A review on clay chemistry, characterization and shale inhibitors for water-based drilling fluids NS Muhammed, T Olayiwola, S Elkatatny Journal of Petroleum Science and Engineering 206, 109043, 2021 | 83 | 2021 |
Insights into the application of surfactants and nanomaterials as shale inhibitors for water-based drilling fluid: A review NS Muhammed, T Olayiwola, S Elkatatny, B Haq, S Patil Journal of Natural Gas Science and Engineering 92, 103987, 2021 | 59 | 2021 |
Application of Artificial Intelligence-based predictive methods in Ionic liquid studies: A review F Yusuf, T Olayiwola, C Afagwu Fluid Phase Equilibria 531, 112898, 2021 | 57 | 2021 |
Carbon dioxide sequestration in underground formations: review of experimental, modeling, and field studies S Kalam, T Olayiwola, MM Al-Rubaii, BI Amaechi, MS Jamal, ... Journal of Petroleum Exploration and Production 11, 303-325, 2021 | 50 | 2021 |
Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach BA Salami, T Olayiwola, TA Oyehan, IA Raji Construction and Building Materials 301, 124152, 2021 | 48 | 2021 |
A data-driven approach to predict compressional and shear wave velocities in reservoir rocks T Olayiwola, OA Sanuade Petroleum 7 (2), 199-208, 2021 | 21 | 2021 |
A review of pressure transient analysis in reservoirs with natural fractures, vugs and/or caves I Mohammed, TO Olayiwola, M Alkathim, AA Awotunde, SF Alafnan Petroleum Science 18, 154-172, 2021 | 17 | 2021 |
Evolving strategies for shear wave velocity estimation: smart and ensemble modeling approach T Olayiwola, Z Tariq, A Abdulraheem, M Mahmoud Neural Computing and Applications 33 (24), 17147-17159, 2021 | 15 | 2021 |
Molecular simulation of kerogen-water interaction: Theoretical insights into maturity LO Lawal, T Olayiwola, S Abdel-Azeim, M Mahmoud, AO Onawole, ... Journal of Molecular Liquids 299, 112224, 2020 | 15 | 2020 |
Modeling the acentric factor of binary and ternary mixtures of ionic liquids using advanced intelligent systems T Olayiwola, O Ogolo, F Yusuf Fluid Phase Equilibria 516, 112587, 2020 | 12 | 2020 |
Determining ion activity coefficients in ion-exchange membranes with machine learning and molecular dynamics simulations HK Gallage Dona, T Olayiwola, LA Briceno-Mena, CG Arges, R Kumar, ... Industrial & Engineering Chemistry Research 62 (24), 9533-9548, 2023 | 4 | 2023 |
Application of artificial neural network to estimate permeability from nuclear magnetic resonance log T Olayiwola SPE Annual Technical Conference and Exhibition?, D023S099R012, 2017 | 4 | 2017 |
Feature Embedding of Molecular Dynamics-Based Descriptors for Modeling Electrochemical Separation Processes HKG Dona, T Olayiwola, LA Briceno-Mena, CG Arges, R Kumar, ... Computer Aided Chemical Engineering 52, 1451-1456, 2023 | 1 | 2023 |
A New Mathematical Workflow to Predict Permeability Variation using Flowing Gas Material Balance B Haq, DA Al Shehri, I Mohammed, T Olayiwola, NS Muhammed, Z Hasan Offshore Technology Conference Asia, D012S001R080, 2020 | 1 | 2020 |
Synergizing data-driven and knowledge-based hybrid models for ionic separations T Olayiwola, L Briceno-Mena, C Arges, J Romagnoli | | 2024 |
Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis M Nnadili, AN Okafor, T Olayiwola, D Akinpelu, R Kumar, JA Romagnoli Industrial & Engineering Chemistry Research 63 (14), 6313-6324, 2024 | | 2024 |
Empowering Capacitive Devices: Harnessing Transfer Learning for Enhanced Data-Driven Optimization T Olayiwola, R Kumar, J Romagnoli | | 2024 |
Integrating Physics and Machine Learning: A Hybrid Modeling Approach for Electrochemical Separation T Olayiwola, R Kumar, C Arges, J Romagnoli 2024 Spring Meeting & 20th Global Congress on Process Safety, 2024 | | 2024 |
Automated Synthesis of Hybrid Models for Ionic Separations T Olayiwola, LA Briceno-Mena, T Kulkarni, CG Arges, R Kumar, ... 2023 AIChE Annual Meeting, 2023 | | 2023 |
Determining ion activity coefficients in ion-exchange membranes with machine learning and molecular dynamics HKG Dona, T Olayiwola, LA Briceno-Mena, CG Arges, R Kumar, ... | | 2023 |