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

Compressional velocity (Vp) and bulk density (rho(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 Vp or rho(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, rho(b), Vp, and resistivity to train the machine learning model to predict Vp or rho(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 Vp and rho(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 Vp and rho(b) prediction in both hydrate and water-saturated sediments, we argue that our model can be applied in most LWD wells to predict Vp or rho(b) logs in near-seafloor siliciclastic sediments on continental slopes irrespective of the presence or absence of gas hydrate.