Estimating Compressional Velocity and Bulk Density Logs in Marine Gas Hydrates Using Machine Learning

Compressional velocity (V p ) and bulk density (ρ b ) logs are essential for characterizing gas hydrates and near-seafloor sediments; however, it is sometimes difficult to acquire these logs due to poor borehole conditions, safety concerns, or cost-related issues. We present a machine learning approach to predict either compressional V p or ρ b logs with high accuracy and low error in near-seafloor sediments within water-saturated intervals, in intervals where hydrate fills fractures, and intervals where hydrate occupies the primary pore space. We use scientific-quality logging-while-drilling well logs, gamma ray, ρ b, V p , and resistivity to train the machine learning model to predict V p or ρ b logs. Of the six machine learning algorithms tested (multilinear regression, polynomial regression, polynomial regression with ridge regularization, K nearest neighbors, random forest, and multilayer perceptron), we find that the random forest and K nearest neighbors algorithms are best suited to predicting V p and ρ b logs based on coefficients of determination (R 2 ) greater than 70% and mean absolute percentage errors less than 4%. Given the high accuracy and low error results for V p and ρ b prediction in both hydrate and water-saturated sediments, we argue that our model can be applied in most LWD wells to predict V p or ρ b logs in near-seafloor siliciclastic sediments on continental slopes irrespective of the presence or absence of gas hydrate.