Formation permeability estimation using mud loss data by deep learning

Permeability estimation plays an essential role in the assessment of reservoirs and hydrocarbon extraction. There are various methods to evaluate the formation and estimate the formation permeability, but in some cases, the evaluation may not be done or it may not be done correctly. This study focuses on a novel method to estimate the formation's permeability with appropriate accuracy using the mud loss data. Machine learning applications are becoming more popular nowadays and can succeed in many fields. This current research focuses on the application of mud loss data and deep learning to estimate the formation's permeability. To implement and validate our methodology, it is considered pilot cases including reservoir and drilling parameters values (depth, formation type, formation thickness, mud density, mud viscosity, and formation permeability). It is assumed that mud loss was occurred because of deferential pressure between formation pressure and bottom-hole pressure. The mud loss rate data were generated at different sets of reservoir and drilling data values using a reservoir simulator and then evaluated by calculating the correlation coefficients to ensure their validity and to check the fit under real conditions. This can be used to estimate the formation permeability values. One-dimensional convolutional neural networks(1D-CNN), a type of convolutional neural network, is utilized to be trained with data to perform a regression problem based on the contribution of flattening, dropout, and fully connected layers to estimate permeability with high accuracy (training data R² = 0.970, testing data R² = 0.964). Then the new deep learning method, Deep jointly informed neural network (DJINN), with the cooperation of neural networks and decision trees, provides a more accurate model than 1D-CNN (training data R² = 0.978, testing data R² = 0.972). These descriptions may provide new applications for mud loss data, where data while drilling can be used to predict formation permeability and provide insights for petroleum engineers to accurately measure design.