Abstract
Decision making to optimize the drilling operation is based on a variety of factors, among them real-time interpretation of drilled lithology. Since logging while drilling (LWD) tools are placed some meters above the bit, mechanical drilling parameters are the earliest indicators, although difficult to interpret accurately. This paper presents a novel deep learning methodology using mechanical drilling parameters for lithology classification. A cascade of multilayered perceptrons (MLPs) are trained on historical data from wells on a field operated by Equinor. Rather than an end-to-end approach, the drilling parameters are utilized to estimate LWD sensor readings in an intermediate step using the first MLPs. This allows continuous updates of the models during operation using delayed LWD data. The second MLP takes the virtual LWD estimates as input to predict currently drilled lithology, similar to manual expert interpretation of logs. This configuration takes into account case dependent (mud, BHA, wellbore geometry) and time varying (bit-wear, wellbore friction) relationships between drilling parameters and LWD readings, while assuming a constant rule when utilizing LWD data to classify lithology. Upon completion of training and validation, the system is tested on a separate, unseen wellbore, for which results are presented. Visualizations for true lithology alongside the estimates are given, along with confusion matrices and model accuracy. The system achieves high accuracy on the test set and presents low confusion between classes, meaning that it distinguishes well between the lithologies present in the wellbore. It can be seen that the borders between successive layers of lithology are detected rapidly, which is crucial seen from an optimization standpoint, so the driller may adjust accordingly immediately. It shows promise as an advisory system, capable of accurately classifying currently drilled lithology by continuously adapting to changing downhole conditions. Although we cannot expect perfect estimates of lithology purely based on drilling parameters, we can obtain a preliminary map of the subsurface this way. This novel configuration gives a real-time interpretation of the currently drilled lithology, allowing the driller to take proper actions to optimize the drilling operation in terms of rate of penetration (ROP) and best practices for different lithologies.
Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time
