  The objective is to study the feasibility of predicting subsurface rock
properties in wells from real-time drilling data. Geophysical logs, namely,
density, porosity and sonic logs are of paramount importance for subsurface
resource estimation and exploitation. These wireline petro-physical
measurements are selectively deployed as they are expensive to acquire;
meanwhile, drilling information is recorded in every drilled well. Hence a
predictive tool for wireline log prediction from drilling data can help
management make decisions about data acquisition, especially for delineation
and production wells. This problem is non-linear with strong ineractions
between drilling parameters; hence the potential for deep learning to address
this problem is explored. We present a workflow for data augmentation and
feature engineering using Distance-based Global Sensitivity Analysis. We
propose an Inception-based Convolutional Neural Network combined with a
Temporal Convolutional Network as the deep learning model. The model is
designed to learn both low and high frequency content of the data. 12 wells
from the Equinor dataset for the Volve field in the North Sea are used for
learning. The model predictions not only capture trends but are also physically
consistent across density, porosity, and sonic logs. On the test data, the mean
square error reaches a low value of 0.04 but the correlation coefficient
plateaus around 0.6. The model is able however to differentiate between
different types of rocks such as cemented sandstone, unconsolidated sands, and
shale.
