confining Pressures
Intelligent chemometric modelling of Al2O3 supported mixed metal oxide catalysts for oxidative dehydrogenation of n-butane using simple features
DOI: 10.1039/d4re00118d
A Comprehensive Overview of Depositional and Diagenetic Control on Pore System Within Turbiditic-Influenced intra-Qusaiba Fine-Grained Sandstone Outcrop, Tabuk Basin, Northern Saudi Arabia
Deep learning with improved hybrid neuro-turning for predictive control of flux based on experimental DCMD module design of water desalination system
Application of machine learning and deep learning in geothermal resource development: Trends and perspectives
DOI: 10.1002/dug2.12098
Predicting soot formation in fossil fuels: A comparative study of regression and machine learning models
Predicting the Rate of Penetration while Horizontal Drilling through Unconventional Reservoirs Using Artificial Intelligence
Rainfall Prediction Using Integrated Machine Learning Models With K-Means Clustering: A Representative Case Study of Harirud Murghab Basin-Afghanistan
A New Model for Predicting the Hardness of Carbonate Mudrocks Through Elemental Compositions Employing Artificial Intelligence Techniques
An artificial intelligence approach for predicting water-filled porosity and water saturation for carbonate reservoirs using conventional well logs
A novel approach to modeling breakdown pressure dynamics using machine learning
Machine learning for data-driven insights into CO2 adsorption on amorphous porous organic polymers
Chitosan-Based Flocculant Heavy Metal Removal Prediction Using Machine Learning Models
Modeling Gas–Brine Surface Tension Using Data-Driven Techniques for Underground Hydrogen Storage: A Focus on Depleted Gas Reservoirs
Machine learning-driven acoustic impedance inversion with globally optimized reservoir characterization for reserve estimation in carbonate reservoirs
Rational prediction of the performance of pH-responsive functionalized iron oxide grafted on graphene oxide for magnetic hyperthermia cancer therapy using machine learning