Table 1. Summary of Recent Applications of Machine Learning Models to Predict Petrophysical Parameters.

reference	tools	inputs	outputs	notes
Tian et al.22	hybrid GA-ANN method	pore structure parameters	permeability	generalizability, requirement for pore structure parameters
Ahmadi and Chen24	ANN, GA, ICA, and PSO	well logging data	porosity, permeability	required well logging data, not real time during drilling operations
Wood25	optimized data-matching algorithm, the transparent open box (TOB) learning network	well logging data	porosity, permeability, and water saturation	required well logging data, not real time during drilling operations
Sun et al.23	SVM, RF, and GBDT	logging while drilling and well logging data	porosity and permeability	required well logging data, not real time during drilling operations
Matinkia et al.30	multilayer perceptron network	well logging data	permeability	required well logging data, not real time during drilling operations
Kalule et al.31	deep neural networks (DNN) than gradient boosting	3D micro-CT images	porosity and permeability	laboratory scale, not real time during drilling operations
Tian et al.32	support vector machine, artificial neural network, decision tree, random forest, gradient-boosting machine, and Bayesian ridge regression	porosity, tortuosity, fractal dimension, average pore diameter, and coordination number	permeability	input data are based on synthetic data
Current Study	DT, RF, and SVM	readily available drilling parameters	porosity and permeability	readily available drilling parameter, realtime prediction