Abdul Azeez

Faculty of Psychology • Psychology

ORCID: 0000-0002-9994-4436

Ringkasan Keahlian

Abdul Azeez memiliki keahlian akademik yang berfokus pada penerapan kecerdasan buatan dan pembelajaran mesin untuk pemodelan dan prediksi dalam bidang energi, terutama terkait reservoir minyak dan gas serta penyimpanan energi bersih. Dalam lima tahun terakhir, risetnya menonjolkan pergeseran signifikan menuju aplikasi machine learning untuk solusi energi berkelanjutan, seperti pemodelan tegangan permukaan gas-air garam untuk penyimpanan hidrogen bawah tanah di reservoir gas yang terdeplesi, prediksi adsorpsi CO2 pada polimer organik berpori amorf, dan pengembangan model hibrida untuk analisis ketidakpastian dalam penyimpanan hidrogen. Publikasi terbaru juga mencakup prediksi laju penetrasi pengeboran menggunakan teknik AI canggih, identifikasi sweet spot reservoir karbonat melalui inversi seismik dan algoritma ML, serta estimasi cadangan berbasis inversi impedansi akustik. Metode yang dominan meliputi regresi multilayer perceptron, random forest, dan extra tree, sering dikombinasikan dengan teknik clustering seperti K-means untuk prediksi curah hujan dan deteksi anomali data pengeboran. Riset sebelumnya, seperti tinjauan sistematis aplikasi data science di industri migas dan prediksi sifat batuan secara real-time, memberikan landasan bagi fokus mutakhir pada integrasi ML dengan karakterisasi reservoir dan optimalisasi operasi pengeboran. Objek kajian meliputi reservoir karbonat, serpih, dan formasi kompleks, dengan arah riset yang semakin menekankan pada solusi berbasis data untuk tantangan lingkungan dan energi terbarukan.

Publikasi Terkini

A Synergistic Chelating Approach for Hematite Filter Cake Removal in Carbonate Reservoirs Using DTPA and Citric Acid

2026

Abdul Azeez

Ringkasan Keahlian

Publikasi Terkini

A Synergistic Chelating Approach for Hematite Filter Cake Removal in Carbonate Reservoirs Using DTPA and Citric Acid

Modeling Gas–Brine Surface Tension Using Data-Driven Techniques for Underground Hydrogen Storage: A Focus on Depleted Gas Reservoirs

Predicting Interfacial Tension for Underground Hydrogen Storage: A Hybrid Machine Learning Model with Conformal Uncertainty Analysis

Machine learning-driven acoustic impedance inversion with globally optimized reservoir characterization for reserve estimation in carbonate reservoirs

Real-Time Prediction of Basic Sediment and Water (BS&amp;W) Content from Surface Data Using Ensemble Machine Learning

Rational prediction of the performance of pH-responsive functionalized iron oxide grafted on graphene oxide for magnetic hyperthermia cancer therapy using machine learning

Chitosan-Based Flocculant Heavy Metal Removal Prediction Using Machine Learning Models

Machine Learning Approach for Modeling Surface Tension Between H2-CH4 Gas Mixture and Brine: Implication for Underground Hydrogen Storage in Partially Depleted Gas Reservoirs

Reserve Estimation Based on Post-Stack Seismic Inversion via Multilayer Perceptron, Random Forest, and Extra Tree Regression Algorithms

Machine learning for data-driven insights into CO2 adsorption on amorphous porous organic polymers

A novel approach to modeling breakdown pressure dynamics using machine learning

Enhanced Rate of Penetration Prediction for Complex Bottom Hole Assemblies Using Advanced Artificial Intelligence Techniques

An artificial intelligence approach for predicting water-filled porosity and water saturation for carbonate reservoirs using conventional well logs

Sweet Spot Identification of Carbonate Reservoirs Using Seismic Inversion and Three Machine Learning Algorithms

Rainfall Prediction Using Integrated Machine Learning Models With K-Means Clustering: A Representative Case Study of Harirud Murghab Basin-Afghanistan

Predicting the Rate of Penetration while Horizontal Drilling through Unconventional Reservoirs Using Artificial Intelligence

A New Model for Predicting the Hardness of Carbonate Mudrocks Through Elemental Compositions Employing Artificial Intelligence Techniques

Predicting soot formation in fossil fuels: A comparative study of regression and machine learning models

Deep learning with improved hybrid neuro-turning for predictive control of flux based on experimental DCMD module design of water desalination system

Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence

Drilling Data Based Approach for Real-Time Gamma Ray Prediction in Surface Holes Using Artificial Intelligence

Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

Acoustic impedance prediction based on extended seismic attributes using multilayer perceptron, random forest, and extra tree regressor algorithms

Drilling Data Based Approach for Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence

Automating Prediction of the Formation Tops and Lithology Changes in Real-Time While Drilling Using Artificial Neural Networks

Functional Neural Networks Model for Prediction of the Formation Tops in Real-Time While Drilling

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

Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies

Intelligent chemometric modelling of Al<sub>2</sub>O<sub>3</sub> supported mixed metal oxide catalysts for oxidative dehydrogenation of <i>n</i>-butane using simple features

Predicting the Rock Hardness of Unconventional Shales Based on Elemental Compositions by Employing Artificial Neuron Networks

Stability of Carbonate Rocks Containing Acid Wormholes Under High Confining Pressures

Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods

Photoelectric factor prediction using automated learning and uncertainty quantification

Microstructural and strength variations in natural sands exposed to diverse environmental conditions

Enhancing Fracture Conductivity in Soft Chalk Formations With Diammonium Phosphate Treatment: A Study at High Temperature, Pressure, and Stresses

Resistivity Log Prediction in Horizontal Low Formation Quality Well Using Data-Driven Robust Models

Estimating electrical resistivity from logging data for oil wells using machine learning

A Comparative Study Between Artificial Intelligence Models and a Statistical Learning Model to Predict Gas Adsorption in Shales

Machine learning accelerated approach to infer nuclear magnetic resonance porosity for a middle eastern carbonate reservoir

Intelligent Prediction of Downhole Drillstring Vibrations in Horizontal Wells by Employing Artificial Neural Network

Microstructural and Strength Variations of Sands Exposed to Freezing-Thawing, Wetting-Drying, and Heating-Cooling Cycles

Chemical Treatment for Sustainable Acid Fracture Conductivity of Weak Carbonates

Prediction of Yield Sooting Index Utilizing Artificial Neural Networks and Adaptive-Network-Based Fuzzy Inference Systems

Ionic liquids as clay stabilizer additive in fracturing fluid

Drilling Data-Based Approach for Pressure Gradient Estimation Using Random Forest

Shale brittleness prediction using machine learning—A Middle East basin case study

Prediction of Dew Point Pressure for High-Pressure Gas Reservoirs Using Artificial Intelligence Techniques

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 Applications in the Petroleum Industry

Real-Time Prediction of Basic Sediment and Water (BS&W) Content from Surface Data Using Ensemble Machine Learning