Publications
Preprint · SSRN Electronic Journal · January 1, 2023
An Insight into Machine Learning Algorithms’ Accuracy in Predicting Mud Loss Rate: Data Processing and Screening
Nurfarahrina Zaimee, Arina Sauki, Sonny Irawan
Drilling and Well EngineeringTunneling and Rock MechanicsHydrocarbon exploration and reservoir analysislost circulationdrillingdata cleaning
More by Sonny Irawan
- An explainable physics-informed neural network prototype for synthetic proppant transport in a two-dimensional fracture domainPhysics of Fluids · 2026
- Physics‐Guided Neural Regression Modeling of Energy‐Balance Proxies for Hydraulic Fracture Propagation in 1D–3DInternational Journal for Numerical and Analytical Methods in Geomechanics · 2026
- Modeling and Prediction of Kappaphycus alvarezii Viscosity Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference SystemJournal of Earth Energy Science Engineering and Technology · 2025 · Citations: 1
- Physics-Informed Machine Learning for Predicting Drilling Fluid Losses in Naturally Fractured Caspian FormationsSPE Annual Caspian Technical Conference and Exhibition · 2025 · Citations: 4